Standardization and quantification of backscattered electron imaging in scanning electron microscopy.

Backscattered electron (BSE) imaging based on scanning electron microscopy (SEM) has been widely used in scientific and industrial disciplines. However, achieving consistent standards and precise quantification in BSE images has proven to be a long-standing challenge. Previous methods incorporating dedicated calibration processes and Monte Carlo simulations have still posed practical limitations for widespread adoption. Here we introduce a bolometer platform that directly measures the absorbed thermal energy of the sample and demonstrates that it can help to analyze the atomic number (Z) of the investigated samples. The technique, named Atomic Number Electron Microscopy (ZEM), employs the conservation of energy as the foundation of standardization and can serve as a nearly ideal BSE detector. Our approach combines the strengths of both BSE and ZEM detectors, simplifying quantitative analysis for samples of various shapes and sizes. The complementary relation between the ZEM and BSE signals also makes the detection of light elements or compounds more accessible than existing microanalysis techniques.

Diffusion magnetic resonance imaging of cerebrospinal fluid dynamics: Current techniques and future advancements.

Cerebrospinal fluid (CSF) plays a critical role in metabolic waste clearance from the brain, requiring its circulation throughout various brain pathways, including the ventricular system, subarachnoid spaces, para-arterial spaces, interstitial spaces, and para-venous spaces. The complexity of CSF circulation has posed a challenge in obtaining noninvasive measurements of CSF dynamics. The assessment of CSF dynamics throughout its various circulatory pathways is possible using diffusion magnetic resonance imaging (MRI) with optimized sensitivity to incoherent water movement across the brain. This review presents an overview of both established and emerging diffusion MRI techniques designed to measure CSF dynamics and their potential clinical applications. The discussion offers insights into the optimization of diffusion MRI acquisition parameters to enhance the sensitivity and specificity of diffusion metrics on underlying CSF dynamics. Lastly, we emphasize the importance of cautious interpretations of diffusion-based imaging, especially when differentiating between tissue- and fluid-related changes or elucidating structural versus functional alterations.

Increased Risk of New-Onset Rheumatoid Arthritis Among Osteoarthritis Patients Received Total Knee Arthroplasty: a global federated health network analysis.

Background: Complications of total knee arthroplasty (TKA) had been widely discussed. However, whether TKA influence risk of rheumatoid arthritis (RA) in osteoarthritis patients remained uncertain. We intend to evaluate the risk of RA in osteoarthritis patients underwent TKA. Methods: In this retrospective cohort study, data was retrieved from the US collaborative networks in TriNetX research network. Within the study period between 2005 and 2017, osteoarthritis patients underwent TKA were enrolled as case cohort whereas osteoarthritis patients never underwent TKA were enrolled as control cohort. Covariates were matched via propensity score matching. Risk of RA in TKA patients were valuated under various follow-up time and sensitivity models. Results: Under 1-year, 3-year and 5-year of follow-up, TKA patients were associated with significantly elevated risk of RA, especially under 1-year follow-up (HR=1.74; 95% CI, 1.39-2.18). Subgroup analysis demonstrated a significant increase in the risk of RA following TKA in the female subgroup (HR=1.42; 95% CI, 1.24-1.63), the subgroup aged 18-64 years (HR=1.48; 95% CI, 1.11-1.97), and the subgroup aged greater than 65 years old (HR=1.38; 95% CI, 1.21-1.58) based on 5-year follow-up. Conclusion: Clinicians should be concerned about uncharted association between TKA and RA reported our current study. Additional prospective studies and in-depth mechanistic inquiries were warranted to determine the causation.

Eco-climate drivers shape virome diversity in a globally invasive tick species.

Spillovers of viruses into human occur more frequently under warmer conditions, particularly arboviruses. The invasive tick species Haemaphysalis longicornis poses a significant public health threat due to its global expansion and its potential to carry a wide range of pathogens. We analyzed meta-transcriptomic data from 3595 adult H. longicornis ticks collected between 2016 and 2019 in 22 provinces across China, encompassing diverse ecological conditions. Generalized additive modelling revealed that climate factors exerted a stronger influence on the virome of H. longicornis compared to other ecological factors, such as ecotypes, distance to coastline, animal host, tick gender, and anti-viral immunity. We investigated the mechanistic understanding of how climate changes drive the tick virome using causality inference and emphasized its significance for public health. Our findings demonstrated that higher temperatures and lower relative humidity/precipitation contribute to variations in animal host diversity, leading to an increased diversity of tick virome, particularly the evenness of vertebrate associated viruses. This finding may explain the evolution of tick-borne viruses into generalists across multiple hosts, thereby increasing the probability of spillover events involving tick-borne pathogens. Deep learning projections indicate that the diversity of H. longicornis virome is expected to increase in 81.9% of regions under the SSP8.5 scenario from 2019-2030. Extension of surveillance should be implemented to avert the spread of tick-borne diseases.

Unitig-centered pan-genome machine learning approach for predicting antibiotic resistance and discovering novel resistance genes in bacterial strains.

In current genomic research, the widely used methods for predicting antimicrobial resistance (AMR) often rely on prior knowledge of known AMR genes or reference genomes. However, these methods have limitations, potentially resulting in imprecise predictions owing to incomplete coverage of AMR mechanisms and genetic variations. To overcome these limitations, we propose a pan-genome-based machine learning approach to advance our understanding of AMR gene repertoires and uncover possible feature sets for precise AMR classification. By building compacted de Brujin graphs (cDBGs) from thousands of genomes and collecting the presence/absence patterns of unique sequences (unitigs) for Pseudomonas aeruginosa, we determined that using machine learning models on unitig-centered pan-genomes showed significant promise for accurately predicting the antibiotic resistance or susceptibility of microbial strains. Applying a feature-selection-based machine learning algorithm led to satisfactory predictive performance for the training dataset (with an area under the receiver operating characteristic curve (AUC) of > 0.929) and an independent validation dataset (AUC, approximately 0.77). Furthermore, the selected unitigs revealed previously unidentified resistance genes, allowing for the expansion of the resistance gene repertoire to those that have not previously been described in the literature on antibiotic resistance. These results demonstrate that our proposed unitig-based pan-genome feature set was effective in constructing machine learning predictors that could accurately identify AMR pathogens. Gene sets extracted using this approach may offer valuable insights into expanding known AMR genes and forming new hypotheses to uncover the underlying mechanisms of bacterial AMR.

Revealing the potential of solute carrier family 31 (copper transporters), member 1: Insights into its role in bladder cancer progression and therapeutic implications.

Introduction: Bladder cancer represents a significant public health concern with diverse genetic alterations influencing disease onset, progression, and therapy response. In this study, we explore the multifaceted role of Solute Carrier Family 31 Member 1 (SLC31A1) in bladder cancer, a pivotal gene involved in copper homeostasis. Methods: Our research involved analyzing the SLC31A1 gene expression via RT-qPCR, promoter methylation via targeted bisulfite sequencing, and mutational status via Next Generation Sequencing (NGS) using the clinical samples sourced by the local bladder cancer patients. Later on, The Cancer Genome Atlas (TCGA) datasets were utilized for validation purposes. Moreover, prognostic significance, gene enrichment terms, and therapeutic drugs of SLC31A1 were also explored using KM Plotter, DAVID, and DrugBank databases. Results: We observed that SLC31A1 was significantly up-regulated at both the mRNA and protein levels in bladder cancer tissue samples, suggesting its potential involvement in bladder cancer development and progression. Furthermore, our investigation into the methylation status revealed that SLC31A1 was significantly hypomethylated in bladder cancer tissues, which may contribute to its overexpression. The ROC analysis of the SLC31A1 gene indicated promising diagnostic potential, emphasizing its relevance in distinguishing bladder cancer patients from normal individuals. However, it is crucial to consider other factors such as cancer stage, metastasis, and recurrence for a more accurate evaluation in the clinical context. Interestingly, mutational analysis of SLC31A1 demonstrated only benign mutations, indicating their unknown role in the SLC31A1 disruption. In addition to its diagnostic value, high SLC31A1 expression was associated with poorer overall survival (OS) in bladder cancer patients, shedding light on its prognostic relevance. Gene enrichment analysis indicated that SLC31A1 could influence metabolic and copper-related processes, further underscoring its role in bladder cancer. Lastly, we explored the DrugBank database to identify potential therapeutic agents capable of reducing SLC31A1 expression. Our findings unveiled six important drugs with the potential to target SLC31A1 as a treatment strategy. Conclusion: Our comprehensive investigation highlights SLC31A1 as a promising biomarker for bladder cancer development, progression, and therapy.

Community Assembly Processes of Deadwood Mycobiome in a Tropical Forest Revealed by Long-Read Third-Generation Sequencing.

Despite the importance of wood-inhabiting fungi on nutrient cycling and ecosystem functions, their ecology, especially related to their community assembly, is still highly unexplored. In this study, we analyzed the wood-inhabiting fungal richness, community composition, and phylogenetics using PacBio sequencing. Opposite to what has been expected that deterministic processes especially environmental filtering through wood-physicochemical properties controls the community assembly of wood-inhabiting fungal communities, here we showed that both deterministic and stochastic processes can highly contribute to the community assembly processes of wood-inhabiting fungi in this tropical forest. We demonstrated that the dynamics of stochastic and deterministic processes varied with wood decomposition stages. The initial stage was mainly governed by a deterministic process (homogenous selection), whereas the early and later decomposition stages were governed by the stochastic processes (ecological drift). Deterministic processes were highly contributed by wood physicochemical properties (especially macronutrients and hemicellulose) rather than soil physicochemical factors. We elucidated that fine-scale fungal-fungal interactions, especially the network topology, modularity, and keystone taxa of wood-inhabiting fungal communities, strongly differed in an initial and decomposing deadwood. This current study contributes to a better understanding of the ecological processes of wood-inhabiting fungi in tropical regions where the knowledge of wood-inhabiting fungi is highly limited.

The longitudinal associations between ambient air pollution exposure and dementia in the UK: results from the cognitive function and ageing study II and Wales.

BACKGROUND: Air pollution has been recognised as a potential risk factor for dementia. Yet recent epidemiological research shows mixed evidence. The aim of this study is to investigate the longitudinal associations between ambient air pollution exposure and dementia in older people across five urban and rural areas in the UK. METHODS: This study was based on two population-based cohort studies of 11329 people aged ≥ 65 in the Cognitive Function and Ageing Study II (2008-2011) and Wales (2011-2013). An algorithmic diagnosis method was used to identify dementia cases. Annual concentrations of four air pollutants (NO2, O3, PM10, PM2.5) were modelled for the year 2012 and linked via the participants' postcodes. Multistate modelling was used to examine the effects of exposure to air pollutants on incident dementia incorporating death and adjusting for sociodemographic factors and area deprivation. A random-effect meta-analysis was carried out to summarise results from the current and nine existing cohort studies. RESULTS: Higher exposure levels of NO2 (HR: 1.04; 95% CI: 0.94, 1.14), O3 (HR: 0.90; 95% CI: 0.70, 1.15), PM10 (HR: 1.17; 95% CI: 0.86, 1.58), PM2.5 (HR: 1.41; 95% CI: 0.71, 2.79) were not strongly associated with dementia in the two UK-based cohorts. Inconsistent directions and strengths of the associations were observed across the two cohorts, five areas, and nine existing studies. CONCLUSIONS: In contrast to the literature, this study did not find clear associations between air pollution and dementia. Future research needs to investigate how methodological and contextual factors can affect evidence in this field and clarity the influence of air pollution exposure on cognitive health over the lifecourse.

Strictosamide promotes wound healing through activation of the PI3K/AKT pathway.

Nauclea officinalis, as a Chinese medicine in Hainan province, had the effect of treating lower limb ulcers, burn infections. In this paper, we studied the effect of Strictosamide (STR), the main bioactive compound in Nauclea officinals, on wound healing and explored its internal mechanism. Firstly, the wound healing potential of STR was evaluated in a rat model, demonstrating its ability to expedite wound healing, mitigate inflammatory infiltration, and enhance collagen deposition. Additionally, immunofluorescence analysis revealed that STR up-regulated the expression of CD31 and PCNA. Subsequently, target prediction, protein-protein interaction (PPI), gene ontology (GO), and pathway enrichment analyses were used to obtain potential targets, specific biological processes, and molecular mechanisms of STR for the potential treatment of wound healing. Furthermore, molecular docking was conducted to predict the binding affinity between STR and its associated targets. Additionally, in vivo and in vitro experiments confirmed that STR could increase the expression of P-PI3K, P-AKT and P-mTOR by activating the PI3K/AKT signaling pathway. In summary, this study provided a new explanation for the mechanism by which STR promotes wound healing through network pharmacology, suggesting that STR may be a new candidate for treating wound.

Nickel-catalysed highly regioselective synthesis of ß-acyl naphthalenes under reductive conditions.

Over the past decade, significant progress has been made in the direct C-H acylation of naphthalenes, occurring at the α or ß-positions to yield valuable ketones through Friedel-Crafts acylation or transition-metal-catalysed carbonylative coupling reactions. Nevertheless, highly regioselective acylation of naphthalenes remains a formidable challenge. Herein, we developed a nickel-catalysed reductive ring-opening reaction of 7-oxabenzonorbornadienes with acyl chlorides as the electrophilic coupling partner, providing a new method for the exclusive preparation of ß-acyl naphthalenes.

Establishment and Verification of an Artificial Intelligence Prediction Model for Children With Sepsis.

BACKGROUND: Early identification of high-risk groups of children with sepsis is beneficial to reduce sepsis mortality. This article used artificial intelligence (AI) technology to predict the risk of death effectively and quickly in children with sepsis in the pediatric intensive care unit (PICU). STUDY DESIGN: This retrospective observational study was conducted in the PICUs of the First Affiliated Hospital of Sun Yat-sen University from December 2016 to June 2019 and Shenzhen Children's Hospital from January 2019 to July 2020. The children were divided into a death group and a survival group. Different machine language (ML) models were used to predict the risk of death in children with sepsis. RESULTS: A total of 671 children with sepsis were enrolled. The accuracy (ACC) of the artificial neural network model was better than that of support vector machine, logical regression analysis, Bayesian, K nearest neighbor method and decision tree models, with a training set ACC of 0.99 and a test set ACC of 0.96. CONCLUSIONS: The AI model can be used to predict the risk of death due to sepsis in children in the PICU, and the artificial neural network model is better than other AI models in predicting mortality risk.

Proteomics of human platelet lysates and insight from animal studies on platelet protein diffusion to hippocampus upon intranasal administration.

Human platelet lysates (HPLs) from allogeneic platelet concentrates (PCs) are biomaterials, which are rich in various trophic factors, increasingly used in regenerative medicine and biotherapy. Understanding how preparation methods influence the HPL protein profile, biological function, and clinical outcomes is crucial. Our study sheds light on the proteomes and functionality of different HPLs, with the aim of advancing their scientifically grounded clinical applications. To achieve this, PCs suspended in plasma underwent three distinct processing methods, resulting in seven HPL types. We used three characterization techniques: label-free proteomics and tandem mass tag (TMT)-based quantitative proteomics, both before and after the immunodepletion of abundant plasma proteins. Bioinformatic tools assessed the proteome, and western blotting validated our quantitative proteomics data. Subsequent pre-clinical studies with fluorescent labeling and label-free proteomics were used as a proof of concept for brain diffusion. Our findings revealed 1441 proteins detected using the label-free method, 952 proteins from the TMT experiment before and after depletion, and 1114 proteins from the subsequent TMT experiment on depleted HPLs. Most detected proteins were cytoplasmic, playing key roles in catalysis, hemostasis, and immune responses. Notably, the processing methodologies significantly influenced HPL compositions, their canonical pathways, and, consequently, their functionality. Each HPL exhibited specific abundant proteins, providing valuable insight for tailored clinical applications. Immunoblotting results for selected proteins corroborated our quantitative proteomics data. The diffusion and differential effects to the hippocampus of a neuroprotective HPL administered intranasally to mice were demonstrated. This proteomics study advances our understanding of HPLs, suggesting ways to standardize and customize their production for better clinical efficacy in regenerative medicine and biotherapy. Proteomic analyses also offered objective evidence that HPPL, upon intranasal delivery, not only effectively diffuses to the hippocampus but also alters protein expression in mice, bolstering its potential as a treatment for memory impairments.

Blood miRNAs as Potential Diagnostic Biomarkers for Chronic Obstructive Pulmonary Disease: A Meta-Analysis.

Purpose: Investigate the efficacy of blood microRNAs (miRNAs) as diagnostic biomarkers for Chronic Obstructive Pulmonary Disease (COPD). Patients and Methods: We conducted a comprehensive search in English and Chinese databases, selecting studies based on predetermined criteria. Diagnostic parameters like summarized sensitivity (SSEN), summarized specificity (SSPE), summarized positive likelihood ratio (SPLR), summarized negative likelihood ratio (SNLR), and diagnostic odds ratio (DOR), and area under the curve (AUC) of the summary receiver operating characteristic (SROC) curves were analyzed using a bivariate model. Each parameter was accompanied by a 95% confidence interval (CI). Results: Eighteen high-quality studies were included. For diagnosing COPD with blood miRNAs, the SSEN was 0.83 (95% CI 0.76-0.89), SSPE 0.76 (95% CI 0.70-0.82), SPLR 3.50 (95% CI 2.66-4.60), SNLR 0.22 (95% CI 0.15-0.33), DOR 15.72 (95% CI 8.58-28.77), and AUC 0.86 (95% CI 0.82-0.88). In acute exacerbations, SSEN was 0.85 (95% CI 0.76-0.91), SSPE 0.80 (95% CI 0.73-0.86), SPLR 4.26 (95% CI 3.05-5.95), SNLR 0.19 (95% CI 0.12-0.30), DOR 22.29 (95% CI 11.47-43.33), and AUC 0.89 (95% CI 0.86-0.91). Conclusion: Blood miRNAs demonstrate significant accuracy in diagnosing COPD, both in general and during acute exacerbations, suggesting their potential as reliable biomarkers.

Oxygen therapy for exercise capacity in fibrotic interstitial lung disease: A systematic review and meta-analysis of randomised controlled trials.

BACKGROUND: Fibrotic interstitial lung disease (fILD) is characterised primarily by impaired lung function and quality of life. The present study investigated whether oxygen therapy could improve exercise capacity among patients with fILD. METHODS: Previously published randomised controlled trials (RCTs) were surveyed. A systematic review and meta-analysis was conducted to evaluate the effectiveness of oxygen therapy in improving the exertional capacity of patients with fILD. The primary outcome was peripheral oxygen saturation (SpO2) during exercise. The effects of oxygen therapy on fatigue, dyspnoea, heart rate, and exercise duration or distance were also analysed. RESULTS: Fourteen RCTs involving 370 patients were included. Oxygen therapy improved SpO2 during exercise (mean difference, MD = 6.26 %), exercise duration (MD = 122.15 s), fatigue (standard mean difference, SMD = -0.30), and dyspnoea (MD = -0.75 Borg score units). High-flow oxygen systems tended to be more effective than low-flow systems in improving exercising SpO2, duration, fatigue, dyspnoea, and heart rate. High-flow nasal cannulas (HFNCs) yielded better outcomes regarding SpO2 and fatigue than did high-flow Venturi masks (MD = 1.60 % and MD = -1.19 Borg score units, respectively). No major adverse events were reported. CONCLUSION: The evidence from RCTs supports the short-term use of oxygen supplementation to improve SpO2, exercise capacity, fatigue, and dyspnoea among patients with fILD. Further analyses demonstrates that HFNCs yield more favourable outcomes, yet not reaching statistical significance except for improving SpO2 and fatigue. However, the long-term effects of oxygen therapy on quality of life and mortality remain unclear.

Uptake of Polycyclic Aromatic Hydrocarbons (PAHs) from PAH-contaminated Soils to Carrots and Chinese Cabbages under the Greenhouse and Field Conditions.

Polycyclic aromatic hydrocarbons (PAHs) with the properties of structural stability, semi-volatility, and hydrophobicity are toxic and persistent in environments; thus, their transport and fate in agroecosystems is essential for reducing PAH accumulation in the edible parts of crops. Here, we cultivated cabbages (Brassica pekinensis L.) and carrots (Daucus carota L.) in PAH-contaminated soils under the greenhouse and field conditions. After harvesting, we observed a 9.5-46% reduction in soil ∑PAH concentrations. There were 37% of bioconcentration factors (BCFbs) > 1 and 93% of translocation factors (TFab) > 1, while low-molecular-weight (LMW) PAHs had higher BCFbs than high-molecular-weight (HMW) PAHs. The PAH concentrations showed significant and positive correlations among soils, the belowground parts, and the aboveground parts. The toxicity equivalent concentration (TEQBaP) followed the order of cabbage (greenhouse) > cabbage (field) > carrot (greenhouse) > carrot (field), suggesting potentially higher health risks in cabbage relative to carrot and vegetables under the greenhouse relative to field condition. Our study suggested growing carrots under field conditions as a management strategy for reducing the risks of vegetables grown in PAH-contaminated soils.

Effect of artificial natural light on the development of myopia among primary school-age children in China: a three-year longitudinal study.

AIM: To assess the efficacy of artificial natural light in preventing incident myopia in primary school-age children. METHODS: This is a prospective, randomized control, intervention study. A total of 1840 students from 39 classes in 4 primary schools in Foshan participated in this study. The whole randomization method was adopted to include classes as a group according to 1:1 randomized control. Classrooms in the control group were illuminated by usual light, and classrooms in the intervention group were illuminated by artificial natural light. All students received uncorrected visual acuity and best-corrected visual acuity measurement, non-cycloplegic autorefraction, ocular biometric examination, slit lamp and strabismus examination. Three-year follow-up, the students underwent same procedures. Myopia was defined as spherical equivalent refraction ≤ -0.50 D and uncorrected visual acuity <20/20. RESULTS: There were 894 students in the control group and 946 students in the intervention group with a mean±SD age of 7.50±0.53y. The three-year cumulative incidence rate of myopia was 26.4% (207 incident cases among 784 eligible participants at baseline) in the control group and 21.2% (164 incident cases among 774 eligible participants at baseline) in the intervention group [difference of 5.2% (95%CI, 3.7% to 10.1%); P=0.035]. There was also a significant difference in the three-year change in spherical equivalent refraction for the control group (-0.81 D) compared with the intervention group [-0.63 D; difference of 0.18 D (95%CI, 0.08 to 0.28 D); P<0.001]. Elongation of axial length was significantly different between in the control group (0.77 mm) and the intervention group [0.72 mm; difference of 0.05 mm (95%CI, 0.01 to 0.09 mm); P=0.003]. CONCLUSION: Artificial natural light in the classroom of primary schools can result in reducing incidence rate of myopia during a period of three years.

Deciphering the Prognostic Efficacy of MRI Radiomics in Nasopharyngeal Carcinoma: A Comprehensive Meta-Analysis.

This meta-analysis investigates the prognostic value of MRI-based radiomics in nasopharyngeal carcinoma treatment outcomes, specifically focusing on overall survival (OS) variability. The study protocol was registered with INPLASY (INPLASY202420101). Initially, a systematic review identified 15 relevant studies involving 6243 patients through a comprehensive search across PubMed, Embase, and Web of Science, adhering to PRISMA guidelines. The methodological quality was assessed using the Quality in Prognosis Studies (QUIPS) tool and the Radiomics Quality Score (RQS), highlighting a low risk of bias in most domains. Our analysis revealed a significant average concordance index (c-index) of 72% across studies, indicating the potential of radiomics in clinical prognostication. However, moderate heterogeneity was observed, particularly in OS predictions. Subgroup analyses and meta-regression identified validation methods and radiomics software as significant heterogeneity moderators. Notably, the number of features in the prognosis model correlated positively with its performance. These findings suggest radiomics' promising role in enhancing cancer treatment strategies, though the observed heterogeneity and potential biases call for cautious interpretation and standardization in future research.

Sinensetin protects against periodontitis through binding to Bach1 enhancing its ubiquitination degradation and improving oxidative stress.

Periodontitis is a chronic inflammatory and immune reactive disease induced by the subgingival biofilm. The therapeutic effect for susceptible patients is often unsatisfactory due to excessive inflammatory response and oxidative stress. Sinensetin (Sin) is a nature polymethoxylated flavonoid with anti-inflammatory and antioxidant activities. Our study aimed to explore the beneficial effect of Sin on periodontitis and the specific molecular mechanisms. We found that Sin attenuated oxidative stress and inflammatory levels of periodontal ligament cells (PDLCs) under inflammatory conditions. Administered Sin to rats with ligation-induced periodontitis models exhibited a protective effect against periodontitis in vivo. By molecular docking, we identified Bach1 as a strong binding target of Sin, and this binding was further verified by cellular thermal displacement assay and immunofluorescence assays. Chromatin immunoprecipitation-quantitative polymerase chain reaction results also revealed that Sin obstructed the binding of Bach1 to the HMOX1 promoter, subsequently upregulating the expression of the key antioxidant factor HO-1. Further functional experiments with Bach1 knocked down and overexpressed verified Bach1 as a key target for Sin to exert its antioxidant effects. Additionally, we demonstrated that Sin prompted the reduction of Bach1 by potentiating the ubiquitination degradation of Bach1, thereby inducing HO-1 expression and inhibiting oxidative stress. Overall, Sin could be a promising drug candidate for the treatment of periodontitis by targeting binding to Bach1.

Standalone deep learning versus experts for diagnosis lung cancer on chest computed tomography: a systematic review.

PURPOSE: To compare the diagnostic performance of standalone deep learning (DL) algorithms and human experts in lung cancer detection on chest computed tomography (CT) scans. MATERIALS AND METHODS: This study searched for studies on PubMed, Embase, and Web of Science from their inception until November 2023. We focused on adult lung cancer patients and compared the efficacy of DL algorithms and expert radiologists in disease diagnosis on CT scans. Quality assessment was performed using QUADAS-2, QUADAS-C, and CLAIM. Bivariate random-effects and subgroup analyses were performed for tasks (malignancy classification vs invasiveness classification), imaging modalities (CT vs low-dose CT [LDCT] vs high-resolution CT), study region, software used, and publication year. RESULTS: We included 20 studies on various aspects of lung cancer diagnosis on CT scans. Quantitatively, DL algorithms exhibited superior sensitivity (82%) and specificity (75%) compared to human experts (sensitivity 81%, specificity 69%). However, the difference in specificity was statistically significant, whereas the difference in sensitivity was not statistically significant. The DL algorithms' performance varied across different imaging modalities and tasks, demonstrating the need for tailored optimization of DL algorithms. Notably, DL algorithms matched experts in sensitivity on standard CT, surpassing them in specificity, but showed higher sensitivity with lower specificity on LDCT scans. CONCLUSION: DL algorithms demonstrated improved accuracy over human readers in malignancy and invasiveness classification on CT scans. However, their performance varies by imaging modality, underlining the importance of continued research to fully assess DL algorithms' diagnostic effectiveness in lung cancer. CLINICAL RELEVANCE STATEMENT: DL algorithms have the potential to refine lung cancer diagnosis on CT, matching human sensitivity and surpassing in specificity. These findings call for further DL optimization across imaging modalities, aiming to advance clinical diagnostics and patient outcomes. KEY POINTS: Lung cancer diagnosis by CT is challenging and can be improved with AI integration. DL shows higher accuracy in lung cancer detection on CT than human experts. Enhanced DL accuracy could lead to improved lung cancer diagnosis and outcomes.

SiX2 (X = S, Se) Nanowire Gate-All-Around MOSFETs for Sub-5 nm Applications.

The gate-all-around (GAA) field-effect transistor (FET) holds great potential to support next-generation integrated circuits. Nanowires such as carbon nanotubes (CNTs) are one important category of channel materials in GAA FETs. Based on first-principles investigations, we propose that SiX2 (X = S, Se) nanowires are promising channel materials that can significantly elevate the performance of GAA FETs. The sub-5 nm SiX2 (X = S, Se) nanowire GAA FETs exhibit excellent ballistic transport properties that meet the requirements of the 2013 International Technology Roadmap for Semiconductors (ITRS). Compared to CNTs, they are also advantageous or at least comparable in terms of gate controllability, device dimensions, etc. Importantly, SiSe2 GAA FETs show superb gate controllability due to the ultralow minimum subthreshold swing (SSmin) that breaks "Boltzmann's tyranny". Moreover, the energy-delay product (EDP) of SiX2 GAA FETs is significantly lower than that of the CNT FETs. These features make SiX2 nanowires ideal channel material in the sub-5 nm GAA FET devices.