Intraoperative hypotension is associated with decreased long-term survival in older patients after major noncardiac surgery: Secondary analysis of three randomized trials.

STUDY OBJECTIVE: To assess the association of intraoperative hypotension with long-term survivals in older patients after major noncardiac surgery mainly for cancer. DESIGN: A secondary analysis of databases from three randomized trials with long-term follow-up. SETTING: The underlying trials were conducted in 17 tertiary hospitals in China. PATIENTS: Patients aged 60 to 90 years who underwent major noncardiac thoracic or abdominal surgeries (≥ 2 h) in a single center were included in this analysis. EXPOSURES: Restricted cubic spline models were employed to determine the lowest mean arterial pressure (MAP) threshold that was potentially harmful for long-term survivals. Patients were arbitrarily divided into three groups according to the cumulative duration or area under the MAP threshold. The association between intraoperative hypotension exposure and long-term survivals were analyzed with the Cox proportional hazard regression models. MEASUREMENTS: Our primary endpoint was overall survival. Secondary endpoints included recurrence-free and event-free survivals. MAIN RESULTS: A total of 2664 patients (mean age 69.0 years, 34.9% female sex, 92.5% cancer surgery) were included in the final analysis. MAP < 60 mmHg was adopted as the threshold of intraoperative hypotension. Patients were divided into three groups according to duration under MAP < 60 mmHg (<1 min, 1-10 min, and > 10 min) or area under MAP <60 mmHg (< 1 mmHg⋅min, 1-30 mmHg⋅min, and > 30 mmHg⋅min). After adjusting confounders, duration under MAP < 60 mmHg for > 10 min was associated with a shortened overall survival when compared with the < 1 min patients (adjusted hazard ratio [HR] 1.31, 95% confidence interval [CI] 1.09 to 1.57, P = 0.004); area under MAP < 60 mmHg for > 30 mmHg⋅min was associated with a shortened overall survival when compared with the < 1 mmHg⋅min patients (adjusted HR 1.40, 95% CI 1.16 to 1.68, P < 0.001). Similar associations exist between duration under MAP < 60 mmHg for > 10 min or area under MAP < 60 mmHg for > 30 mmHg⋅min and recurrence-free or event-free survivals. CONCLUSIONS: In older patients who underwent major noncardiac surgery mainly for cancer, intraoperative hypotension was associated with worse overall, recurrence-free, and event-free survivals.

Nanofluidic Membrane-Assisted Organic Electrochemical Transistors for Bioinspired Gustatory Sensation Based on Selective Cation Transport.

Natural organisms have evolved precise sensing systems relying on unique ion channels, which can efficiently perceive various physical/chemical stimuli based on ionic signal transmission in biological fluid environments. However, it is still a huge challenge to achieve extensive applications of the artificial counterparts as an efficient wet sensing platform due to the fluidity of the working medium. Herein, nanofluidic membranes with selective cation transport properties and solid-state organic electrochemical transistors (OECTs) with amplified signals are integrated together to mimic human gustatory sensation, achieving ionic gustatory reagent recognition and a portable configuration. Cu-HHTP nanofluidic membranes with selective cation transport through their uniform micropores are constructed first, followed by assembly with OECTs to form the designed nanofluidic membrane-assisted OECTs (nanofluidic OECTs). As a result, they can distinguish typically ionic gustatory reagents, and even ionic liquids (ILs), demonstrating enhanced gustatory perception performance under a wide concentration range (10-7-10-1 m) compared with those of conventional OECTs. The linear correlations between the response and the reagent concentration further indicate the promising potential for practical application as a next-generation sensing platform. It is suggested that nanofluidic membranes mediated intramembrane cation transport based on the steric hindrance effect, resulting in distinguishable and improved response to multiple ions.

Asymmetric catalytic synthesis of chiral covalent organic framework composite (S)-DTP-COF@SiO2 for HPLC enantioseparations by normal-phase and reversed-phase chromatographic modes.

A novel CCOF core-shell composite material (S)-DTP-COF@SiO2 was prepared via asymmetric catalytic and in situ growth strategy. The prepared (S)-DTP-COF@SiO2 was utilized as separation medium for HPLC enantioseparation using normal-phase and reversed-phase chromatographic modes, which displays excellent chiral separation performance for alcohols, esters, ketones, and epoxides, etc. Compared with chiral commercial chromatographic columns (Chiralpak AD-H and Chiralcel OD-H columns) and some previously reported chiral CCOF@SiO2 (CC-MP CCTF@SiO2 and MDI-ß-CD-modified COF@SiO2)-packed columns, there are 4, 3, 13, and 15 tested racemic compounds that could not be resolved on the Chiralpak AD-H column, Chiralcel OD-H column, CC-MP CCTF@SiO2 column, and MDI-ß-CD-modified COF@SiO2 column, respectively, which indicates that the resolution effect of (S)-DTP-COF@SiO2-packed column can be complementary to the other ones. The effects of the analyte mass, column temperature, and mobile phase composition on the enantiomeric separation were investigated. The chiral column exhibits good reproducibility after multiple consecutive injections. The RSDs (n = 5) of the peak area and retention time were less than 1.5% for repetitive separation of 2-methoxy-2-phenylethanol and 1-phenyl-1-pentanol. The chiral core-shell composite (S)-DTP-COF@SiO2 exhibited good enantiomeric separation performance, which not only demonstrates its potential as a novel CSP material in HPLC but also expands the range of applications for chiral COFs.

Development and validation of AI models using LR and LightGBM for predicting distant metastasis in breast cancer: a dual-center study.

Objective: This study aims to develop an artificial intelligence model utilizing clinical blood markers, ultrasound data, and breast biopsy pathological information to predict the distant metastasis in breast cancer patients. Methods: Data from two medical centers were utilized, Clinical blood markers, ultrasound data, and breast biopsy pathological information were separately extracted and selected. Feature dimensionality reduction was performed using Spearman correlation and LASSO regression. Predictive models were constructed using LR and LightGBM machine learning algorithms and validated on internal and external validation sets. Feature correlation analysis was conducted for both models. Results: The LR model achieved AUC values of 0.892, 0.816, and 0.817 for the training, internal validation, and external validation cohorts, respectively. The LightGBM model achieved AUC values of 0.971, 0.861, and 0.890 for the same cohorts, respectively. Clinical decision curve analysis showed a superior net benefit of the LightGBM model over the LR model in predicting distant metastasis in breast cancer. Key features identified included creatine kinase isoenzyme (CK-MB) and alpha-hydroxybutyrate dehydrogenase. Conclusion: This study developed an artificial intelligence model using clinical blood markers, ultrasound data, and pathological information to identify distant metastasis in breast cancer patients. The LightGBM model demonstrated superior predictive accuracy and clinical applicability, suggesting it as a promising tool for early diagnosis of distant metastasis in breast cancer.

The integration of geographic methods and ecological momentary assessment in public health research: A systematic review of methods and applications.

With the widespread prevalence of mobile devices, ecological momentary assessment (EMA) can be combined with geospatial data acquired through geographic techniques like global positioning system (GPS) and geographic information system. This technique enables the consideration of individuals' health and behavior outcomes of momentary exposures in spatial contexts, mostly referred to as "geographic ecological momentary assessment" or "geographically explicit EMA" (GEMA). However, the definition, scope, methods, and applications of GEMA remain unclear and unconsolidated. To fill this research gap, we conducted a systematic review to synthesize the methodological insights, identify common research interests and applications, and furnish recommendations for future GEMA studies. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines to systematically search peer-reviewed studies from six electronic databases in 2022. Screening and eligibility were conducted following inclusion criteria. The risk of bias assessment was performed, and narrative synthesis was presented for all studies. From the initial search of 957 publications, we identified 47 articles included in the review. In public health, GEMA was utilized to measure various outcomes, such as psychological health, physical and physiological health, substance use, social behavior, and physical activity. GEMA serves multiple research purposes: 1) enabling location-based EMA sampling, 2) quantifying participants' mobility patterns, 3) deriving exposure variables, 4) describing spatial patterns of outcome variables, and 5) performing data linkage or triangulation. GEMA has advanced traditional EMA sampling strategies and enabled location-based sampling by detecting location changes and specified geofences. Furthermore, advances in mobile technology have prompted considerations of additional sensor-based data in GEMA. Our results highlight the efficacy and feasibility of GEMA in public health research. Finally, we discuss sampling strategy, data privacy and confidentiality, measurement validity, mobile applications and technologies, and GPS accuracy and missing data in the context of current and future public health research that uses GEMA.

Recognition of screening out hierarchical toxic contaminants tuned by quantified pseudo-components from complex engineering co-combustion.

Co-combustion of industrial and municipal solid wastes has emerged as the most promising disposal technology, yet its effect on unknown contaminants generation remains rarely revealed due to waste complexity. Hence, six batches of large-scale engineering experiments were designed in an incinerator of 650 t/d, which overcame the inauthenticity and deviation of laboratory tests. 953-1772 non-targeted compounds were screened in fly ash. Targeting the impact of co-combustion, a pseudo-component matrix model was innovatively integrated to quantitatively extract nine components from complex wastes grouped into biomass and plastic. Thus, the influence was evaluated across eight dimensions, covering molecular characteristics and toxicity. The effect of co-combustion with biomass pseudo-components was insignificant. However, co-combustion with high ratios of plastic pseudo-components induced higher potential risks, significantly promoting the formation of unsaturated hydrocarbons, highly unsaturated compounds (DBE≥15), and cyclic compounds by 19 %- 49 %, 17 %- 31 %, and 7 %- 27 %, respectively. Especially, blending with high ratios of PET plastic pseudo-components produced more species of contaminants. Unique 2 Level I toxicants, bromomethyl benzene and benzofuran-2-carbaldehyde, as well as 4 Level II toxicants, were locked, receiving no concern in previous combustion. The results highlighted risks during high proportion plastics co-combustion, which can help pollution reduction by tuning source wastes to enable healthy co-combustion.

Multikingdom characterization of gut microbiota in patients with rheumatoid arthritis and rheumatoid arthritis-associated interstitial lung disease.

Rheumatoid arthritis-associated interstitial lung disease (RA-ILD) is a serious and common extra-articular disease manifestation. Patients with RA-ILD experience reduced bacterial diversity and gut bacteriome alterations. However, the gut mycobiome and virome in these patients have been largely neglected. In this study, we performed whole-metagenome shotgun sequencing on fecal samples from 30 patients with RA-ILD, and 30 with RA-non-ILD, and 40 matched healthy controls. The gut bacteriome and mycobiome were explored using a reference-based approach, while the gut virome was profiled based on a nonredundant viral operational taxonomic unit (vOTU) catalog. The results revealed significant alterations in the gut microbiomes of both RA-ILD and RA-non-ILD groups compared with healthy controls. These alterations encompassed changes in the relative abundances of 351 bacterial species, 65 fungal species, and 4,367 vOTUs. Bacteria such as Bifidobacterium longum, Dorea formicigenerans, and Collinsella aerofaciens were enriched in both patient groups. Ruminococcus gnavus (RA-ILD), Gemmiger formicilis, and Ruminococcus bromii (RA-non-ILD) were uniquely enriched. Conversely, Faecalibacterium prausnitzii, Bacteroides spp., and Roseburia inulinivorans showed depletion in both patient groups. Mycobiome analysis revealed depletion of certain fungi, including Saccharomyces cerevisiae and Candida albicans, in patients with RA compared with healthy subjects. Notably, gut virome alterations were characterized by an increase in Siphoviridae and a decrease in Myoviridae, Microviridae, and Autographiviridae in both patient groups. Hence, multikingdom gut microbial signatures showed promise as diagnostic indicators for both RA-ILD and RA-non-ILD. Overall, this study provides comprehensive insights into the fecal virome, bacteriome, and mycobiome landscapes of RA-ILD and RA-non-ILD gut microbiota, thereby offering potential biomarkers for further mechanistic and clinical research.

The prognostic value of the combined neutrophil-to-lymphocyte ratio (NLR) and neutrophil-to-platelet ratio (NPR) in sepsis.

Sepsis is a severe disease characterized by high mortality rates. Our aim was to develop an early prognostic indicator of adverse outcomes in sepsis, utilizing easily accessible routine blood tests. A retrospective analysis of sepsis patients from the MIMIC-IV database was conducted. We performed univariate and multivariate regression analyses to identify independent risk factors associated with in-hospital mortality within 28 days. Logistic regression was utilized to combine the neutrophil-to-lymphocyte ratio (NLR) and the neutrophil-to-platelet ratio (NPR) into a composite score, denoted as NLR_NPR. We used ROC curves to compare the prognostic performance of the models and Kaplan-Meier survival curves to assess the 28 day survival rate. Subgroup analysis was performed to evaluate the applicability of NLR_NPR in different subpopulations based on specific characteristics. This study included a total of 1263 sepsis patients, of whom 179 died within 28 days of hospitalization, while 1084 survived beyond 28 days. Multivariate regression analysis identified age, respiratory rate, neutrophil-to-lymphocyte ratio (NLR), neutrophil-to-platelet ratio (NPR), hypertension, and sequential organ failure assessment (SOFA) score as independent risk factors for 28 day mortality in septic patients (P < 0.05). Additionally, in the prediction model based on blood cell-related parameters, the combined NLR_NPR score exhibited the highest predictive value for 28 day mortality (AUC = 0.6666), followed by NLR (AUC = 0.6456) and NPR (AUC = 0.6284). Importantly, the performance of the NLR_NPR score was superior to that of the commonly used SOFA score (AUC = 0.5613). Subgroup analysis showed that NLR_NPR remained an independent risk factor for 28 day in-hospital mortality in the subgroups of age, respiratory rate, and SOFA, although not in the hypertension subgroup. The combined use of NLR and NPR from routine blood tests represents a readily available and reliable predictive marker for 28 day mortality in sepsis patients. These results imply that clinicians should prioritize patients with higher NLR_NPR scores for closer monitoring to reduce mortality rates.

Pilot study assessing gut microbial diversity among sexual and gender minority young adults.

Evidence supports that people identifying as a sexual or gender minority (SGMs) experience minority-related stress resulting from discrimination or expectations of prejudice, and that this is associated with increased mental and physical health problems compared to cisgender heterosexuals. However, the biological mechanisms driving minority-related stress impacts remain unknown, including the role of the gut microbiome. Thus, the aim of this study was to determine the relationship between SGM status and gut microbiome health among young adults attending a 4-year university. To this end, a prospective pilot study was completed in the fall and spring semesters of 2021-22. Self-identified SGMs (N = 22) and cisgender-heterosexuals (CIS-HET, N = 43) completed in-person interviews to provide mental health data and demographic information. Nail and saliva samples were collected at the time of interview to quantify chronic and acute cortisol. Stool samples were collected within 48 hours of interview for microbiome analysis. Assessment of the gut microbiota identified a significant reduction in alpha diversity among the SGM group, even when adjusting for mental health outcome. SGM group showed trends for higher abundance of microbes in phylum Bacteroidetes and lower abundance of microbes in phyla Firmicutes, Actinobacteria, and Proteobacteria compared to the CIS-HET group. These findings support that the gut microbiome could be contributing to negative health effects among the SGM community.

Repurposing lipid-lowering drugs on asthma and lung function: evidence from a genetic association analysis.

OBJECTIVE: To explore the correlation between asthma risk and genetic variants affecting the expression or function of lipid-lowering drug targets. METHODS: We conducted Mendelian randomization (MR) analyses using variants in several genes associated with lipid-lowering medication targets: HMGCR (statin target), PCSK9 (alirocumab target), NPC1L1 (ezetimibe target), APOB (mipomersen target), ANGPTL3 (evinacumab target), PPARA (fenofibrate target), and APOC3 (volanesorsen target), as well as LDLR and LPL. Our objective was to investigate the relationship between lipid-lowering drugs and asthma through MR. Finally, we assessed the efficacy and stability of the MR analysis using the MR Egger and inverse variance weighted (IVW) methods. RESULTS: The elevated triglyceride (TG) levels associated with the APOC3, and LPL targets were found to increase asthma risk. Conversely, higher LDL-C levels driven by LDLR were found to decrease asthma risk. Additionally, LDL-C levels (driven by APOB, NPC1L1 and HMGCR targets) and TG levels (driven by the LPL target) were associated with improved lung function (FEV1/FVC). LDL-C levels driven by PCSK9 were associated with decreased lung function (FEV1/FVC). CONCLUSION: In conclusion, our findings suggest a likely causal relationship between asthma and lipid-lowering drugs. Moreover, there is compelling evidence indicating that lipid-lowering therapies could play a crucial role in the future management of asthma.

Synergistic interactions in core microbiome Rhizobiales accelerate 1,4-dioxane biodegradation.

Next-generation sequencing (NGS) has revolutionized taxa identification within contaminant-degrading communities. However, uncovering a core degrading microbiome in diverse polluted environments and understanding its associated microbial interactions remains challenging. In this study, we isolated two distinct microbial consortia, namely MA-S and Cl-G, from separate environmental samples using 1,4-dioxane as a target pollutant. Both consortia exhibited a persistent prevalence of the phylum Proteobacteria, especially within the order Rhizobiales. Extensive analysis confirmed that Rhizobiales as the dominant microbial population (> 90 %) across successive degradation cycles, constituting the core degrading microbiome. Co-occurrence network analysis highlighted synergistic interactions within Rhizobiales, especially within the Shinella and Xanthobacter genera, facilitating efficient 1,4-dioxane degradation. The enrichment of Rhizobiales correlated with an increased abundance of essential genes such as PobA, HpaB, ADH, and ALDH. Shinella yambaruensis emerged as a key degrader in both consortia, identified through whole-genome sequencing and RNA-seq analysis, revealing genes implicated in 1,4-dioxane degradation pathways, such as PobA and HpaB. Direct and indirect co-cultivation experiments confirmed synergistic interaction between Shinella sp. and Xanthobacter sp., enhancing the degradation of 1,4-dioxane within the core microbiome Rhizobiales. Our findings advocate for integrating the core microbiome concept into engineered consortia to optimize 1,4-dioxane bioremediation strategies.

RNA modification gene WDR4 facilitates tumor progression and immunotherapy resistance in breast cancer.

INTRODUCTION: Growing interest toward RNA modification in cancer has inspired the exploration of gene sets related to multiple RNA modifications. However, a comprehensive elucidation of the clinical value of various RNA modifications in breast cancer is still lacking. OBJECTIVES: This study aimed to provide a strategy based on RNA modification-related genes for predicting therapy response and survival outcomes in breast cancer patients. METHODS: Genes related to thirteen RNA modification patterns were integrated for establishing a nine-gene-containing signature-RMscore. Alterations of tumor immune microenvironment and therapy response featured by different RMscore levels were assessed by bulk transcriptome, single-cell transcriptome and genomics analyses. The biological function of key RMscore-related molecules was investigated by cellular experiments in vitro and in vivo, using flow cytometry, immunohistochemistry and immunofluorescence staining. RESULTS: This study has raised an effective therapy strategy for breast cancer patients after a well-rounded investigation of RNA modification-related genes. With a great performance of predicting patient prognosis, high levels of the RMscore proposed in this study represented suppressive immune microenvironment and therapy resistance, including adjuvant chemotherapy and PD-L1 blockade treatment. As the key contributor of the RMscore, inhibition of WDR4 impaired breast cancer progression significantly in vitro and in vivo, as well as participated in regulating cell cycle and mTORC1 signaling pathway via m7G modification. CONCLUSION: Briefly, this study has developed promising and effective tactics to achieve the prediction of survival probabilities and treatment response in breast cancer patients.

Single-cell sequencing reveals the correlation of aggrephagy signaling and multiple myeloma immune microenvironment composition.

Aggrephagy, a type of autophagy, degrades the aggregation of misfolded protein in cells. However, the role of aggrephagy in multiple myeloma (MM) has not been fully demonstrated. In this study, we first investigated the correlation between aggrephagy signaling, MM immune microenvironment composition and disease prognosis. Single-cell RNA-seq data, including the expression profiles of 12,187 single cells from seven MM bone marrow (BM) and seven healthy BM samples, were analyzed by non-negative matrix factorization for 44 aggrephagy-related genes. Bulk RNA-seq cohorts from the Gene Expression Omnibus database were used to evaluate the prognostic value of aggrephagy-related immune cell subtypes and predict immune checkpoint blockade immunotherapeutic response in MM. Compared with healthy BM, MM BM exhibited different patterns of aggrephagy-related gene expression. In MM BM, macrophages, CD8+ T cells, B cells and natural killer cells could be grouped into four to nine aggrephagy-related subclusters. The signature of aggrephagy signaling molecule expression in the immune cells correlates with the patient's prognosis. Our investigation provides a novel view of aggrephagy signaling in MM tumor microenvironment cells, which might be a prognostic indicator and potential target for MM treatment.

AI models predicting breast cancer distant metastasis using LightGBM with clinical blood markers and ultrasound maximum diameter.

Breast cancer metastasis significantly impacts women's health globally. This study aimed to construct predictive models using clinical blood markers and ultrasound data to predict distant metastasis in breast cancer patients, ensuring clinical applicability, cost-effectiveness, relative non-invasiveness, and accessibility of these models. Analysis was conducted on data from 416 patients across two centers, focusing on clinical blood markers (tumor markers, liver and kidney function indicators, blood lipid markers, cardiovascular biomarkers) and maximum lesion diameter from ultrasound. Feature reduction was performed using Spearman correlation and LASSO regression. Two models were built using LightGBM: a clinical model (using clinical blood markers) and a combined model (incorporating clinical blood markers and ultrasound features), validated in training, internal test, and external validation (test1) cohorts. Feature importance analysis was conducted for both models, followed by univariate and multivariate regression analyses of these features. The AUC values of the clinical model in the training, internal test, and external validation (test1) cohorts were 0.950, 0.795, and 0.883, respectively. The combined model showed AUC values of 0.955, 0.835, and 0.918 in the training, internal test, and external validation (test1) cohorts, respectively. Clinical utility curve analysis indicated the combined model's superior net benefit in identifying breast cancer with distant metastasis across all cohorts. This suggests the combined model's superior discriminatory ability and strong generalization performance. Creatine kinase isoenzyme (CK-MB), CEA, CA153, albumin, creatine kinase, and maximum lesion diameter from ultrasound played significant roles in model prediction. CA153, CK-MB, lipoprotein (a), and maximum lesion diameter from ultrasound positively correlated with breast cancer distant metastasis, while indirect bilirubin and magnesium ions showed negative correlations. This study successfully utilized clinical blood markers and ultrasound data to develop AI models for predicting distant metastasis in breast cancer. The combined model, incorporating clinical blood markers and ultrasound features, exhibited higher accuracy, suggesting its potential clinical utility in predicting and identifying breast cancer distant metastasis. These findings highlight the potential prospects of developing cost-effective and accessible predictive tools in clinical oncology.

Post-condylectomy orthodontic treatment for a severe asymmetrical open bite in a condylar hyperplasia patient.

A satisfactory treatment of an 18-year-old lady was reported with right combination-type condylar hyperplasia (CH) in active phase. The chin severely deviated to the left, with the right gonial angle locating at a lower level. Intraorally, the lower centre line shifted to the left, the scale of which reached the width of one lower incisor. The right molar relation was mesial. Right maxillary second molar over-erupted without contact to lower teeth. There had been 2.5-mm anterior open bite (AOB) before surgery (T1) due to the tongue-spitting habit. After judging the benefits and disadvantages of all treatment alternatives, the decision was made to perform a right condylectomy and post-surgery orthodontics. Before orthodontics (T2) when the chin was positioned centred, an asymmetrical open bite occurred, caused by pre-contact between the right maxillary and mandibular second molars. Meanwhile, the AOB at T2 became 11.5mm. Orthodontic-related treatment included four premolars extraction and intrusion of bilateral maxillary molars using four miniscrews. Finally, this treatment achieved a clinically centred chin with two gonial angles at the same level. Post-condylectomy, the large AOB was resolved, together with a bilateral neutral molar relationship and alignment of the incisor midlines. Besides, the resected right condyle was covered by a continuous cortex bone and returned to the glenoid fossa. In sum, a high-challenging combined-type CH case was accomplished with impressive improvement in facial and occlusal symmetry, thanks to condylectomy and post-surgery miniscrew-assisted orthodontics.

Serum metabolomics reveals the metabolic profile and potential biomarkers of ankylosing spondylitis.

Ankylosing spondylitis (AS) is a chronic systemic inflammatory disease that significantly impairs physical function in young individuals. However, the identification of radiographic changes in AS is frequently delayed, and the diagnostic efficacy of biomarkers like HLA-B27 remains moderately effective, with unsatisfactory sensitivity and specificity. In contrast to existing literature, our current experiment utilized a larger sample size and employed both untargeted and targeted UHPLC-QTOF-MS/MS based metabolomics to identify the metabolite profile and potential biomarkers of AS. The results indicated a notable divergence between the two groups, and a total of 170 different metabolites were identified, which were associated with the 6 primary metabolic pathways exhibiting a correlation with AS. Among these, 26 metabolites exhibited high sensitivity and specificity with area under curve (AUC) values greater than 0.8. Subsequent targeted quantitative analysis discovered 3 metabolites, namely 3-amino-2-piperidone, hypoxanthine and octadecylamine, exhibiting excellent distinguishing ability based on the results of the ROC curve and the Random Forest model, thus qualifying as potential biomarkers for AS. Summarily, our untargeted and targeted metabolomics investigation offers novel and precise insights into potential biomarkers for AS, potentially enhancing diagnostic capabilities and furthering the comprehension of the condition's pathophysiology.

Suppression of negative transfer in motor imagery brain-computer interface based on mutual information and Pearson correlation coefficient.

The focus of this paper is on the main challenges in brain-computer interface transfer learning: how to address data characteristic length and the source domain sample selection problems caused by individual differences. To overcome the negative migration that results from feature length, we propose a migration algorithm based on mutual information transfer (MIT), which selects effective features by calculating the entropy value of the probability distribution and conditional distribution, thereby reducing negative migration and improving learning efficiency. Source domain participants who differ too much from the target domain distribution can affect the overall classification performance. On the basis of MIT, we propose the Pearson correlation coefficient source domain automatic selection algorithm (PDAS algorithm). The PDAS algorithm can automatically select the appropriate source domain participants according to the target domain distribution, which reduces the negative migration of participant data among the source domain participants, improves experimental accuracy, and greatly reduces training time. The two proposed algorithms were tested offline and online on two public datasets, and the results were compared with those from existing advanced algorithms. The experimental results showed that the MIT algorithm and the MIT + PDAS algorithm had obvious advantages.

Nonreciprocal magnetoacoustic waves with out-of-plane phononic angular momenta.

Surface acoustic wave (SAW) can carry phononic angular momentum, showing great potential as an energy-efficient way to control magnetism. Still, out-of-plane phononic angular momentum in SAW and its interaction with magnetism remain elusive. Here, we studied the SAW-induced magnetoacoustic waves and spin pumping in Ni-based films on LiNbO3 with selected SAW propagation direction. The crystal inversion asymmetry induces circularly polarized phonons with large out-of-plane angular momenta so that up to 60% of the SAW power attenuates nonreciprocally controlled by the out-of-plane magnetization component. The SAW propagation direction dependence of the nonreciprocity verifies the crystal origin of the phononic angular momentum, and a chiral spin pumping demonstrates that the circular polarization can control the spin current generation efficiency. These results provide an additional degree of freedom for the acoustic control of magnetism and open an avenue for applying circularly polarized phonons.

A ferritin-based nanoparticle displaying a neutralizing epitope for foot-and-mouth disease virus (FMDV) confers partial protection in guinea pigs.

BACKGROUND: Foot-and-mouth disease (FMD) is a devastating disease affecting cloven-hoofed animals, that leads to significant economic losses in affected countries and regions. Currently, there is an evident inclination towards the utilization of nanoparticles as powerful platforms for innovative vaccine development. Therefore, this study developed a ferritin-based nanoparticle (FNP) vaccine that displays a neutralizing epitope of foot-and-mouth disease virus (FMDV) VP1 (aa 140-158) on the surface of FNP, and evaluated the immunogenicity and protective efficacy of these FNPs in mouse and guinea pig models to provide a strategy for developing potential FMD vaccines. RESULTS: This study expressed the recombinant proteins Hpf, HPF-NE and HPF-T34E via an E. coli expression system. The results showed that the recombinant proteins Hpf, Hpf-NE and Hpf-T34E could be effectively assembled into nanoparticles. Subsequently, we evaluated the immunogenicity of the Hpf, Hpf-NE and Hpf-T34E proteins in mice, as well as the immunogenicity and protectiveness of the Hpf-T34E protein in guinea pigs. The results of the mouse experiment showed that the immune efficacy in the Hpf-T34E group was greater than the Hpf-NE group. The results from guinea pigs immunized with Hpf-T34E showed that the immune efficacy was largely consistent with the immunogenicity of the FMD inactivated vaccine (IV) and could confer partial protection against FMDV challenge in guinea pigs. CONCLUSIONS: The Hpf-T34E nanoparticles stand out as a superior choice for a subunit vaccine candidate against FMD, offering effective protection in FMDV-infected model animals. FNP-based vaccines exhibit excellent safety and immunogenicity, thus representing a promising strategy for the continued development of highly efficient and safe FMD vaccines.

A bibliometric analysis based on hotspots and frontier trends of positron emission tomography/computed tomography utility in bone and soft tissue sarcoma.

Purpose: This study aimed to analyze articles on the diagnosis and treatment of bone and soft tissue sarcoma using positron emission tomography (PET)/computed tomography (CT) published in the last 13 years. The objective was to conduct a bibliometric analysis and identify the research hotspots and emerging trends. Methods: Web of Science was used to search for articles on PET/CT diagnosis and treatment of bone and soft tissue sarcoma published from January 2010 to June 2023. CiteSpace was utilized to import data for bibliometric analysis. Results: In total, 425 relevant publications were identified. Publications have maintained a relatively stable growth rate for the past 13 years. The USA has the highest number of published articles (139) and the highest centrality (0.35). The UDICE-French Research Universities group is the most influential institution. BYUN BH is a prominent contributor to this field. The Journal of Clinical Oncology has the highest impact factor in the field. Conclusion: The clinical application of PET/CT is currently a research hotspot. Upcoming areas of study concentrate on the merging of PET/CT with advanced machine learning and/or alternative imaging methods, novel imaging substances, and the fusion of diagnosis and therapy. The use of PET/CT has progressively become a crucial element in the identification and management of sarcomas. To confirm its efficacy, there is a need for extensive, multicenter, prospective studies.