Initiation of rest-grazing during soil thawing improves interspecific relationships and stability of plant communities on alpine grassland.

Introduction: Grazing management is essential to maintain the stability of grassland ecosystems. Methods: To determine the optimal rest-grazing period of alpine meadow, five rest-grazing periods were set based on soil thawing and plant re-greening in this study. The niche, interspecific relationships, and stability of plant communities at different rest-grazing periods were investigated. Results: Rest-grazing during soil thawing resulted in a small niche width and niche overlap of plants, overall positive interspecific associations, and a high stability of plant communities. Delayed rest-grazing time to plant re-greening resulted in a large niche width and niche overlap of plants, overall negative interspecific associations, and a low stability of plant communities. Discussion: Rest-grazing in alpine meadows should begin as soon as possible to promote healthy and sustainable utilization of grasslands.

[Comparative study of computer-assisted and robot-assisted atlantoaxial pedicle screw implantation for reversible atlantoaxial dislocation].

Objective: To investigate the effectiveness of computer-assisted and robot-assisted atlantoaxial pedicle screw implantation for the treatment of reversible atlantoaxial dislocation (AAD). Methods: The clinical data of 42 patients with reversible AAD admitted between January 2020 and June 2023 and met the selection criteria were retrospectively analyzed, of whom 23 patients were treated with computer-assisted surgery (computer group) and 19 patients were treated with Mazor X spinal robot-assisted surgery (robot group). There was no significant difference in gender, age, T value of bone mineral density, body mass index, etiology, and preoperative Japanese Orthopaedic Association (JOA) score, Neck Dysfunction Index (NDI) between the two groups ( P>0.05). The operation time, screw implantation time, intraoperative blood loss, hand and wrist radiation exposure, and complications were recorded and compared between the two groups. Gertzbein classification was used to evaluate the accuracy of screw implantation. JOA score and NDI were used to evaluate the function before operation, at 3 days after operation, and at last follow-up. At last follow-up, the status of screws and bone fusion were observed by neck three-dimensional CT. Results: The operation time and hand and wrist radiation exposure of the computer group were significantly longer than those of the robot group ( P<0.05), and there was no significant difference in the screw implantation time and intraoperative blood loss between the two groups ( P>0.05). All patients were followed up 11-24 months, with an average of 19.6 months. There was no significant difference in the follow-up time between the two groups ( P>0.05). There was no significant difference in the accuracy of screw implantation between the two groups ( P>0.05). Except for 1 case of incision infection in the computer group, which improved after antibiotic treatment, there was no complication such as nerve and vertebral artery injury, screw loosening, or breakage in the two groups. The JOA score and NDI significantly improved in both groups at 3 days after operation and at last follow-up ( P<0.05) compared to those before operation, but there was no significant difference between the two groups ( P>0.05). At last follow-up, 21 patients (91.3%) in the computer group and 18 patients (94.7%) in the robot group achieved satisfactory atlantoaxial fusion, and there was no significant difference in the fusion rate between the two groups ( P>0.05). Conclusion: Computer-assisted or robot-assisted atlantoaxial pedicle screw implantation is safe and effective, and robotic navigation shortens operation time and reduces radiation exposure.

Artemisia smithii patches form fertile islands and lead to heterogeneity of soil bacteria and fungi within and around the patches in alpine meadows of the Qinghai-Tibetan Plateau.

Herbivore-avoided plant patches are one of the initial characteristics of natural grassland degradation. These vegetation patches can intensify the spatial heterogeneity of soil nutrients within these grasslands. However, the effects of non-edible plant patches patches on the spatial heterogeneity of microorganisms have not been sufficiently studied in alpine meadows of the Qinghai-Tibetan Plateau, especially patches formed by herbaceous plants. To answer this question, soil nutrients, plant assembly, and microbial communities were measured inside, around, and outside of Artemisia smithii patches. These were 0 m (within the patch), 0-1 m (one meter from the edge of the patch), 1-2 m (two meters from the edge of the patch), 2-3 m (three meters from the edge of the patch), and >30 m (non-patch grassland more than thirty meters from the edge of the patch). Our results showed that A. smithii patches accumulated more aboveground biomass (AGB) within the patches (0 m), and formed fertile islands with the soil around the patches. Additionally, A. smithii patches increased soil bacterial diversity within (0 m) and around (0-1 m) the patches by primarily enriching copiotrophic bacteria (Actinobacteria), while the diversity of fungal communities increased mainly in the 0-1 m area but not within the patches. Bacterial community diversity was driven by pH, urease, nitrate nitrogen (NO3 --N), and microbial biomass carbon (MBC). The contents of soil water (SWC), soil organic matter (SOM), urease, NO3 --N, and MBC were the main factors influencing the diversity of the fungal community. This study elucidates the vegetation, nutrients, and microbial heterogeneity and their interrelationships, which are observed in fertile islands of herbivore-avoided plant patches in alpine meadows, and provides further insights into the spatial pattern of nutrients in patchy degraded grasslands.

Unveiling the Therapeutic Potential of hUCMSC-Derived EVs in Intervertebral Disc Degeneration through MALAT1/miR-138-5p/SLC7A11 Coexpression Regulation.

Intervertebral disc degeneration (IVDD) is a prevalent chronic condition causing spinal pain and functional impairment. This study investigates the role of extracellular vesicles (EVs) derived from human umbilical cord mesenchymal stem cells (hUCMSCs) in regulating IVDD. Using RNA-seq, we analyzed differential expressions of lncRNA and miRNA in nucleus pulposus tissues from various mouse groups. We identified key regulatory molecules, MALAT1 and miRNA-138-5p, which contribute to IVDD. Further experiments demonstrated that MALAT1 can up-regulate SLC7A11 expression by competitively binding to miR-138-5p, forming a MALAT1/miR-138-5p/SLC7A11 coexpression regulatory network. This study elucidates the molecular mechanism by which hUCMSC-derived EVs regulate IVDD and could help develop novel therapeutic strategies for treating this condition. Our findings demonstrate that hUCMSCs-EVs inhibit ferroptosis in nucleus pulposus cells, thereby improving IVDD. These results highlight the therapeutic potential of hUCMSCs-EVs in ameliorating the development of IVDD, offering significant scientific and clinical implications for new treatments.

Fabrication and Evaluation of PCL/PLGA/ß-TCP Spiral-Structured Scaffolds for Bone Tissue Engineering.

Natural bone is a complex material that has been carefully designed. To prepare a successful bone substitute, two challenging conditions need to be met: biocompatible and bioactive materials for cell proliferation and differentiation, and appropriate mechanical stability after implantation. Therefore, a hybrid Poly ε-caprolactone/Poly(lactic-co-glycolide)/ß-tricalcium phosphate (PCL/PLGA/ß-TCP) scaffold has been introduced as a suitable composition that satisfies the above two conditions. The blended PCL and PLGA can improve the scaffold's mechanical properties and biocompatibility compared to single PCL or PLGA scaffolds. In addition, the incorporated ß-TCP increases the mechanical strength and osteogenic potential of PCL/PLGA scaffolds, while the polymer improves the mechanical stability of ceramic scaffolds. The PCL/PLGA/ß-TCP scaffold is designed using spiral structures to provide a much better transport system through the gaps between spiral walls than conventional cylindrical scaffolds. Human fetal osteoblasts (hFOBs) were cultured on spiral PCL/PLGA/ß-TCP (PPBS), cylindrical PCL/PLGA/ß-TCP (PPBC), and cylindrical PCL scaffolds for a total of 28 days. The cell proliferation, viability, and osteogenic differentiation capabilities were analyzed. Compared with PCL and PPBC scaffolds, the PPBS scaffold exhibits great biocompatibility and potential to stimulate cell proliferation and differentiation and, therefore, can serve as a bone substitute for bone tissue regeneration.

Dietary Clostridium autoethanogenum protein has dose-dependent influence on the gut microbiota, immunity, inflammation and disease resistance of abalone Haliotis discus hannai.

Clostridium autoethanogenum protein (CAP) is an eco-friendly protein source and has great application potential in aquafeeds. The present study aimed to investigate the effects of dietary CAP inclusion on the anti-oxidation, immunity, inflammation, disease resistance and gut microbiota of abalone Haliotis discus hannai after a 110-day feeding trial. Three isonitrogenous and isolipidic diets were formulated by adding 0 % (control), 4.10 % (CAP4.10) and 16.25 % (CAP16.25) of CAP, respectively. A total of 540 abalones with an initial mean body weight of 22.05 ± 0.19 g were randomly distributed in three groups with three replicates per group and 60 abalones per replicate. Results showed that the activities of superoxide dismutase and glutathione peroxidase in the cell-free hemolymph (CFH) were significantly decreased and the content of malondialdehyde in CFH was significantly increased in the CAP16.25 group. The diet with 4.1 % of CAP significantly increased the activities of lysozyme and acid phosphatase in CFH. The expressions of pro-inflammatory genes such as tumor necrosis factor-α (tnf-α), nuclear factor-κb (nf-κb) and toll-like receptor 4 (tlr4) in digestive gland were downregulated, and the expressions of anti-inflammatory genes such as ß-defensin and mytimacin 6 in digestive gland were upregulated in the CAP4.10 group. Dietary CAP inclusion significantly decreased the cumulative mortality of abalone after the challenge test with Vibrio parahaemolyticus for 7 days. Dietary CAP inclusion changed the composition of gut microbiota of abalone. Besides, the balance of the ecological interaction network of bacterial genera in the intestine of abalone was enhanced by dietary CAP. The association analysis showed that two bacterial genera Ruegeria and Bacteroides were closely correlated with the inflammatory genes. In conclusion, the 4.10 % of dietary CAP enhanced the immunity and disease resistance as well as inhibited the inflammation of abalone. The 16.25 % of dietary CAP decreased the anti-oxidative capacity of abalone. The structure of the gut microbiota of abalone changed with dietary CAP levels.

Monitoring imatinib decreasing pericyte coverage and HIF-1α level in a colorectal cancer model by an ultrahigh-field multiparametric MRI approach.

BACKGROUND: Excessive pericyte coverage promotes tumor growth, and a downregulation may solve this dilemma. Due to the double-edged sword role of vascular pericytes in tumor microenvironment (TME), indiscriminately decreasing pericyte coverage by imatinib causes poor treatment outcomes. Here, we optimized the use of imatinib in a colorectal cancer (CRC) model in high pericyte-coverage status, and revealed the value of multiparametric magnetic resonance imaging (mpMRI) at 9.4T in monitoring treatment-related changes in pericyte coverage and the TME. METHODS: CRC xenograft models were evaluated by histological vascular characterizations and mpMRI. Mice with the highest pericyte coverage were treated with imatinib or saline; then, vascular characterizations, tumor apoptosis and HIF-1α level were analyzed histologically, and alterations in the expression of Bcl-2/bax pathway were assessed through qPCR. The effects of imatinib were monitored by dynamic contrast-enhanced (DCE)-, diffusion-weighted imaging (DWI)- and amide proton transfer chemical exchange saturation transfer (APT CEST)-MRI at 9.4T. RESULTS: The DCE- parameters provided a good histologic match the tumor vascular characterizations. In the high pericyte coverage status, imatinib exhibited significant tumor growth inhibition, necrosis increase and pericyte coverage downregulation, and these changes were accompanied by increased vessel permeability, decreased microvessel density (MVD), increased tumor apoptosis and altered gene expression of apoptosis-related Bcl-2/bax pathway. Strategically, a 4-day imatinib effectively decreased pericyte coverage and HIF-1α level, and continuous treatment led to a less marked decrease in pericyte coverage and re-elevated HIF-1α level. Correlation analysis confirmed the feasibility of using mpMRI parameters to monitor imatinib treatment, with DCE-derived Ve and Ktrans being most correlated with pericyte coverage, Ve with vessel permeability, AUC with microvessel density (MVD), DWI-derived ADC with tumor apoptosis, and APT CEST-derived MTRasym at 1 µT with HIF-1α. CONCLUSIONS: These results provided an optimized imatinib regimen to achieve decreasing pericyte coverage and HIF-1α level in the high pericyte-coverage CRC model, and offered an ultrahigh-field multiparametric MRI approach for monitoring pericyte coverage and dynamics response of the TME to treatment.

The role of aryl hydrocarbon receptors in nutrient metabolism and immune regulation at the maternal-fetal interface.

The maternal-fetal interface is composed of the placenta, which is affiliated with the fetus, and the maternal decidua. During pregnancy, the placenta is mainly responsible for nutrient transport and immune tolerance maintenance, which plays a key role in fetal growth and development and pregnancy maintenance. The aryl hydrocarbon receptor (AhR) is a ligand-activated transcription factor that exists in various cell types at the maternal-fetal interface and is involved in multiple cellular processes. Recent studies have highlighted the role of AhR in regulating various physiological processes, including glucose and lipid metabolism, as well as tryptophan metabolism and immune responses, within non-pregnant tissues. This review shifts focus towards understanding how AhR modulation impacts metabolism and immune regulation at the maternal-fetal interface. This may implicate the development of pregnancy-related complications and the potential target of the AhR pathway for therapeutic strategies against poor pregnancy outcomes.

Pediatric diffuse intrinsic pontine glioma radiotherapy response prediction: MRI morphology and T2 intensity-based quantitative analyses.

OBJECTIVES: An easy-to-implement MRI model for predicting partial response (PR) postradiotherapy for diffuse intrinsic pontine glioma (DIPG) is lacking. Utilizing quantitative T2 signal intensity and introducing a visual evaluation method based on T2 signal intensity heterogeneity, and compared MRI radiomic models for predicting radiotherapy response in pediatric patients with DIPG. METHODS: We retrospectively included patients with brainstem gliomas aged ≤ 18 years admitted between July 2011 and March 2023. Applying Response Assessment in Pediatric Neuro-Oncology criteria, we categorized patients into PR and non-PR groups. For qualitative analysis, tumor heterogeneity vision was classified into four grades based on T2-weighted images. Quantitative analysis included the relative T2 signal intensity ratio (rT2SR), extra pons volume ratio, and tumor ring-enhancement volume. Radiomic features were extracted from T2-weighted and T1-enhanced images of volumes of interest. Univariate analysis was used to identify independent variables related to PR. Multivariate logistic regression was performed using significant variables (p < 0.05) from univariate analysis. RESULTS: Of 140 patients (training n = 109, and test n = 31), 64 (45.7%) achieved PR. The AUC of the predictive model with extrapontine volume ratio, rT2SRmax-min (rT2SRdif), and grade was 0.89. The AUCs of the T2-weighted and T1WI-enhanced models with radiomic signatures were 0.84 and 0.81, respectively. For the 31 DIPG test sets, the AUCs were 0.91, 0.83, and 0.81, for the models incorporating the quantitative features, radiomic model (T2-weighted images, and T1W1-enhanced images), respectively. CONCLUSION: Combining T2-weighted quantification with qualitative and extrapontine volume ratios reliably predicted pediatric DIPG radiotherapy response. CLINICAL RELEVANCE STATEMENT: Combining T2-weighted quantification with qualitative and extrapontine volume ratios can accurately predict diffuse intrinsic pontine glioma (DIPG) radiotherapy response, which may facilitate personalized treatment and prognostic assessment for patients with DIPG. KEY POINTS: Early identification is crucial for radiotherapy response and risk stratification in diffuse intrinsic pontine glioma. The model using tumor heterogeneity and quantitative T2 signal metrics achieved an AUC of 0.91. Using a combination of parameters can effectively predict radiotherapy response in this population.

The relationship between imaging features, therapeutic response, and overall survival in pediatric diffuse intrinsic pontine glioma.

We aimed to evaluate the relationship between imaging features, therapeutic responses (comparative cross-product and volumetric measurements), and overall survival (OS) in pediatric diffuse intrinsic pontine glioma (DIPG). A total of 134 patients (≤ 18 years) diagnosed with DIPG were included. Univariate and multivariate analyses were performed to evaluate correlations of clinical and imaging features and therapeutic responses with OS. The correlation between cross-product (CP) and volume thresholds in partial response (PR) was evaluated by linear regression. The log-rank test was used to compare OS patients with discordant therapeutic response classifications and those with concordant classifications. In univariate analysis, characteristics related to worse OS included lower Karnofsky, larger extrapontine extension, ring-enhancement, necrosis, non-PR, and increased ring enhancement post-radiotherapy. In the multivariate analysis, Karnofsky, necrosis, extrapontine extension, and therapeutic response can predict OS. A 25% CP reduction (PR) correlated with a 32% volume reduction (R2 = 0.888). Eight patients had discordant therapeutic response classifications according to CP (25%) and volume (32%). This eight patients' median survival time was 13.0 months, significantly higher than that in the non-PR group (8.9 months), in which responses were consistently classified as non-PR based on CP (25%) and volume (32%). We identified correlations between imaging features, therapeutic responses, and OS; this information is crucial for future clinical trials. Tumor volume may represent the DIPG growth pattern more accurately than CP measurement and can be used to evaluate therapeutic response.

A prospective cohort-based artificial intelligence evaluation system for the protective efficacy and immune response of SARS-CoV-2 inactivated vaccines.

BACKGROUND: Novel coronaviruses constitute a significant health threat, prompting the adoption of vaccination as the primary preventive measure. However, current evaluations of immune response and vaccine efficacy are deemed inadequate. OBJECTIVES: The study sought to explore the evolving dynamics of immune response at various vaccination time points and during breakthrough infections. It aimed to elucidate the synergistic effects of epidemiological factors, humoral immunity, and cellular immunity. Additionally, regression curves were used to determine the correlation between the protective efficacy of the vaccine and the stimulated immune response. METHODS: Employing LASSO for high-dimensional data analysis, the study utilised four machine learning algorithms-logistical regression, random forest, LGBM classifier, and AdaBoost classifier-to comprehensively assess the immune response following booster vaccination. RESULTS: Neutralising antibody levels exhibited a rapid surge post-booster, escalating to 102.38 AU/mL at one week and peaking at 298.02 AU/mL at two weeks. Influential factors such as sex, age, disease history, and smoking status significantly impacted post-booster antibody levels. The study further constructed regression curves for neutralising antibodies, non-switched memory B cells, CD4+T cells, and CD8+T cells using LASSO combined with the random forest algorithm. CONCLUSION: The establishment of an artificial intelligence evaluation system emerges as pivotal for predicting breakthrough infection prognosis after the COVID-19 booster vaccination. This research underscores the intricate interplay between various components of immunity and external factors, elucidating key insights to enhance vaccine effectiveness. 3D modelling discerned distinctive interactions between humoral and cellular immunity within prognostic groups (Class 0-2). This underscores the critical role of the synergistic effect of humoral immunity, cellular immunity, and epidemiological factors in determining the protective efficacy of COVID-19 vaccines post-booster administration.

Development and validation of a nomogram to predict leptomeningeal metastases in lung adenocarcinoma: Cervical lymph node metastasis is an important association factor.

BACKGROUND: The goal of this study was to create a nomogram using routine parameters to predict leptomeningeal metastases (LMs) in advanced lung adenocarcinoma (LAC) patients to prevent needless exams or lumbar punctures and to assist in accurately diagnosing LMs. METHODS: Two hundred and seventy-three patients with LMs and brain metastases were retrospectively reviewed and divided into derivation (n = 191) and validation (n = 82) cohorts using a 3:7 random allocation. All LAC patients with LMs had positive cerebrospinal fluid cytology results and brain metastases confirmed by magnetic resonance imaging. Binary logistic regression with backward stepwise selection was used to identify significant characteristics. A predictive nomogram based on the logistic model was assessed through receiver operating characteristic curves. The validation cohort and Hosmer-Lemeshow test were used for internal validation of the nomogram. RESULTS: Five clinicopathological parameters, namely, gene mutations, surgery at the primary lung cancer site, clinical symptoms of the head, N stage, and therapeutic strategy, were used as predictors of LMs. The area under the curve was 0.946 (95% CI 0.912-0.979) for the training cohort and 0.861 (95% CI 0.761-0.961) for the internal validation cohort. There was no significant difference in performance between the two cohorts (p = 0.116). In the internal validation, calibration plots revealed that the nomogram predictions were well suited to the actual outcomes. CONCLUSIONS: We created a user-friendly nomogram to predict LMs in advanced lung cancer patients, which could help guide treatment decisions and reduce unnecessary lumbar punctures.

Exploring the potential of pretrained CNNs and time-frequency methods for accurate epileptic EEG classification: a comparative study.

Prompt diagnosis of epilepsy relies on accurate classification of automated electroencephalogram (EEG) signals. Several approaches have been developed to characterize epileptic EEG data; however, none of them have exploited time-frequency data to evaluate the effect of tweaking parameters in pretrained frameworks for EEG data classification. This study compares the performance of several pretrained convolutional neural networks (CNNs) namely, AlexNet, GoogLeNet, MobileNetV2, ResNet-18 and SqueezeNet for the localization of epilepsy EEG data using various time-frequency data representation algorithms. Continuous wavelet transform (CWT), empirical Fourier decomposition (EFD), empirical mode decomposition (EMD), empirical wavelet transform (EWT), and variational mode decomposition (VMD) were exploited for the acquisition of 2D scalograms from 1D data. The research evaluates the effect of multiple factors, including noisy versus denoised scalograms, different optimizers, learning rates, single versus dual channels, model size, and computational time consumption. The benchmark Bern-Barcelona EEG dataset is used for testing purpose. Results obtained show that the combination of MobileNetV2, Continuous Wavelet Transform (CWT) and Adam optimizer at a learning rate of 10-4, coupled with dual-data channels, provides the best performance metrics. Specifically, these parameters result in optimal sensitivity, specificity, f1-score, and classification accuracy, with respective values of 96.06%, 96.15%, 96.08%, and 96.10%. To further corroborate the efficacy of opted pretrained models on exploited Signal Decomposition (SD) algorithms, the classifiers are also being simulated on Temple University database at pinnacle modeling composition. A similar pattern in the outcome readily validate the findings of our study and robustness of deep learning models on epilepsy EEG scalograms.The conclusions drawn emphasize the potential of pretrained CNN-based models to create a robust, automated system for diagnosing epileptiform. Furthermore, the study offers insights into the effectiveness of varying time-frequency techniques and classifier parameters for classifying epileptic EEG data.

Genkwanin alleviates intervertebral disc degeneration via regulating ITGA2/PI3K/AKT pathway and inhibiting apoptosis and senescence.

Intervertebral disc degeneration (IVDD) is a progressive degenerative disease influenced by various factors. Genkwanin, a known anti-inflammatory flavonoid, has not been explored for its potential in IVDD management. This study aims to investigate the effects and mechanisms of genkwanin on IVDD. In vitro, cell experiments revealed that genkwanin dose-dependently inhibited Interleukin-1ß-induced expression levels of inflammatory factors (Interleukin-6, inducible nitric oxide synthase, cyclooxygenase-2) and degradation metabolic protein (matrix metalloproteinase-13). Concurrently, genkwanin upregulated the expression of synthetic metabolism genes (type II collagen, aggrecan). Moreover, genkwanin effectively reduced the phosphorylation of phosphatidylinositol 3-kinase (PI3K)/AKT/mammalian target of rapamycin, mitogen-activated protein kinase (MAPK), and nuclear factor-κB (NF-κB) pathways. Transcriptome sequencing analysis identified integrin α2 (ITGA2) as a potential target of genkwanin, and silencing ITGA2 reversed the activation of PI3K/AKT pathway induced by Interleukin-1ß. Furthermore, genkwanin alleviated Interleukin-1ß-induced senescence and apoptosis in nucleus pulposus cells. In vivo animal experiments demonstrated that genkwanin mitigated the progression of IVDD in the rat model through imaging and histological examinations. In conclusion, This study suggest that genkwanin inhibits inflammation in nucleus pulposus cells, promotes extracellular matrix remodeling, suppresses cellular senescence and apoptosis, through the ITGA2/PI3K/AKT, NF-κB and MAPK signaling pathways. These findings indicate that genkwanin may be a promising therapeutic candidate for IVDD.

Ascorbic acid biosynthesis in Pacific abalone Haliotis discus hannai Ino and L-gulonolactone oxidase gene loss as an independent event.

Up to now, it has been believed that invertebrates are unable to synthesize ascorbic acid (AA) in vivo. However, in the present study, the full-length CDs (Coding sequence) of L-gulonolactone oxidase (GLO) from Pacific abalone (Haliotis discus hannai Ino) were obtained through molecular cloning. The Pacific abalone GLO contained a FAD-binding domain in the N-termination, and ALO domain and conserved HWAK motif in the C-termination. The GLO gene possesses 12 exons and 11 introns. The Pacific abalone GLO was expressed in various tissues, including the kidney, digestive gland, gill, intestine, muscle and mantle. The GLO activity assay revealed that GLO activity was only detected in the kidney of Pacific abalone. After a 100-day feeding trial, dietary AA levels did not significantly affect the survival, weight gain, daily increment in shell length, and feed conversion ratio of Pacific abalone. The expression of GLO in the kidney was downregulated by dietary AA. These results implied that the ability to synthesize AA in abalone had not been lost. From the evolutionary perspective, the loss of GLO occurred independently as an independent event by matching with the genomes of various species. The positive selection analysis revealed that the GLO gene underwent purifying selective pressure during its evolution. In conclusion, the present study provided direct evidence to prove that the GLO activity and the ability to synthesize AA exist in abalone. The AA synthesis ability in vertebrates might have originated from invertebrates dating back 930.31 million years.

A prognostic model for SARS-CoV-2 breakthrough infection: Analyzing a prospective cellular immunity cohort.

BACKGROUND: Following the COVID-19 pandemic, studies have identified several prevalent characteristics, especially related to lymphocyte subsets. However, limited research is available on the focus of this study, namely, the specific memory cell subsets among individuals who received COVID-19 vaccine boosters and subsequently experienced a SARS-CoV-2 breakthrough infection. METHODS: Flow cytometry (FCM) was employed to investigate the early and longitudinal pattern changes of cellular immunity in patients with SARS-CoV-2 breakthrough infections following COVID-19 vaccine boosters. XGBoost (a machine learning algorithm) was employed to analyze cellular immunity prior to SARS-CoV-2 breakthrough, aiming to establish a prognostic model for SARS-CoV-2 breakthrough infections. RESULTS: Following SARS-CoV-2 breakthrough infection, naïve T cells and TEMRA subsets increased while the percentage of TCM and TEM cells decreased. Naïve and non-switched memory B cells increased while switched and double-negative memory B cells decreased. The XGBoost model achieved an area under the curve (AUC) of 0.78, with an accuracy rate of 81.8 %, a sensitivity of 75 %, and specificity of 85.7 %. TNF-α, CD27-CD19+cells, and TEMRA subsets were identified as high predictors. An increase in TNF-α, cTfh, double-negative memory B cells, IL-6, IL-10, and IFN-γ prior to SARS-CoV-2 infection was associated with enduring clinical symptoms; conversely, an increase in CD3+ T cells, CD4+ T cells, and IL-2 was associated with clinical with non-enduring clinical symptoms. CONCLUSION: SARS-CoV-2 breakthrough infection leads to disturbances in cellular immunity. Assessing cellular immunity prior to breakthrough infection serves as a valuable prognostic tool for SARS-CoV-2 infection, which facilitates clinical decision-making.

Distinguishing EGFR mutation molecular subtypes based on MRI radiomics features of lung adenocarcinoma brain metastases.

OBJECTIVE: To explore the feasibility of identifying epidermal growth factor receptor (EGFR) mutation molecular subtypes in primary lesions based on the radiomics features of lung adenocarcinoma brain metastases using magnetic resonance imaging (MRI). METHODS: We retrospectively analyzed clinical, imaging, and genetic testing data of patients with lung adenocarcinoma with EGFR gene mutations who had brain metastases. Three-dimensional radiomics features were extracted from contrast-enhanced T1-weighted images. The volume of interest was delineated and normalized using Z-score, dimensionality reduction was performed using principal component analysis, feature selection using Relief, and radiomics model construction using adaptive boosting as a classifier. Data were randomly divided into training and testing datasets at an 8:2 ratio. Five-fold cross-validation was conducted in the training set to select the optimal radiomics features and establish a predictive model for distinguishing between exon 19 deletion (19Del) and exon 21 L858R point mutation (21L858R), the two most common EGFR gene mutations. The testing set was used for external validation of the models. Model performance was evaluated using receiver operating characteristic curve and decision curve analyses. RESULTS: Overall, 86 patients with 228 brain metastases were included. Patient age was identified as an independent predictor for distinguishing between 19Del and 21L858R. The area under the curve (AUC) values of the radiomics model in the training and testing datasets were 0.895 (95% confidence interval [CI]: 0.850-0.939) and 0.759 (95% CI: 0.0.614-0.903), respectively. The AUC for diagnosis of all cases using a combined model of age and radiomics was 0.888 (95% CI: 0.846-0.930), slightly higher than that of the radiomics model alone (0.866, 95% CI: 0.820-0.913), but without statistical significance (p=0.1626). In the decision curve analysis, both models demonstrated clinical net benefits. CONCLUSIONS: The radiomics model based on MRI of lung adenocarcinoma brain metastases could distinguish between EGFR 19Del and 21L858R mutations in the primary lesion.

RSPSSL: A novel high-fidelity Raman spectral preprocessing scheme to enhance biomedical applications and chemical resolution visualization.

Raman spectroscopy has tremendous potential for material analysis with its molecular fingerprinting capability in many branches of science and technology. It is also an emerging omics technique for metabolic profiling to shape precision medicine. However, precisely attributing vibration peaks coupled with specific environmental, instrumental, and specimen noise is problematic. Intelligent Raman spectral preprocessing to remove statistical bias noise and sample-related errors should provide a powerful tool for valuable information extraction. Here, we propose a novel Raman spectral preprocessing scheme based on self-supervised learning (RSPSSL) with high capacity and spectral fidelity. It can preprocess arbitrary Raman spectra without further training at a speed of ~1 900 spectra per second without human interference. The experimental data preprocessing trial demonstrated its excellent capacity and signal fidelity with an 88% reduction in root mean square error and a 60% reduction in infinite norm ([Formula: see text]) compared to established techniques. With this advantage, it remarkably enhanced various biomedical applications with a 400% accuracy elevation (ΔAUC) in cancer diagnosis, an average 38% (few-shot) and 242% accuracy improvement in paraquat concentration prediction, and unsealed the chemical resolution of biomedical hyperspectral images, especially in the spectral fingerprint region. It precisely preprocessed various Raman spectra from different spectroscopy devices, laboratories, and diverse applications. This scheme will enable biomedical mechanism screening with the label-free volumetric molecular imaging tool on organism and disease metabolomics profiling with a scenario of high throughput, cross-device, various analyte complexity, and diverse applications.

Self-supervised Self2Self denoising strategy for OCT speckle reduction with a single noisy image.

Optical coherence tomography (OCT) inevitably suffers from the influence of speckles originating from multiple scattered photons owing to its low-coherence interferometry property. Although various deep learning schemes have been proposed for OCT despeckling, they typically suffer from the requirement for ground-truth images, which are difficult to collect in clinical practice. To alleviate the influences of speckles without requiring ground-truth images, this paper presents a self-supervised deep learning scheme, namely, Self2Self strategy (S2Snet), for OCT despeckling using a single noisy image. Specifically, in this study, the main deep learning architecture is the Self2Self network, with its partial convolution being updated with a gated convolution layer. Specifically, both the input images and their Bernoulli sampling instances are adopted as network input first, and then, a devised loss function is integrated into the network to remove the background noise. Finally, the denoised output is estimated using the average of multiple predicted outputs. Experiments with various OCT datasets are conducted to verify the effectiveness of the proposed S2Snet scheme. Results compared with those of the existing methods demonstrate that S2Snet not only outperforms those existing self-supervised deep learning methods but also achieves better performances than those non-deep learning ones in different cases. Specifically, S2Snet achieves an improvement of 3.41% and 2.37% for PSNR and SSIM, respectively, as compared to the original Self2Self network, while such improvements become 19.9% and 22.7% as compared with the well-known non-deep learning NWSR method.