Microneedle Array Electrodes Fabricated With 3D Printing Technology for High-Quality Electrophysiological Acquisition.

Electrophysiological recordings are vital in assessing biological functions, diagnosing diseases, and facilitating biofeedback and rehabilitation. The applications of conventional wet (gel) electrodes often come with some limitations. Microneedle array electrodes (MAEs) present a possible solution for high-quality electrophysiological acquisition, while the prior technologies for MAE fabrication have been either complex, expensive, or incapable of producing microneedles with uniform dimensions. This work employed a projection stereolithography (P µ SL) 3D printing technology to fabricate MAEs with micrometer-level precision. The MAEs were compared with gel and flat electrodes on electrode-skin interface impedance (EII) and performances of EMG and ECG acquisition. The experimental results indicate that the P µ SL 3D printing technology contributed to an easy-to-perform and low-cost fabrication approach for MAEs. The developed MAEs exhibited promising EII and enabled a stable EMG and ECG acquisition in different conditions without inducing skin allergies, inflammation, or injuries. This research lies in the development of a type of customizable MAE with considerable biomedical application potentials for ultra-minimally invasive or non-invasive electrophysiological acquisition.

Targeting HMGCS1 restores chemotherapy sensitivity in acute myeloid leukemia.

Acute myeloid leukemia (AML) is a common hematological malignancy with overall poor prognosis. Exploring novel targets is urgent and necessary to improve the clinical outcome of relapsed and refractory (RR) AML patients. Through clinical specimens, animal models and cell-level studies, we explored the specific mechanism of 3-hydroxy-3-methylglutaryl coenzyme A synthase 1 (HMGCS1) in AML and the mechanism of targeting HMGCS1 to attenuate cell proliferation, increase chemotherapy sensitivity and improve the occurrence and development of AML. Here, we reveal that HMGCS1 is overexpressed in RR patients and negatively related to overall survival (OS). Knocking out HMGCS1 in AML cells attenuated cell proliferation and increased chemotherapy sensitivity, while stable overexpression of HMGCS1 had the opposite effects. Mechanistically, we identified that knockout of HMGCS1 suppressed mitogen-activated protein kinase (MAPK) pathway activity, while overexpression of HMGCS1 could remarkably enhance the pathway. U0126, a MEK1 inhibitor, offset the effects of HMGCS1 overexpression, indicating that HMGCS1 promotes RR AML through the MAPK pathway. Further, we verified that hymeglusin, a specific inhibitor of HMGCS1, decreases cell growth both in AML cell lines and primary bone marrow cells of AML patients. Furthermore, combination of hymeglusin and the common chemotherapeutic drug cytarabine and adriamycin (ADR) had synergistic toxic effects on AML cells. Our study demonstrates the important role of HMGCS1 in AML, and targeting this protein is promising for the treatment of RR AML.

A novel mechanism behind irreversible development of cartilage degradation driven articular cartilage defects revealed by rat model: The chain reaction initiated by extracellular vesicles delivered LOC102546541.

BACKGROUND: Articular cartilage defects (ACD) are injuries with a diameter greater than 3 mm, resulting from wear and tear on joints. When the diameter of the defect exceeds 6 mm, it can further damage the surrounding joint cartilage, causing osteoarthritis (OA). Try to explain why OA is an irreversible disease, we hypothesize that damaged articular chondrocytes (DAC) may have reduced capacities to repair cartilage because its extracellular vesicle (EVs) that might directly contribute to OA formation. METHODS: In this study, DAC-EVs and AC-EVs were isolated using ultracentrifugation. Next-generation sequencing was employed to screen for a pathogenic long non-coding RNA (lncRNA). After verifying its function in vitro, the corresponding small interfering RNA (siRNA) was constructed and loaded into extracellular vesicles, which were then injected into the knee joint cavities of rats. RESULTS: The results revealed that DAC-EVs packaged lncRNA LOC102546541 acts as a competitive endogenous RNA (ceRNA) of MMP13, down-regulating miR-632. Consequently, the function of MMP13 in degrading the extracellular matrix is enhanced, promoting the development of osteoarthritis. CONCLUSIONS: This study uncovered a novel mode of OA pathogenesis using rat models, which DAC deliver pathogenic LOC102546541 packaged EVs to normal articular chondrocytes, amplifying the degradation of the extracellular matrix. Nonetheless, the functions of highly homologous human gene of LOC102546541 need to be verified in the future.

Development and validation of bioanalytical assays for the quantification of 9MW2821, a nectin-4-targeting antibody-drug conjugate.

We designed and developed 9MW2821, an anti-Nectin-4 antibody-drug conjugate (ADC) with an enzymatically cleavable valine-citrulline linker and monomethyl auristatin E (MMAE) as the payload. Four bioanalytical assays for total antibodies, conjugated antibodies, conjugated payload, and free payload were then developed and validated for the comprehensive evaluation of the multiple drug forms of 9MW2821. Specific sandwich enzyme-linked immunosorbent assays were used to quantify total antibodies and conjugated antibody, showing good drug-to-antibody ratio (DAR) tolerance. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) was used to determine free MMAE, and conjugated MMAE was quantified using a combination of ligand-binding assay (LBA) and LC-MS/MS. Based on these four assays, we studied the serum stability and monkey pharmacokinetic profiles of 9MW2821, and the in vivo DAR of 9MW2821 was calculated and dynamically monitored. In conclusion, we developed and validated series of bioanalytical assays to quantify multiple forms of 9MW2821, a new ADC, and used the assays to evaluate the serum stability and monkey pharmacokinetic characteristics. The results indicate good linker stability and suggest that the developed assays can be further used in clinical settings.

The efficacy of low frequency repetitive transcial magnetic stimulation for treating auditory verbal hallucinations in schizophrenia: Insights from functional gradient analyses.

Background: Auditory Verbal Hallucinations (AVH) constitute a prominent feature of schizophrenia. Although low-frequency repetitive transcranial magnetic stimulation (rTMS) has demonstrated therapeutic benefits in ameliorating AVH, the underlying mechanisms of its efficacy necessitate further elucidation. Objective: This study investigated the cortical gradient characteristics and their associations with clinical responses in schizophrenia patients with AVH, mediated through 1 Hz rTMS targeting the left temporoparietal junction. Method: Functional gradient metrics were employed to examine the hierarchy patterns of cortical organization, capturing whole-brain functional connectivity profiles in patients and controls. Results: The 1 Hz rTMS treatment effectively ameliorated the positive symptoms in patients, specifically targeting AVH. Initial evaluations revealed expanded global gradient distribution patterns and specific principal gradient variations in certain brain regions in patients at baseline compared to a control cohort. Following treatment, these divergent global and local patterns showed signs of normalizing. Furthermore, there was observed a closer alignment in between-network dispersion among various networks after treatment, including the somatomotor, attention, and limbic networks, indicating a potential harmonization of brain functionality. Conclusion: Low-frequency rTMS induces alternations in principal functional gradient patterns, may serve as imaging markers to elucidate the mechanisms underpinning the therapeutic efficacy of rTMS on AVH in schizophrenia.

Mendelian Randomization Reveals: Triglycerides and Sensorineural Hearing Loss.

BACKGROUND: Sensorineural hearing loss (SNHL) is a multifactorial disorder with potential links to various physiological systems, including the cardiovascular system via blood lipid levels such as triglycerides (TG). This study investigates the causal relationship between TG levels and SNHL using Mendelian randomization (MR), which offers a method to reduce confounding and reverse causality by using genetic variants as instrumental variables. METHODS: Utilizing publicly available genome-wide association study (GWAS) data, we performed a two-sample MR analysis. The initial analysis unveiled a causal relationship between TG (GWAS ID: ebi-a-GCST90018975) and SNHL (GWAS ID: finn b-H8_HL_SEN-NAS). Subsequent analysis validated this through MR with a larger sample size for TG (GWAS ID: ieu-b-111) and SNHL. To conduct the MR analysis, we utilized several methods including inverse-variance weighted (IVW), MR Egger, weighted median, and weighted mode. We also employed Cochrane's Q test to identify any heterogeneity in the MR results. To detect horizontal pleiotropy, we conducted the MR-Egger intercept test and MR pleiotropy residual sum and outliers (MR-PRESSO) test. We performed a leave-one-out analysis to assess the sensitivity of this association. Finally, a meta-analysis of the MR results was undertaken. RESULTS: Our study found a significant positive correlation between TG and SNHL, with OR values of 1.14 (95% CI: 1.07-1.23, p < 0.001) in the IVW analysis and 1.09 (95% CI: 1.03-1.16, p < 0.006) in the replicate analysis. We also found no evidence of horizontal pleiotropy or heterogeneity between the genetic variants (p > 0.05), and a leave-one-out test confirmed the stability and robustness of this association. The meta-analysis combining the initial and replicate analyses showed a significant causal effect with OR values of 1.11 (95% CI: 1.06-1.16, p = 0.01). CONCLUSION: These findings indicate TG as a risk factor for SNHL, suggesting potential pathways for prevention and intervention in populations at risk. This conclusion underscores the importance of managing TG levels as a strategy to mitigate the risk of developing SNHL.

Proteome-wide Mendelian randomization identifies therapeutic targets for ankylosing spondylitis.

Background: Ankylosing Spondylitis (AS) is a chronic inflammatory disorder which can lead to considerable pain and disability. Mendelian randomization (MR) has been extensively applied for repurposing licensed drugs and uncovering new therapeutic targets. Our objective is to pinpoint innovative therapeutic protein targets for AS and assess the potential adverse effects of druggable proteins. Methods: We conducted a comprehensive proteome-wide MR study to assess the causal relationships between plasma proteins and the risk of AS. The plasma proteins were sourced from the UK Biobank Pharma Proteomics Project (UKB-PPP) database, encompassing GWAS data for 2,940 plasma proteins. Additionally, GWAS data for AS were extracted from the R9 version of the Finnish database, including 2,860 patients and 270,964 controls. The colocalization analysis was executed to identify shared causal variants between plasma proteins and AS. Finally, we examined the potential adverse effects of druggable proteins for AS therapy by conducting a phenome-wide association study (PheWAS) utilizing the extensive Finnish database in version R9, encompassing 2,272 phenotypes categorized into 46 groups. Results: The findings revealed a positive genetic association between the predicted plasma levels of six proteins and an elevated risk of AS, while two proteins exhibited an inverse association with AS risk (P fdr < 0.05). Among these eight plasma proteins, colocalization analysis identified AIF1, TNF, FKBPL, AGER, ALDH5A1, and ACOT13 as shared variation with AS(PPH3+PPH4>0.8), suggesting that they represent potential direct targets for AS intervention. Further phenotype-wide association studies have shown some potential side effects of these six targets (P fdr < 0.05). Conclusion: Our investigation examined the causal connections between six plasma proteins and AS, providing a comprehensive understanding of potential therapeutic targets.

Association of neutrophil-to-lymphocyte ratio and risk of cardiovascular and all-cause mortality in hypertension patients.

Background and objective: Hypertension affects over a billion people worldwide and is often associated with poor prognoses. The neutrophil-to-lymphocyte ratio (NLR) has become a significant marker, showing a connection to adverse outcomes in cardiovascular diseases (CVDs). The objective of this study is to examine the relationship between the NLR and outcomes in patients with hypertension. Methods: The study included hypertensive individuals who were surveyed in the National Health and Nutrition Examination Survey (NHANES) from 2009 to 2018. Mortality status was determined using the data from National Death Index (NDI). To investigate the dose-response relationship, restricted cubic spline (RCS) models were used. This study employed adjusted cox proportional hazards regression models to compute hazard ratios (HRs) and their corresponding 95% confidence intervals (CIs) for all-cause and cardiovascular mortality. The predictive accuracy of the NLR for survival outcomes was assessed utilizing time-dependent receiver operating characteristic (ROC) curve analysis. Results: A total of 13,724 participants were included in the final analysis, including 7073 males and 6651 females. The cohort was stratified into higher (>2.0) and lower (≤2.0) NLR groups according to the median value. Over a median follow-up of 64 months, there were 1619 all-cause deaths and 522 cardiovascular deaths among participants. The RCS analysis indicated a non-linear relationship between NLR and the risk of mortality. The adjusted model showed that the group with a higher NLR had a significantly higher risk of all-cause (HR 1.47, 95% CI 1.22-1.77) and cardiovascular mortality (HR 2.08, 95% CI 1.52-2.86). ROC analysis showed that the area under the curves (AUCs) of 0.692, 0.662, 0.644, and 0.625 for predicting all-cause mortality, and 0.712, 0.692, 0.687, and 0.660 for cardiovascular mortality at 1, 3, 5, and 10 years. Conclusion: Elevated NLR is associated with increased risk of cardiovascular and all-cause mortality, and NLR may independently predict outcomes in individuals with hypertension.

Within-Session Reliability of fNIRS in Robot-Assisted Upper-Limb Training.

Functional near-infrared spectroscopy (fNIRS) seems opportune for neurofeedback in robot-assisted rehabilitation training due to its noninvasive, less physical restriction, and no electromagnetic disturbance. Previous research has proved the cross-session reliability of fNIRS responses to non-motor tasks (e.g., visual stimuli) and fine-motor tasks (e.g., finger tapping). However, it is still unknown whether fNIRS responses remain reliable 1) in gross-motor tasks, 2) within a training session, and 3) for different training parameters. Hence, this study aimed to investigate the within-session reliability of fNIRS responses to gross-motor tasks for different training parameters. Ten healthy participants were recruited to conduct right elbow extension-flexion in three robot-assisted modes. The Passive mode was fully motor-actuated, while Active1 and Active2 modes involved active engagement with different resistance levels. FNIRS data of three identical runs were used to assess the within-session reliability in terms of the map- ( R2 ) and cluster-wise ( Roverlap ) spatial reproducibility and the intraclass correlation (ICC) of temporal features. The results revealed good spatial reliability ( R2 up to 0.69, Roverlap up to 0.68) at the subject level. Besides, the within-session temporal reliabilities of Slope, Max/Min, and Mean were between good and excellent ( ICC < 0.86). We also found that the within-session reliability was positively correlated with the intensity of the training mode, except for the temporal reliability of HbO in Active2 mode. Overall, our results demonstrated good within-session reliability of fNIRS responses, suggesting fNIRS as reliable neurofeedback for constructing closed-loop robot-assisted rehabilitation systems.

Loading Recognition for Lumbar Exoskeleton Based on Multi-Channel Surface Electromyography From Low Back Muscles.

Lumbar exoskeleton is an assistive robot, which can reduce the risk of injury and pain in low back muscles when lifting heavy objects. An important challenge it faces involves enhancing assistance with minimal muscle energy consumption. One of the viable solutions is to adjust the force or torque of assistance in response to changes in the load on the low back muscles. It requires accurate loading recognition, which has yet to yield satisfactory outcomes due to the limitations of available measurement tools and load classification methods. This study aimed to precisely identify muscle loading using a multi-channel surface electromyographic (sEMG) electrode array on the low back muscles, combined with a participant-specific load classification method. Ten healthy participants performed a stoop lifting task with objects of varying weights, while sEMG data was collected from the low back muscles using a 3x7 electrode array. Nineteen time segments of the lifting phase were identified, and time-domain sEMG features were extracted from each segment. Participant-specific classifiers were built using four classification algorithms to determine the object weight in each time segment, and the classification performance was evaluated using a 5-fold cross-validation method. The artificial neural network classifier achieved an impressive accuracy of up to 96%, consistently improving as the lifting phase progressed, peaking towards the end of the lifting movement. This study successfully achieves accurate recognition of load on low back muscles during the object lifting task. The obtained results hold significant potential in effectively reducing muscle energy consumption when wearing a lumbar exoskeleton.

Low-frequency rTMS induces modifications in cortical structural connectivity - functional connectivity coupling in schizophrenia patients with auditory verbal hallucinations.

Auditory verbal hallucinations (AVH) are distinctive clinical manifestations of schizophrenia. While low-frequency repetitive transcranial magnetic stimulation (rTMS) has demonstrated potential in mitigating AVH, the precise mechanisms by which it operates remain obscure. This study aimed to investigate alternations in structural connectivity and functional connectivity (SC-FC) coupling among schizophrenia patients with AVH prior to and following treatment with 1 Hz rTMS that specifically targets the left temporoparietal junction. Initially, patients exhibited significantly reduced macroscopic whole brain level SC-FC coupling compared to healthy controls. Notably, SC-FC coupling increased significantly across multiple networks, including the somatomotor, dorsal attention, ventral attention, frontoparietal control, and default mode networks, following rTMS treatment. Significant alternations in SC-FC coupling were noted in critical nodes comprising the somatomotor network and the default mode network, such as the precentral gyrus and the ventromedial prefrontal cortex, respectively. The alternations in SC-FC coupling exhibited a correlation with the amelioration of clinical symptom. The results of our study illuminate the intricate relationship between white matter structures and neuronal activity in patients who are receiving low-frequency rTMS. This advances our understanding of the foundational mechanisms underlying rTMS treatment for AVH.

Unveiling the flames: macrophage pyroptosis and its crucial role in liver diseases.

Macrophages play a critical role in innate immunity, with approximately 90% of the total macrophage population in the human body residing in the liver. This population encompasses both resident and infiltrating macrophages. Recent studies highlight the pivotal role of liver macrophages in various aspects such as liver inflammation, regeneration, and immune regulation. A novel pro-inflammatory programmed cell death, pyroptosis, initially identified in macrophages, has garnered substantial attention since its discovery. Studies investigating pyroptosis and inflammation progression have particularly centered around macrophages. In liver diseases, pyroptosis plays an important role in driving the inflammatory response, facilitating the fibrotic process, and promoting tumor progression. Notably, the role of macrophage pyroptosis cannot be understated. This review primarily focuses on the role of macrophage pyroptosis in liver diseases. Additionally, it underscores the therapeutic potential inherent in targeting macrophage pyroptosis.

An interpretable model based on graph learning for diagnosis of Parkinson's disease with voice-related EEG.

Parkinson's disease (PD) exhibits significant clinical heterogeneity, presenting challenges in the identification of reliable electroencephalogram (EEG) biomarkers. Machine learning techniques have been integrated with resting-state EEG for PD diagnosis, but their practicality is constrained by the interpretable features and the stochastic nature of resting-state EEG. The present study proposes a novel and interpretable deep learning model, graph signal processing-graph convolutional networks (GSP-GCNs), using event-related EEG data obtained from a specific task involving vocal pitch regulation for PD diagnosis. By incorporating both local and global information from single-hop and multi-hop networks, our proposed GSP-GCNs models achieved an averaged classification accuracy of 90.2%, exhibiting a significant improvement of 9.5% over other deep learning models. Moreover, the interpretability analysis revealed discriminative distributions of large-scale EEG networks and topographic map of microstate MS5 learned by our models, primarily located in the left ventral premotor cortex, superior temporal gyrus, and Broca's area that are implicated in PD-related speech disorders, reflecting our GSP-GCN models' ability to provide interpretable insights identifying distinctive EEG biomarkers from large-scale networks. These findings demonstrate the potential of interpretable deep learning models coupled with voice-related EEG signals for distinguishing PD patients from healthy controls with accuracy and elucidating the underlying neurobiological mechanisms.

The restoration ability of a short nap after sleep deprivation on the brain cognitive function: A dynamic functional connectivity analysis.

AIMS: The brain function impairment induced by sleep deprivation (SD) is temporary and can be fully reversed with sufficient sleep. However, in many cases, long-duration recovery sleep is not feasible. Thus, this study aimed to investigate whether a short nap after SD is sufficient to restore brain function. METHODS: The data of 38 subjects, including resting state functional magnetic resonance imaging data collected at three timepoints (before SD, after 30 h of SD, and after a short nap following SD) and psychomotor vigilance task (PVT) data, were collected. Dynamic functional connectivity (DFC) analysis was used to evaluate changes in brain states among three timepoints, and four DFC states were distinguished across the three timepoints. RESULTS: Before SD, state 2 (a resting-like FC matrix) was dominant (48.26%). However, after 30 h SD, the proportion of state 2 dramatically decreased, and state 3 (still resting-like, but FCs were weakened) became dominant (40.92%). The increased proportion of state 3 positively correlated with a larger PVT "lapse" time. After a nap, the proportions of states 2 and 3 significantly increased and decreased, respectively, and the change in proportion of state 2 negatively correlated with the change in PVT "lapse" time. CONCLUSIONS: Taken together, the results indicated that, after a nap, the cognitive function impairment caused by SD may be reversed to some extent. Additionally, DFC differed among timepoints, which was also associated with the extent of cognitive function impairment after SD (state 3) and the extent of recovery therefrom after a nap (state 2).

White matter structural topologic efficiency predicts individual resistance to sleep deprivation.

BACKGROUND: Sleep deprivation (SD) is commonplace in modern society and there are large individual differences in the vulnerability to SD. We aim to identify the structural network differences based on diffusion tensor imaging (DTI) that contribute to the individual different vulnerability to SD. METHODS: The number of psychomotor vigilance task (PVT) lapses was used to classify 49 healthy subjects on the basis of whether they were vulnerable or resistant to SD. DTI and graph theory approaches were used to investigate the topologic organization differences of the brain structural connectome between SD-vulnerable and -resistant individuals. We measured the level of global efficiency and clustering in rich club and non-rich club organizations. RESULTS: We demonstrated that participants vulnerable to SD had less global efficiency, network strength, and local efficiency but longer shortest path length compared with participants resistant to SD. Lower efficiency was mainly distributed in the right insula, bilateral thalamus, bilateral frontal, temporal, and temporal lobes. Furthermore, a disrupted subnetwork was observed that consisted of widespread connections. Moreover, the vulnerable group showed significantly decreased strength of the rich club compared with the resistant group. The strength of rich club connectivity was found to be correlated negatively with PVT performance (r = -0.395, p = 0.005). We further tested the reliability of the results. CONCLUSION: The findings revealed that individual differences in resistance to SD are related to disrupted topologic efficiency connectome pattern, and our study may provide potential connectome-based biomarkers for the early detection of the vulnerable degree to SD.

Orderly Nanodendritic Nickel Substitute for Raney Nickel Catalyst Improving Alkali Water Electrolyzer.

The development of nonprecious metal catalysts to meet the activity-stability balance at industrial-grade large current densities remains a challenge toward practical alkali-water electrolysis. Here, this work develops an orderly nanodendritic nickel (ND-Ni) catalyst that consists of ultrafine nanograins in chain-like conformation, which shows both excellent activity and robust stability for large current density hydrogen evolution reaction (HER) in alkaline media, superior to currently applied Raney nickel (R-Ni) catalyst in commercial alkali-water electrolyzer (AWE). The ND-Ni catalyst featured by a three-dimensional (3D) interconnecting microporous structure endows with high specific surface area and excellent conductivity and hydrophilicity, which together afford superior charge/mass transport favorable to HER kinetics at high current densities. An actual AWE with ND-Ni catalyst demonstrates durable water splitting with 1.0 A cm-2 at 1.71 V under industrial conditions and renders a record-low power consumption of 3.95 kW h Nm-3 with an energy efficiency close to 90%. The hydrogen price per gallon of gasoline equivalent (GGE) is calculated to be ≈$0.95, which is less than the target of $2.0 per GGE by 2026 from the U.S. Department of Energy. The results suggest the feasibility of ND-Ni substitute for R-Ni catalyst in commercial AWE.

A radiomics-based interpretable model to predict the pathological grade of pancreatic neuroendocrine tumors.

OBJECTIVES: To develop a computed tomography (CT) radiomics-based interpretable machine learning (ML) model to predict the pathological grade of pancreatic neuroendocrine tumors (pNETs) in a non-invasive manner. METHODS: Patients with pNETs who underwent contrast-enhanced abdominal CT between 2010 and 2022 were included in this retrospective study. Radiomics features were extracted, and five radiomics-based ML models, namely logistic regression (LR), random forest (RF), support vector machine (SVM), XGBoost, and GaussianNB, were developed. The performance of these models was evaluated using a time-independent testing set, and metrics such as sensitivity, specificity, accuracy, and the area under the receiver operating characteristic curve (AUC) were calculated. The accuracy of the radiomics model was compared to that of needle biopsy. The Shapley Additive Explanation (SHAP) tool and the correlation between radiomics and biological features were employed to explore the interpretability of the model. RESULTS: A total of 122 patients (mean age: 50 ± 14 years; 53 male) were included in the training set, whereas 100 patients (mean age: 48 ± 13 years; 50 male) were included in the testing set. The AUCs for LR, SVM, RF, XGBoost, and GaussianNB were 0.758, 0.742, 0.779, 0.744, and 0.745, respectively, with corresponding accuracies of 73.0%, 70.0%, 77.0%, 71.9%, and 72.9%. The SHAP tool identified two features of the venous phase as the most significant, which showed significant differences among the Ki-67 index or mitotic count subgroups (p < 0.001). CONCLUSIONS: An interpretable radiomics-based RF model can effectively differentiate between G1 and G2/3 of pNETs, demonstrating favorable interpretability. CLINICAL RELEVANCE STATEMENT: The radiomics-based interpretable model developed in this study has significant clinical relevance as it offers a non-invasive method for assessing the pathological grade of pancreatic neuroendocrine tumors and holds promise as an important complementary tool to traditional tissue biopsy. KEY POINTS: • A radiomics-based interpretable model was developed to predict the pathological grade of pNETs and compared with preoperative needle biopsy in terms of accuracy. • The model, based on CT radiomics, demonstrated favorable interpretability. • The radiomics model holds potential as a valuable complementary technique to preoperative needle biopsy; however, it should not be considered a replacement for biopsy.

Methane flux at the water-gas interface is influenced by complex interactions among phytoplankton, phosphorus inputs and methane-functional bacteria: A microcosm systems study.

Phytoplankton affect carbon cycling and emissions in eutrophic reservoirs dramatically, but our knowledge about carbon emissions response to phytoplankton bloom and phosphorus enrichment is rather limited. Here we performed a microcosm experiment with five treatments to investigate how phytoplankton blooms and phosphorus addition will impact the carbon emissions and the methane-functional bacterial community. During the 43-day incubation, the CH4 and CO2 flux at the water-air interface in the five water columns fluctuated between 7.536 and 16.689 µmol and between 2788.501 and 4142.726 µmol, respectively. The flux of CH4 and CO2 during phytoplankton decay was 1.542 to 10.397 times and 4.203 to 8.622 times higher, respectively, compared to that during phytoplankton growth. Furthermore, exogenous phosphorus increases bloom biomass of phytoplankton and subsequent CH4 production, even with low nitrogen concentrations. The addition of 1 mg KH2PO4 resulted in a conservative increase of 0.0715 µmol in CH4 emission and 11.911 µmol in CO2 emission in the water column, respectively, compared to the in-situ water column. High throughput sequencing determined that hydrogenotrophic Methanoregula dominated methanogens (MPB) and Methylocystaceae dominated methanotrophs (MOB) in the sediment. Phosphorus inhibited the relative abundance of Methanoregula after incubation, resulting in a significant decrease. Real-time quantitative polymerase chain reaction indicated that the absolute abundance of MPB and MOB (i.e., the mcrA gene and the pmoA gene) in the sediments ranged from 5.1354E+06 to 6.3176E+07 copies·g-1 and 1.1656E+06 to 9.5056E+06 copies·g-1, respectively. The mcrA gene showed a preference for sediments with high organic carbon content. The effect of eutrophication on CH4 emissions is closely related to nutrient load and distinct niche of methane-functional bacteria.