A thermodynamic model of the surface hydroxylation of γ-Al2O3.

Rational design of γ-alumina-based catalysts relies on an extensive understanding of the distribution of hydroxyl groups on the surface of γ-alumina and their physicochemical properties, which remain unclear and challenging to determine experimentally due to the structural complexity. In this work, by means of DFT and thermodynamic calculations, various hydroxylation modes of γ-alumina (110) and (100) surfaces at different OH coverages were evaluated, based on which a thermodynamic model to reflect the relationship between temperature and the surface structure was established and the stable hydroxylation modes under experimental conditions were predicted. This enables us to identify the experimentally measured IR spectra. The effect of hydroxyl coverages on the surface Lewis acidity was then analyzed, showing that the presence of hydroxyl groups could promote the Lewis acidity of neighboring Al sites. This work provides fundamental insights into the molecular level understanding of the surface properties of γ-alumina and benefits the rational design of alumina-based catalysts.

A transformer-based multi-task deep learning model for simultaneous T-stage identification and segmentation of nasopharyngeal carcinoma.

Background: Accurate tumor target contouring and T staging are vital for precision radiation therapy in nasopharyngeal carcinoma (NPC). Identifying T-stage and contouring the Gross tumor volume (GTV) manually is a laborious and highly time-consuming process. Previous deep learning-based studies have mainly been focused on tumor segmentation, and few studies have specifically addressed the tumor staging of NPC. Objectives: To bridge this gap, we aim to devise a model that can simultaneously identify T-stage and perform accurate segmentation of GTV in NPC. Materials and methods: We have developed a transformer-based multi-task deep learning model that can perform two tasks simultaneously: delineating the tumor contour and identifying T-stage. Our retrospective study involved contrast-enhanced T1-weighted images (CE-T1WI) of 320 NPC patients (T-stage: T1-T4) collected between 2017 and 2020 at our institution, which were randomly allocated into three cohorts for three-fold cross-validations, and conducted the external validation using an independent test set. We evaluated the predictive performance using the area under the receiver operating characteristic curve (ROC-AUC) and accuracy (ACC), with a 95% confidence interval (CI), and the contouring performance using the Dice similarity coefficient (DSC) and average surface distance (ASD). Results: Our multi-task model exhibited sound performance in GTV contouring (median DSC: 0.74; ASD: 0.97 mm) and T staging (AUC: 0.85, 95% CI: 0.82-0.87) across 320 patients. In early T category tumors, the model achieved a median DSC of 0.74 and ASD of 0.98 mm, while in advanced T category tumors, it reached a median DSC of 0.74 and ASD of 0.96 mm. The accuracy of automated T staging was 76% (126 of 166) for early stages (T1-T2) and 64% (99 of 154) for advanced stages (T3-T4). Moreover, experimental results show that our multi-task model outperformed the other single-task models. Conclusions: This study emphasized the potential of multi-task model for simultaneously delineating the tumor contour and identifying T-stage. The multi-task model harnesses the synergy between these interrelated learning tasks, leading to improvements in the performance of both tasks. The performance demonstrates the potential of our work for delineating the tumor contour and identifying T-stage and suggests that it can be a practical tool for supporting clinical precision radiation therapy.

DNA barcoding combined with high-resolution melting analysis to discriminate rhubarb species and its traditional Chinese patent medicines.

Introduction: Rhubarb is a frequently used and beneficial traditional Chinese medicine. Wild resources of these plants are constantly being depleted, meaning that rhubarb products have been subjected to an unparalleled level of adulteration. Consequentially, reliable technology is urgently required to verify the authenticity of rhubarb raw materials and commercial botanical drugs. Methods: In this study, the barcode-DNA high-resolution melting (Bar-HRM) method was applied to characterize 63 rhubarb samples (five Polygonaceae species: Rheum tanguticum, Rh. palmatum, Rh. officinale, Rumex japonicus and Ru. sp.) and distinguish the rhubarb contents of 24 traditional Chinese patent medicine (TCPM) samples. Three markers, namely ITS2, rbcL and psbA-trnH, were tested to assess the candidate DNA barcodes for their effectiveness in distinguishing rhubarb from its adulterants. A segment from ITS2 was selected as the most suitable mini-barcode to identify the botanical drug rhubarb in TCPMs. Then, rhubarbs and TCPM samples were subjected to HRM analysis based on the ITS2 barcode. Results: Among the tested barcoding loci, ITS2 displayed abundant sites of variation and was effective in identifying Polygonaceae species and their botanical origins. HRM analysis based on the ITS2 mini-barcode region successfully distinguished the authenticity of five Polygonaceae species and eight batches of TCPMs. Of the 18 TCPM samples, 66.7 % (12 samples) were identified as containing Rh. tanguticum or Rh. officinale. However, 33.3 % were shown to consist of adulterants. Conclusions: These results demonstrated that DNA barcoding combined with HRM is a specific, suitable and powerful approach for identifying rhubarb species and TCPMs, which is crucial to guaranteeing the security of medicinal plants being traded internationally.

Prediction of the short-term efficacy and recurrence of photodynamic therapy in the treatment of oral leukoplakia based on deep learning.

BACKGROUND: The treatment of oral leukoplakia (OLK) with aminolaevulinic acid photodynamic therapy (ALA-PDT) is widespread. Nonetheless, there is variation in efficacy. Therefore, this study constructed a model for predicting the short-term efficacy and recurrence of OLK after ALA-PDT. METHODS: The short-term efficacy and recurrence of ALA-PDT were calculated by statistical analysis, and the relevant influencing factors were analyzed by Logistic regression and COX regression model. Finally, prediction models for total response (TR) rate, complete response (CR) rate and recurrence in OLK patients after ALA-PDT treatment were established. Features from pathology sections were extracted using deep learning autoencoder and combined with clinical variables to improve prediction performance of the model. RESULTS: The logistic regression analysis showed that the non-homogeneous (OR: 4.911, P: 0.023) OLK and lesions with moderate to severe epithelial dysplasia (OR: 4.288, P: 0.042) had better short-term efficacy. The area under receiver operating characteristic curve (AUC) of CR, TR and recurrence predict models after the ALA-PDT treatment of OLK patients is 0.872, 0.718, and 0.564, respectively. Feature extraction revealed an association between inflammatory cell infiltration in the lamina propria and recurrence after PDT. Combining clinical variables and deep learning improved the performance of recurrence model by more than 30 %. CONCLUSIONS: ALA-PDT has excellent short-term efficacy in the management of OLK but the recurrence rate was high. Prediction model based on clinicopathological characteristics has excellent predictive effect for short-term efficacy but limited effect for recurrence. The use of deep learning and pathology images greatly improves predictive value of the models.

Joint association of diabetes mellitus and inflammation status with biological ageing acceleration and premature mortality.

BACKGROUND: We aimed to investigate the associations of diabetes mellitus (DM) and C-reactive protein (CRP) with biological ageing acceleration and mortality risk. METHODS: We analyzed data from 41,634 adults with CRP and DM at baseline. Subjects were categorized into high CRP (>3 mg/L) and low CRP (≤3 mg/L) groups. The cross-sectional endpoints of the study were biological ageing indicators Klemera-Doubal method BioAge acceleration (KDMAccel) and Phenotypic age acceleration (PhenoAgeAccel), and the follow-up endpoints were all-cause mortality and cardiovascular mortality. RESULTS: In adults with high CRP, compared with those without DM, PhenoAgeAccel increased by 1.66 years (95 % CI: 1.38-1.93), and 8.74 years (95 % CI: 8.25-9.22) in adults with prediabetes and DM, respectively (p for interaction <0.001). Using the CRPlow/non-DM group as a reference, adults in the CRPhigh/non-DM, CRPlow/DM, and CRPhigh/DM groups had significantly advanced biological ageing. Compared to adults without DM, low CRP, and no ageing acceleration, the multivariable-adjusted HRs (95%CIs) of all-cause and cardiovascular mortality in those with DM, CRP, and ageing acceleration were 3.22 (2.79-3.72), and 3.57 (2.81-4.54), respectively. CONCLUSIONS: These findings suggest that the joint presence of low-grade inflammation and DM might be associated with higher odds of biological ageing acceleration and premature mortality.

Causal association between particulate matter 2.5 and Alzheimer's disease: a Mendelian randomization study.

Background: Although epidemiological evidence implies a link between exposure to particulate matter (PM) and Alzheimer's disease (AD), establishing causality remains a complex endeavor. In the present study, we used Mendelian randomization (MR) as a robust analytical approach to explore the potential causal relationship between PM exposure and AD risk. We also explored the potential associations between PM exposure and other neurodegenerative diseases. Methods: Drawing on extensive genome-wide association studies related to PM exposure, we identified the instrumental variables linked to individual susceptibility to PM. Using summary statistics from five distinct neurodegenerative diseases, we conducted two-sample MR analyses to gauge the causal impact of PM on the risk of developing these diseases. Sensitivity analyses were undertaken to evaluate the robustness of our findings. Additionally, we executed multivariable MR (MVMR) to validate the significant causal associations identified in the two-sample MR analyses, by adjusting for potential confounding risk factors. Results: Our MR analysis identified a notable association between genetically predicted PM2.5 (PM with a diameter of 2.5 µm or less) exposure and an elevated risk of AD (odds ratio, 2.160; 95% confidence interval, 1.481 to 3.149; p < 0.001). A sensitivity analysis supported the robustness of the observed association, thus alleviating concerns related to pleiotropy. No discernible causal relationship was identified between PM and any other neurodegenerative diseases. MVMR analyses-adjusting for smoking, alcohol use, education, stroke, hearing loss, depression, and hypertension-confirmed a persistent causal relationship between PM2.5 and AD. Sensitivity analyses, including MR-Egger and weighted median analyses, also supported this causal association. Conclusion: The present MR study provides evidence to support a plausible causal connection between PM2.5 exposure and AD. The results emphasize the importance of contemplating air quality interventions as a public health strategy for reducing AD risk.

Association of adverse childhood experiences with poor health condition among middle-aged and elderly adults in the United States: A nationally retrospective cohort study.

BACKGROUND: The specific effects of adverse childhood experiences (ACEs) in adulthood and senectitude were less known. We aim to examine the relationship between early ACEs and overall health condition as well as specific dimensions in the middle-aged and elderly population. METHODS: In the 2019-2021 Behavioral Risk Factor Surveillance System Study, robust Poisson regression models were used to estimate the relationship between ACE exposure and current health status among adults aged 45 ≥ years. RESULTS: Of the 195,472 participants, 53.8 % were female and the mean age was 65.0 years. Compared to populations without ACE, ACE exposures were more significantly associated with depression (PR: 2.03, 95 %CI: 1.94-2.21), frequent mental health (PR: 1.85, 95 %CI: 1.74-1.97) and subject cognitive decline (PR: 1.99, 95 %CI:1.85-2.14) than with physical health (PR: 1.37, 95 %CI: 1.32-1.44), with dose-response patterns. The association with mental disorder was especially significant among the elderly population. CONCLUSION: Early ACEs are associated with adverse health outcomes that persist into later life, particularly mental disorders and cognitive decline. Poor mental health may indirectly influence associations with ACEs and cognitive decline as well as physical health. Our findings emphasize the importance of lifelong psychological screening and support for the ACE-exposed middle-aged and elderly population.

Generation of a human induced pluripotent stem cell line JHUi004-A with heterozygous mutation for spinocerebellar ataxia type 12 using genome editing.

Spinocerebellar ataxia type 12 (SCA12) is caused by a CAG expansion mutation in PPP2R2B, a gene encoding brain-specific regulatory units of protein phosphatase 2A (PP2A); while normal alleles carry 4 to 31 triplets, the disease alleles carry 43 to 78 triplets. Here, by CRISPR/Cas9n genome editing, we have generated a human heterozygous SCA12 iPSC line with 73 triplets for the mutant allele. The heterozygous SCA12 iPSCs have normal karyotype, express pluripotency markers and are able to differentiate into the three germ layers.

Artificial Intelligence-Assisted Prescription Determination for Orthokeratology Lens Fitting: From Algorithm to Clinical Practice.

OBJECTIVES: To explore the potential of artificial intelligence (AI) to assist prescription determination for orthokeratology (OK) lenses. METHODS: Artificial intelligence algorithm development followed by a real-world trial. A total of 11,502 OK lenses fitting records collected from seven clinical environments covering major brands. Records were randomly divided in a three-way data split. Cross-validation was used to identify the most accurate algorithm, followed by an evaluation using an independent test data set. An online AI-assisted system was implemented and assessed in a real-world trial involving four junior and three senior clinicians. RESULTS: The primary outcome measure was the algorithm's accuracy (ACC). The ACC of the best performance of algorithms to predict the targeted reduction amplitude, lens diameter, and alignment curve of the prescription was 0.80, 0.82, and 0.83, respectively. With the assistance of the AI system, the number of trials required to determine the final prescription significantly decreased for six of the seven participating clinicians (all P <0.01). This reduction was more significant among junior clinicians compared with consultants (0.76±0.60 vs. 0.32±0.60, P <0.001). Junior clinicians achieved clinical outcomes comparable to their seniors, as 93.96% (140/149) and 94.44% (119/126), respectively, of the eyes fitted achieved unaided visual acuity no worse than 0.8 ( P =0.864). CONCLUSIONS: AI can improve prescription efficiency and reduce discrepancies in clinical outcomes among clinicians with differing levels of experience. Embedment of AI in practice should ultimately help lessen the medical burden and improve service quality for myopia boom emerging worldwide.

Decreased cobalamin sensitivity and biological aging acceleration in the general population.

BACKGROUND: The evidence on the association between cobalamin (Cbl) and aging or relevant outcomes is limited and controversial. We aimed to investigate the relationships between cobalamin intake- and function-related biomarkers and biological aging. METHODS: The study encompassed 22,812 participants aged 20 years and older from the National Health and Nutrition Examination Survey. A panel of biomarkers or algorithms was used to assess biological aging, including Klemera-Doubal Age Acceleration (KDMAccel), Phenotypic age acceleration (PhenoAgeAccel), telomere length, α-Klotho, and PhenoAge advancement. Weighted generalized linear regression analysis was used to assess the associations between cobalamin-intake biomarkers (serum cobalamin, cobalamin intake from food, cobalamin supplement use, serum methylmalonic acid [MMA], and homocysteine [Hcy]) and function-related biomarkers (functional cobalamin deficiency and cobalamin insensitivity index). RESULTS: Among the 22,812 individuals, the weighted mean (SE) age was 48.3 (0.2) years and 48.0% were males. Unexpectedly, serum and dietary cobalamin as well as serum MMA and Hcy levels were positively associated with most indicators of biological aging. Cobalamin sensitivity was assessed by the combination of binary Cbllow/high and MMAlow/high or Hcylow/high (cutoff values: 400 pg/mL for cobalamin, 250 nmol/L for MMA, and 12.1 µmol/l for Hcy) and a newly constructed cobalamin insensitivity index (based on the multiplicative term of serum cobalamin and serum MMA or Hcy). The multivariable-adjusted ß (95%CIs) of KDMAccel in the MMAlowCbllow, MMAlowCblhigh, MMAhighCbllow, and MMAhighCblhigh groups were reference, 0.27 (0.03 to 0.51), 0.85 (0.41 to 1.29), and 7.97 years (5.77 to 10.17) respectively, which were consistent for the combination of serum Hcy and cobalamin. Both cobalamin insensitivity indices were robustly associated with biological aging acceleration in a dose-response pattern (each p < 0.001). CONCLUSIONS: Decreased cobalamin sensitivity but not cobalamin insufficiency might be associated with biological aging acceleration. Further studies would improve understanding of the underlying mechanisms between decreased cobalamin sensitivity and biological aging acceleration.

The association between serum methylmalonic acid, cobalamin-related biomarkers, and long-term mortality risk in cancer survivors: a prospective cohort study.

BACKGROUND: Elevated serum methylmalonic acid (MMA), a marker of cobalamin (vitamin B12) deficiency, has been linked to cancer progression. However, the impact of MMA or cobalamin on mortality risk in cancer survivors remains unknown. OBJECTIVES: To explore the relationship between MMA, serum, dietary, and supplement of cobalamin, MMA metabolism-related genes, and poor prognosis in adult cancer survivors. METHODS: We analyzed data from 1988 cancer survivors aged ≥20 y. Patients were selected from the National Health and Nutrition Examination Survey and followed up until December 31, 2019. Weighted Cox proportional hazard regression was used to estimate hazard ratios (HRs) and the corresponding 95% confidence intervals (CIs) for mortality risk assessment. Genomic analysis identified MMA metabolism-related genes linked to early death in a 33-cancer-type cohort from The Cancer Genome Atlas. RESULTS: Among 1988 participants, 872 deaths occurred over a 10-year follow-up. Higher serum MMA levels were significantly linked to increased long-term mortality risk (tertile 3 compared with tertile 1: adjusted HR: 1.37; 95% CI: 1.11, 1.70; P-trend < 0.001). No associations were found between serum, dietary, and supplement of cobalamin and cancer survivor mortality (each P-trend > 0.143). However, MMA-associated mortality was notable in patients without deficiency. When combining cobalamin and MMA categories, multivariate-adjusted HR (95% CI) for all-cause mortality was 2.06 (95% CI: 1.60, 2.65) in participants with >250 nmol/L and cobalamin >295.1 pmol/L compared with those with MMA ≤250 nmol/L and cobalamin >295.1 pmol/L. Moreover, reduced transcriptional levels of MMA metabolism-related genes, indicating decreased mitochondrial MMA metabolism capability, are linked to an unfavorable prognosis in certain cancer types. CONCLUSIONS: Serum MMA was associated with long-term mortality risk in adult cancer survivors, which was more significant among individuals with higher levels of serum cobalamin. These findings suggest that mortality related to MMA was attributed to the insufficient flux of MMA metabolism, not cobalamin deficiency.

Error detection for radiotherapy planning validation based on deep learning networks.

BACKGROUND: Quality assurance (QA) of patient-specific treatment plans for intensity-modulated radiation therapy (IMRT) and volumetric modulated arc therapy (VMAT) necessitates prior validation. However, the standard methodology exhibits deficiencies and lacks sensitivity in the analysis of positional dose distribution data, leading to difficulties in accurately identifying reasons for plan verification failure. This issue complicates and impedes the efficiency of QA tasks. PURPOSE: The primary aim of this research is to utilize deep learning algorithms for the extraction of 3D dose distribution maps and the creation of a predictive model for error classification across multiple machine models, treatment methodologies, and tumor locations. METHOD: We devised five categories of validation plans (normal, gantry error, collimator error, couch error, and dose error), conforming to tolerance limits of different accuracy levels and employing 3D dose distribution data from a sample of 94 tumor patients. A CNN model was then constructed to predict the diverse error types, with predictions compared against the gamma pass rate (GPR) standard employing distinct thresholds (3%, 3 mm; 3%, 2 mm; 2%, 2 mm) to evaluate the model's performance. Furthermore, we appraised the model's robustness by assessing its functionality across diverse accelerators. RESULTS: The accuracy, precision, recall, and F1 scores of CNN model performance were 0.907, 0.925, 0.907, and 0.908, respectively. Meanwhile, the performance on another device is 0.900, 0.918, 0.900, and 0.898. In addition, compared to the GPR method, the CNN model achieved better results in predicting different types of errors. CONCLUSION: When juxtaposed with the GPR methodology, the CNN model exhibits superior predictive capability for classification in the validation of the radiation therapy plan on different devices. By using this model, the plan validation failures can be detected more rapidly and efficiently, minimizing the time required for QA tasks and serving as a valuable adjunct to overcome the constraints of the GPR method.

Cross-Domain Mutual-Assistance Learning Framework for Fully Automated Diagnosis of Primary Tumor in Nasopharyngeal Carcinoma.

Accurate T-staging of nasopharyngeal carcinoma (NPC) holds paramount importance in guiding treatment decisions and prognosticating outcomes for distinct risk groups. Regrettably, the landscape of deep learning-based techniques for T-staging in NPC remains sparse, and existing methodologies often exhibit suboptimal performance due to their neglect of crucial domain-specific knowledge pertinent to primary tumor diagnosis. To address these issues, we propose a new cross-domain mutual-assistance learning framework for fully automated diagnosis of primary tumor using H&N MR images. Specifically, we tackle primary tumor diagnosis task with the convolutional neural network consisting of a 3D cross-domain knowledge perception network (CKP net) for excavated cross-domain-invariant features emphasizing tumor intensity variations and internal tumor heterogeneity, and a multi-domain mutual-information sharing fusion network (M2SF net), comprising a dual-pathway domain-specific representation module and a mutual information fusion module, for intelligently gauging and amalgamating multi-domain, multi-scale T-stage diagnosis-oriented features. The proposed 3D cross-domain mutual-assistance learning framework not only embraces task-specific multi-domain diagnostic knowledge but also automates the entire process of primary tumor diagnosis. We evaluate our model on an internal and an external MR images dataset in a three-fold cross-validation paradigm. Exhaustive experimental results demonstrate that our method outperforms the state-of-the-art algorithms, and obtains promising performance for tumor segmentation and T-staging. These findings underscore its potential for clinical application, offering valuable assistance to clinicians in treatment decision-making and prognostication for various risk groups.

Role of Bß1 overexpression in the pathogenesis of SCA12.

BACKGROUND: Spinocerebellar ataxia type 12 (SCA12) is a neurodegenerative disease caused by a CAG/CTG repeat expansion at the PPP2R2B locus. OBJECTIVE: We investigated how the CAG repeat expansion within the PPP2R2B 7B7D transcript influences the expression of Bß1 and a potential protein containing a long polyserine tract. METHODS: Transcript and protein expression were measured using quantitative PCR (qPCR) Role of Bß1 overexpression in the pathogenesis of SCA12 and Western blot, respectively, in an SK-N-MC cell model that overexpresses the full-length PPP2R2B 7B7D transcript. The apoptotic effect of a protein containing a long polyserine tract on SK-N-MC cells was evaluated using caspase 3/7 activity. RESULTS: The CAG repeat expansion increases the expression of the PPP2R2B 7B7D transcript, as well as Bß1 protein, in an SK-N-MC cell model in which the full-length PPP2R2B 7B7D transcript is overexpressed. The CAG repeat expansion within the 7B7D transcript is translated into a long polyserine tract that triggers apoptosis in SK-N-MC cells. CONCLUSIONS: The SCA12 mutation leads to overexpression of PPP2R2B Bß1 and to expression of a protein containing a long polyserine tract; both these effects potentially contribute to SCA12 pathogenesis. © 2024 International Parkinson and Movement Disorder Society.

Population genomics analysis reveals footprints of selective breeding in a rapid-growth variety of Paulownia fortunei with apical dominance.

Paulownia fortunei is an ecologically and economically valuable tree cultivated for its rapid growth and high-quality timber. To enhance Paulownia germplasm, we have developed the elite variety QingT with patented advantages in growth rate and apical dominance. To illuminate the genetic basis of QingT's superior traits, here we harness comparative population genomics to analyze genomic variation patterns between QingT and common Paulownia. We performed whole-genome re-sequencing of 30 QingT and 30 common samples, detecting 15.6 million SNPs and 2.6 million indels. Phylogeny and population structure analyses robustly partitioned common and QingT into distinct groups which indicate robust genome stabilization. QingT exhibited reduced heterozygosity and linkage disequilibrium decay compared to common Paulownia, reflecting high recombination, indicating hybridizing effects with common white-flowered string is the source of its patented advantages. Genome selection scans uncovered 25 regions of 169 genes with elevated nucleotide diversity, indicating selection sweeps among groups. Functional analysis of sweep genes revealed upregulation of ribosomal, biosynthesis, and growth pathways in QingT, implicating enhanced protein production and developmental processes in its rapid growth phenotype. This study's insights comprehensively chart genomic variation during Paulownia breeding, localizing candidate loci governing agronomic traits, and underpinnings of future molecular breeding efforts to boost productivity.

Harnessing sub-comb dynamics in a graphene-sensitized microresonator for gas detection.

Since their inception, frequency combs generated in microresonators, known as microcombs, have sparked significant scientific interests. Among the various applications leveraging microcombs, soliton microcombs are often preferred due to their inherent mode-locking capability. However, this choice introduces additional system complexity because an initialization process is required. Meanwhile, despite the theoretical understanding of the dynamics of other comb states, their practical potential, particularly in applications like sensing where simplicity is valued, remains largely untapped. Here, we demonstrate controllable generation of sub-combs that bypasses the need for accessing bistable regime. And in a graphene-sensitized microresonator, the sub-comb heterodynes produce stable, accurate microwave signals for high-precision gas detection. By exploring the formation dynamics of sub-combs, we achieved 2 MHz harmonic comb-to-comb beat notes with a signal-to-noise ratio (SNR) greater than 50 dB and phase noise as low as - 82 dBc/Hz at 1 MHz offset. The graphene sensitization on the intracavity probes results in exceptional frequency responsiveness to the adsorption of gas molecules on the graphene of microcavity surface, enabling detect limits down to the parts per billion (ppb) level. This synergy between graphene and sub-comb formation dynamics in a microcavity structure showcases the feasibility of utilizing microcombs in an incoherent state prior to soliton locking. It may mark a significant step toward the development of easy-to-operate, systemically simple, compact, and high-performance photonic sensors.

Paeonol interferes with lupus nephritis by regulating M1/M2 polarization of macrophages.

Systemic lupus erythematosus (SLE) is a complex autoimmune disease of unknown etiology. It is marked by the production of pathogenic autoantibodies and the deposition of immune complexes. Lupus nephritis (LN) is a prevalent and challenging clinical complications of SLE. Cortex Moutan contains paeonol as its main effective component. In this study, using the animal model of SLE induced by R848, it was found that paeonol could alleviate the lupus-like symptoms of lupus mouse model induced by R848 activating TLR7, reduce the mortality and ameliorate the renal damage of mice. In order to explore the mechanism of paeonol on lupus nephritis, we studied the effect of paeonol on the polarization of Raw264.7 macrophages in vitro. The experimental results show that paeonol can inhibit the polarization of macrophages to M1 and promote their polarization to M2, which may be related to the inhibition of MAPK and NF-κB signaling pathways. Our research provides a new insight into paeonol in the treatment of lupus nephritis, which is of great importance for the treatment of systemic lupus erythematosus and its complications.

Unraveling the microbial puzzle: exploring the intricate role of gut microbiota in endometriosis pathogenesis.

Endometriosis (EMs) is a prevalent gynecological disorder characterized by the growth of uterine tissue outside the uterine cavity, causing debilitating symptoms and infertility. Despite its prevalence, the exact mechanisms behind EMs development remain incompletely understood. This article presents a comprehensive overview of the relationship between gut microbiota imbalance and EMs pathogenesis. Recent research indicates that gut microbiota plays a pivotal role in various aspects of EMs, including immune regulation, generation of inflammatory factors, angiopoietin release, hormonal regulation, and endotoxin production. Dysbiosis of gut microbiota can disrupt immune responses, leading to inflammation and impaired immune clearance of endometrial fragments, resulting in the development of endometriotic lesions. The dysregulated microbiota can contribute to the release of lipopolysaccharide (LPS), triggering chronic inflammation and promoting ectopic endometrial adhesion, invasion, and angiogenesis. Furthermore, gut microbiota involvement in estrogen metabolism affects estrogen levels, which are directly related to EMs development. The review also highlights the potential of gut microbiota as a diagnostic tool and therapeutic target for EMs. Interventions such as fecal microbiota transplantation (FMT) and the use of gut microbiota preparations have demonstrated promising effects in reducing EMs symptoms. Despite the progress made, further research is needed to unravel the intricate interactions between gut microbiota and EMs, paving the way for more effective prevention and treatment strategies for this challenging condition.

Ultralow Electric Current-Assisted Magnetization Switching due to Thermally Engineered Magnetic Anisotropy.

Control of magnetic anisotropy in thin films with perpendicular magnetic anisotropy is of paramount importance for the development of spintronics with ultralow-energy consumption and high density. Traditional magnetoelectric heterostructures utilized the synergistic effect of piezoelectricity and magnetostriction to realize the electric field control of magnetic anisotropy, resulting in additional fabrication and modulation processes and a complicated device architecture. Here, we have systematically investigated the electric current tuning of the magnetic properties of the metallic NiCo2O4 film with intrinsic perpendicular magnetic anisotropy. Ferrimagnetic-to-paramagnetic phase transition has been induced through Joule heating, resulting in a rapid decrease of both magnetic coercivity and moment. An ultralow current density of 2.5 × 104 A/cm2, which is 2 to 3 orders magnitude lower than that of conventional spin transfer torque devices, has been verified to be effective for the control of the magnetic anisotropy of NiCo2O4. Successful triggering of magnetic switching has been realized through the application of a current pulse. These findings provide new perspectives toward the electric control of magnetic anisotropy and design of spintronics with an ultralow driving current density.

Deep learning algorithms for classification and detection of recurrent aphthous ulcerations using oral clinical photographic images.

Background/purpose: The application of artificial intelligence diagnosis based on deep learning in the medical field has been widely accepted. We aimed to evaluate convolutional neural networks (CNNs) for automated classification and detection of recurrent aphthous ulcerations (RAU), normal oral mucosa, and other common oral mucosal diseases in clinical oral photographs. Materials and methods: The study included 785 clinical oral photographs, which was divided into 251 images of RAU, 271 images of the normal oral mucosa, and 263 images of other common oral mucosal diseases. Four and three CNN models were used for the classification and detection tasks, respectively. 628 images were randomly selected as training data. In addition, 78 and 79 images were assigned as validating and testing data. Main outcome measures included precision, recall, F1, specificity, sensitivity and area under the receiver operating characteristics curve (AUC). Results: In the classification task, the Pretrained ResNet50 model had the best performance with a precision of 92.86%, a recall of 91.84%, an F1 score of 92.24%, a specificity of 96.41%, a sensitivity of 91.84% and an AUC of 98.95%. In the detection task, the Pretrained YOLOV5 model had the best performance with a precision of 98.70%, a recall of 79.51%, an F1 score of 88.07% and an AUC of Precision-Recall curve 90.89%. Conclusion: The Pretrained ResNet50 and the Pretrained YOLOV5 algorithms were shown to have superior performance and acceptable potential in the classification and detection of RAU lesions based on non-invasive oral images, which may prove useful in clinical practice.