Flexible wearable sensors for crop monitoring: a review.

Crops were the main source of human food, which have met the increasingly diversified demand of consumers. Sensors were used to monitor crop phenotypes and environmental information in real time, which will provide a theoretical reference for optimizing crop growth environment, resisting biotic and abiotic stresses, and improve crop yield. Compared with non-contact monitoring methods such as optical imaging and remote sensing, wearable sensing technology had higher time and spatial resolution. However, the existing crop sensors were mainly rigid mechanical structures, which were easy to cause damage to crop organs, and there were still challenges in terms of accuracy and biosafety. Emerging flexible sensors had attracted wide attention in the field of crop phenotype monitoring due to their excellent mechanical properties and biocompatibility. The article introduced the key technologies involved in the preparation of flexible wearable sensors from the aspects of flexible preparation materials and advanced preparation processes. The monitoring function of flexible sensors in crop growth was highlighted, including the monitoring of crop nutrient, physiological, ecological and growth environment information. The monitoring principle, performance together with pros and cons of each sensor were analyzed. Furthermore, the future opportunities and challenges of flexible wearable devices in crop monitoring were discussed in detail from the aspects of new sensing theory, sensing materials, sensing structures, wireless power supply technology and agricultural sensor network, which will provide reference for smart agricultural management system based on crop flexible sensors, and realize efficient management of agricultural production and resources.

Exploring potential neuroimaging biomarkers for the response to non-steroidal anti-inflammatory drugs in episodic migraine.

BACKGROUND: Non-steroidal anti-inflammatory drugs (NSAIDs) are considered first-line medications for acute migraine attacks. However, the response exhibits considerable variability among individuals. Thus, this study aimed to explore a machine learning model based on the percentage of amplitude oscillations (PerAF) and gray matter volume (GMV) to predict the response to NSAIDs in migraine treatment. METHODS: Propensity score matching was adopted to match patients having migraine with response and nonresponse to NSAIDs, ensuring consistency in clinical characteristics and migraine-related features. Multimodal magnetic resonance imaging was employed to extract PerAF and GMV, followed by feature selection using the least absolute shrinkage and selection operator regression and recursive feature elimination algorithms. Multiple predictive models were constructed and the final model with the smallest predictive residuals was chosen. The model performance was evaluated using the area under the receiver operating characteristic (ROCAUC) curve, area under the precision-recall curve (PRAUC), balance accuracy (BACC), sensitivity, F1 score, positive predictive value (PPV), and negative predictive value (NPV). External validation was performed using a public database. Then, correlation analysis was performed between the neuroimaging predictors and clinical features in migraine. RESULTS: One hundred eighteen patients with migraine (59 responders and 59 non-responders) were enrolled. Six features (PerAF of left insula and left transverse temporal gyrus; and GMV of right superior frontal gyrus, left postcentral gyrus, right postcentral gyrus, and left precuneus) were observed. The random forest model with the lowest predictive residuals was selected and model metrics (ROCAUC, PRAUC, BACC, sensitivity, F1 score, PPV, and NPV) in the training and testing groups were 0.982, 0.983, 0.927, 0.976, 0.930, 0.889, and 0.973; and 0.711, 0.648, 0.639, 0.667,0.649, 0.632, and 0.647, respectively. The model metrics of external validation were 0.631, 0.651, 0.611, 0.808, 0.656, 0.553, and 0.706. Additionally, a significant positive correlation was found between the GMV of the left precuneus and attack time in non-responders. CONCLUSIONS: Our findings suggest the potential of multimodal neuroimaging features in predicting the efficacy of NSAIDs in migraine treatment and provide novel insights into the neural mechanisms underlying migraine and its optimized treatment strategy.

Neutrophil extracellular traps promote invasion and metastasis via NLRP3-mediated oral squamous cell carcinoma pyroptosis inhibition.

Neutrophil extracellular traps (NETs) are reticular structures composed of neutrophil elastase (NE), cathepsin G (CG) and DNA-histone enzyme complexes. Accumulating evidence has revealed that NETs play important roles in tumor progression, metastasis, and thrombosis. However, our understanding of its clinical value and mechanism of action in oral squamous cell carcinoma (OSCC) is limited and has not yet been systematically described. Here, we aimed to investigate the clinical significance of NETs in OSCC and the mechanisms by which they affect its invasive and metastatic capacity. Our results demonstrated that high enrichment of NETs is associated with poor prognosis in OSCC, and mechanistic studies have shown that NE in NETs promotes invasion and metastasis via NLRP3-mediated inhibition of pyroptosis in OSCC. These findings may provide a new therapeutic approach for OSCC.

BiU-net: A dual-branch structure based on two-stage fusion strategy for biomedical image segmentation.

BACKGROUND AND OBJECTIVE: Computer-based biomedical image segmentation plays a crucial role in planning of assisted diagnostics and therapy. However, due to the variable size and irregular shape of the segmentation target, it is still a challenge to construct an effective medical image segmentation structure. Recently, hybrid architectures based on convolutional neural networks (CNNs) and transformers were proposed. However, most current backbones directly replace one or all convolutional layers with transformer blocks, regardless of the semantic gap between features. Thus, how to sufficiently and effectively eliminate the semantic gap as well as combine the global and local information is a critical challenge. METHODS: To address the challenge, we propose a novel structure, called BiU-Net, which integrates CNNs and transformers with a two-stage fusion strategy. In the first fusion stage, called Single-Scale Fusion (SSF) stage, the encoding layers of the CNNs and transformers are coupled, with both having the same feature map size. The SSF stage aims to reconstruct local features based on CNNs and long-range information based on transformers in each encoding block. In the second stage, Multi-Scale Fusion (MSF), BiU-Net interacts with multi-scale features from various encoding layers to eliminate the semantic gap between deep and shallow layers. Furthermore, a Context-Aware Block (CAB) is embedded in the bottleneck to reinforce multi-scale features in the decoder. RESULTS: Experiments on four public datasets were conducted. On the BUSI dataset, our BiU-Net achieved 85.50 % on Dice coefficient (Dice), 76.73 % on intersection over union (IoU), and 97.23 % on accuracy (ACC). Compared to the state-of-the-art method, BiU-Net improves Dice by 1.17 %. For the Monuseg dataset, the proposed method attained the highest scores, reaching 80.27 % and 67.22 % for Dice and IoU. The BiU-Net achieves 95.33 % and 81.22 % Dice on the PH2 and DRIVE datasets. CONCLUSIONS: The results of our experiments showed that BiU-Net transcends existing state-of-the-art methods on four publicly available biomedical datasets. Due to the powerful multi-scale feature extraction ability, our proposed BiU-Net is a versatile medical image segmentation framework for various types of medical images. The source code is released on (https://github.com/ZYLandy/BiU-Net).

Metabolic profile of follicular fluid in patients with ovarian endometriosis undergoing IVF: a pilot study.

RESEARCH QUESTION: What are the metabolic characteristics of follicular fluid in patients with ovarian endometriosis undergoing IVF? DESIGN: This was an exploratory cohort study on endometriosis. In total, 19 infertile patients with ovarian endometriosis diagnosed by laparoscopy, and 23 controls matched in terms of age and body mass index (women with infertility due to male or tubal factors) were enrolled in this study. All patients underwent IVF treatment with a gonadotrophin-releasing hormone antagonist protocol, and follicular fluid was collected at oocyte retrieval. The metabolomics of follicular fluid samples was analysed using an ultra-high-performance liquid chromatography Orbitrap Exploris mass spectrometer (UHPLC-OE-MS). The best combination of biomarkers was selected by performing stepwise logistic regression analysis with backward elimination. RESULTS: Fifteen metabolites were identified as biomarkers associated with endometriosis. A final model containing 8-hydroxy-2-deoxyguanosine, biotin, n-acetyl-L-methionine and n-methylnicotinamide was constructed. Receiver operating characteristic analysis confirmed the value of these parameters in diagnosing endometriosis, with sensitivity of 94.7% and specificity of 95.7%. Enrichment analysis via the Kyoto Encyclopedia of Genes and Genome showed that 15 metabolites were enriched in eight metabolic pathways. CONCLUSION: Metabolomics based on UHPLC-OE-MS effectively characterized the metabolomics analysis of follicular fluid in patients with ovarian endometriosis. These findings may provide a new basis for better understanding of how diseases progress, and for the discovery of new biomarkers.

Association analysis between an epigenetic risk score and blood pressure.

Background: Epigenome-wide association studies have revealed multiple DNA methylation sites (CpGs) associated with alcohol consumption, an important lifestyle risk factor for cardiovascular diseases. Results: We generated an alcohol consumption epigenetic risk score (ERS) based on previously reported 144 alcohol-associated CpGs and examined the association of the ERS with systolic blood pressure (SBP), diastolic blood pressure (DBP), and hypertension (HTN) in 3,898 Framingham Heart Study (FHS) participants. We found an association of alcohol intake with the ERS in the meta-analysis with 0.09 units higher ERS per drink consumed per day (p < 0.0001). Cross-sectional analyses in FHS revealed that a one-unit increment of the ERS was associated with 1.93 mm Hg higher SBP (p = 4.64E-07), 0.68 mm Hg higher DBP (p = 0.006), and an odds ratio of 1.78 for HTN (p < 2E-16). Meta-analysis of the cross-sectional association of the ERS with BP traits in eight independent external cohorts (n = 11,544) showed similar relationships with blood pressure levels, i.e., a one-unit increase in ERS was associated with 0.74 (p = 0.002) and 0.50 (p = 0.0006) mm Hg higher SBP and DBP, but could not confirm the association with hypertension. Longitudinal analyses in FHS (n = 3,260) and five independent external cohorts (n = 4,021) showed that the baseline ERS was not associated with a change in blood pressure over time or with incident HTN. Conclusions: Our findings provide proof-of-concept that utilizing an ERS is a useful approach to capture the recent health consequences of lifestyle behaviors such as alcohol consumption.

Identification of immunogenic cell death-related damage-related molecular patterns (DAMPs) to predict outcomes in patients with head and neck squamous cell carcinoma.

PURPOSE: Head and neck cancer is the sixth most common type of cancer worldwide, wherein the immune responses are closely associated with disease occurrence, development, and prognosis. Investigation of the role of immunogenic cell death-related genes (ICDGs) in adaptive immune response activation may provide cues into the mechanism underlying the outcome of HNSCC immunotherapy. METHODS: ICDGs expression patterns in HNSCC were analyzed, after which consensus clustering in HNSCC cohort conducted. A 4-gene prognostic model was constructed through LASSO and Cox regression analyses to analyze the prognostic index using the TCGA dataset, followed by validation with two GEO datasets. The distribution of immune cells and the response to immunotherapy were compared between different risk subtypes through multiple algorithms. Moreover, immunohistochemical (IHC) analyses were conducted to validate the prognostic value of HSP90AA1 as a predictor of HNSCC patient prognosis. In vitro assays were performed to further detect the effect of HSP90AA1 in the development of HNSCC. RESULTS: A novel prognostic index based on four ICDGs was constructed and proved to be useful as an independent factor of HNSCC prognosis. The risk score derived from this model grouped patients into high- and low-risk subtypes, wherein the high-risk subtype had worse survival outcomes and poorer immunotherapy response. IHC analysis validated the applicability of HSP90AA1 as a predictor of prognosis of HNSCC patients. HSP90AA1 expression in tumor cells promotes the progression of HNSCC. CONCLUSIONS: Together, these results highlight a novel four-gene prognostic signature as a valuable tool to assess survival status and prognosis of HNSCC patients.

Hyperspectral imaging combined with GA-SVM for maize variety identification.

The demand for identification of maize varieties has increased dramatically due to the phenomenon of mixed seeds and inferior varieties pretending to be high-quality varieties continuing to occur. It is urgent to solve the problem of efficient and accurate identification of maize varieties. A hyperspectral image acquisition system was used to acquire images of maize seeds. Regions of interest (ROI) with an embryo size of 10 × 10 pixel were extracted, and the average spectral information in the range of 949.43-1709.49 nm was intercepted for the subsequent study in order to eliminate random noise at both ends. Savitzky-Golay (SG) smoothing algorithm and multiple scattering correction (MSC) were used to pretreat the full-band spectrum. The feature wavelengths were screened by successive projection algorithms (SPA), competitive adaptive reweighted sampling (CARS) single screening, and two combinations of CARS-SPA and CARS + SPA, respectively. Support vector machines (SVMs) and models optimized based on genetic algorithm (GA), particle swarm optimization (PSO) were established by using full bands (FB) and feature bands as the model input. The results showed that the MSC-(CARS-SPA)-GA-SVM model had the best performance with 93.00% of the test set accuracy, 8 feature variables, and a running time of 24.45 s. MSC pretreatment can effectively eliminate the scattering effect of spectral data, and the feature wavelengths extracted by CARS-SPA can represent all wavelength information. The study proved that hyperspectral imaging combined with GA-SVM can realize the identification of maize varieties, which provided a theoretical basis for maize variety classification and authenticity identification.

Development of a cell adhesion-based prognostic model for multiple myeloma: Insights into chemotherapy response and potential reversal of adhesion effects.

Multiple myeloma (MM) is a hematologic malignancy notorious for its high relapse rate and development of drug resistance, in which cell adhesion-mediated drug resistance plays a critical role. This study integrated four RNA sequencing datasets (CoMMpass, GSE136337, GSE9782, and GSE2658) and focused on analyzing 1706 adhesion-related genes. Rigorous univariate Cox regression analysis identified 18 key prognosis-related genes, including KIF14, TROAP, FLNA, MSN, LGALS1, PECAM1, and ALCAM, which demonstrated the strongest associations with poor overall survival (OS) in MM patients. To comprehensively evaluate the impact of cell adhesion on MM prognosis, an adhesion-related risk score (ARRS) model was constructed using Lasso Cox regression analysis. The ARRS model emerged as an independent prognostic factor for predicting OS. Furthermore, our findings revealed that a heightened cell adhesion effect correlated with tumor resistance to DNA-damaging drugs, protein kinase inhibitors, and drugs targeting the PI3K/Akt/mTOR signaling pathway. Nevertheless, we identified promising drug candidates, such as tirofiban, pirenzepine, erlotinib, and bosutinib, which exhibit potential in reversing this resistance. In vitro, experiments employing NCIH929, RPMI8226, and AMO1 cell lines confirmed that MM cell lines with high ARRS exhibited poor sensitivity to the aforementioned candidate drugs. By employing siRNA-mediated knockdown of the key ARRS model gene KIF14, we observed suppressed proliferation of NCIH929 cells, along with decreased adhesion to BMSCs and fibronectin. This study presents compelling evidence establishing cell adhesion as a significant prognostic factor in MM. Additionally, potential molecular mechanisms underlying adhesion-related resistance are proposed, along with viable strategies to overcome such resistance. These findings provide a solid scientific foundation for facilitating clinically stratified treatment of MM.

Simultaneous Detection of Ochratoxin A and Aflatoxin B1 Based on Stable Tuning Fork-shaped DNA Fluorescent Aptasensor.

Tuning fork, consisting of two fork arms and a fork handle, has a stable and rigid structure. Inspired by this structure, a tuning fork-shaped DNA (TF-DNA) fluorescence aptasensor was constructed to detect ochratoxin A (OTA) and aflatoxin B1 (AFB1). A TF-DNA double-stranded structure capable of attaching both OTA aptamer labeled with the FAM fluorescent group (FAM-Apt) and AFB1 aptamer labeled with the ROX fluorescent group (ROX-Apt) was designed and linked to magnetic beads. This TF-DNA double-stranded structure can provide a stable platform for dual-target detection. In the presence of OTA and AFB1, FAM-Apt and ROX-Apt preferentially bound to them and detached from the TF-DNA double-stranded structure. Dual-signal fluorescent probes were collected from the supernatant by magnetic separation, and achieved fluorescence enhancement at 520 nm and 607 nm, respectively. The linear ranges are 0.05 ng/mL to 100 ng/mL for OTA and 0.1 ng/mL to 100 ng/mL for AFB1, and the detection limits are 0.015 ng/mL and 0.045 ng/mL, respectively. The developed sensor has the advantages of simple and fast preparation, good specificity and reproducibility, which is promising for the simultaneous determination of multiple hazardous substances in food.

[Association of polymorphisms in SEC16B, DNAJC27, FTO and MC4R genes with overweight and obesity in Han preschool children].

OBJECTIVE: To investigate the association of polymorphisms in SEC16B rs633715, DNAJC27 rs713586, FTO rs11642015 and MC4R rs6567160 with overweight and obesity in Han Chinese preschool children. METHODS: A total of 749 Han Chinese preschool children from Henan and Guizhou Province of Long-term Health Effects Assessment Project of Infants and Toddlers Nutritional Pack were selected for the study and divided into an overweight and obese group and a normal control group in 2022. rs633715, rs713586, rs11642015 and rs6567160 were genotyped using Kompetitive allele-specific PCR(KASP) technology. The distribution of genotypic polymorphisms was compared using the χ~2 test. The association between the four loci and overweight and obesity in preschool children was analyzed using a multifactorial logistic regression model. RESULTS: The statistical analysis revealed a significant disparity(P<0.05) in the distribution of genotypic polymorphisms of rs633715 and rs6567160 among preschoolers in Henan and Guizhou Province. CC heterozygous mutant and recessive models at rs633715 locus were associated with susceptibility to overweight and obesity in preschool children [OR and 95% CI 2.915(1.163-7.305), and 2.997(1.226-7.323), respectively, both P<0.05]. TC heterozygous mutant and dominant models at rs713586 locus were also associated susceptibility to overweight and obesity in preschool children(OR and 95% CI were 2.362(1.054-5.289)and 2.362(1.054-5.289), respectively, both P<0.05). rs11642015 and rs6567160 loci were not associated with susceptibility to overweight and obesity in preschool children(P>0.05). The result of the analysis of the cumulative effect of rs633715 and rs713586 showed that the number of genotypes carrying the risk genotype was positively associated with the risk of overweight and obesity in preschool children(P_(trend)<0.01). CONCLUSION: Among Han Chinese preschool children, SEC16B rs633715 and DNAJC27 rs713586 were associated with susceptibility to overweight and obesity in preschool children. Moreover, rs633715 and rs713586 had a cumulative effect on susceptibility to overweight and obesity in preschool children, the number of risk genotypes carried was positively associated with childhood overweight and obesity risk.

Enhancing selection of alcohol consumption-associated genes by random forest.

Machine learning methods have been used in identifying omics markers for a variety of phenotypes. We aimed to examine whether a supervised machine learning algorithm can improve identification of alcohol-associated transcriptomic markers. In this study, we analysed array-based, whole-blood derived expression data for 17 873 gene transcripts in 5508 Framingham Heart Study participants. By using the Boruta algorithm, a supervised random forest (RF)-based feature selection method, we selected twenty-five alcohol-associated transcripts. In a testing set (30 % of entire study participants), AUC (area under the receiver operating characteristics curve) of these twenty-five transcripts were 0·73, 0·69 and 0·66 for non-drinkers v. moderate drinkers, non-drinkers v. heavy drinkers and moderate drinkers v. heavy drinkers, respectively. The AUC of the selected transcripts by the Boruta method were comparable to those identified using conventional linear regression models, for example, AUC of 1958 transcripts identified by conventional linear regression models (false discovery rate < 0·2) were 0·74, 0·66 and 0·65, respectively. With Bonferroni correction for the twenty-five Boruta method-selected transcripts and three CVD risk factors (i.e. at P < 6·7e-4), we observed thirteen transcripts were associated with obesity, three transcripts with type 2 diabetes and one transcript with hypertension. For example, we observed that alcohol consumption was inversely associated with the expression of DOCK4, IL4R, and SORT1, and DOCK4 and SORT1 were positively associated with obesity, and IL4R was inversely associated with hypertension. In conclusion, using a supervised machine learning method, the RF-based Boruta algorithm, we identified novel alcohol-associated gene transcripts.

Functional near-infrared spectroscopy-based diagnosis support system for distinguishing between mild and severe depression using machine learning approaches.

Significance: Early diagnosis of depression is crucial for effective treatment. Our study utilizes functional near-infrared spectroscopy (fNIRS) and machine learning to accurately classify mild and severe depression, providing an objective auxiliary diagnostic tool for mental health workers. Aim: Develop prediction models to distinguish between severe and mild depression using fNIRS data. Approach: We collected the fNIRS data from 140 subjects and applied a complete ensemble empirical mode decomposition with an adaptive noise-wavelet threshold combined denoising method (CEEMDAN-WPT) to remove noise during the verbal fluency task. The temporal features (TF) and correlation features (CF) from 18 prefrontal lobe channels of subjects were extracted as predictors. Using recursive feature elimination with cross-validation, we identified optimal TF or CF and examined their role in distinguishing between severe and mild depression. Machine learning algorithms were used for classification. Results: The combination of TF and CF as inputs for the prediction model yielded higher classification accuracy than using either TF or CF alone. Among the prediction models, the SVM-based model demonstrates excellent performance in nested cross-validation, achieving an accuracy rate of 92.8%. Conclusions: The proposed model can effectively distinguish mild depression from severe depression.

Molecularly Imprinted Electrochemical Sensor Based on α-Cyclodextrin Inclusion Complex and MXene Modification for Highly Sensitive and Selective Detection of Alkylresorcinols in Whole Wheat Foods.

Authenticating whole wheat foods poses a significant challenge for both the grain industry and consumers. Alkylresorcinols (ARs), serving as biomarkers of whole wheat, play a crucial role in assessing the authenticity of whole wheat foods. Herein, we introduce a novel molecularly imprinted electrochemical sensor with modifications involving a molecularly imprinted polymer (MIP) and MXene nanosheets, enabling highly sensitive and selective detection of ARs. Notably, we specifically chose 5-heneicosylresorcinol (AR21), the predominant homologue in whole wheat, as the template molecule. α-Cyclodextrin and acrylamide served as dual functional monomers, establishing a robust multiple interaction between the MIP and AR21. As a result, the sensor exhibited a wide linear range of 0.005 to 100 µg·mL-1 and a low detection limit of 2.52 ng·mL-1, demonstrating exceptional selectivity and stability. When applied to commercial whole wheat foods, the assay achieved satisfactory recoveries and accuracy, strongly validating the practicality and effectiveness of this analytical technique.

SALL4 in gastrointestinal tract cancers: upstream and downstream regulatory mechanisms.

Effective therapeutic targets and early diagnosis are major challenges in the treatment of gastrointestinal tract (GIT) cancers. SALL4 is a well-known transcription factor that is involved in organogenesis during embryonic development. Previous studies have revealed that SALL4 regulates cell proliferation, survival, and migration and maintains stem cell function in mature cells. Additionally, SALL4 overexpression is associated with tumorigenesis. Despite its characterization as a biomarker in various cancers, the role of SALL4 in GIT cancers and the underlying mechanisms are unclear. We describe the functions of SALL4 in GIT cancers and discuss its upstream/downstream genes and pathways associated with each cancer. We also consider the possibility of targeting these genes or pathways as potential therapeutic options for GIT cancers.

pH-Responsive Co-Assembled Peptide Hydrogel to Inhibit Drug-Resistant Bacterial Infection and Promote Wound Healing.

Drug-resistant bacterial infection and biofilm formation are the key inhibitors of wound healing, and new strategies are urgently needed to address these issues. In this study, we designed a pH-responsive co-assembled peptide hydrogel to inhibit Methicillin-resistant Staphylococcus aureus (MRSA) infection and promote wound healing. We synthesized a cationic short peptide (Nap-FFKKK) and a co-assembled hydrogel with curcumin at pH ∼ 7.8. The loaded curcumin was continuously released in a weak acid environment (pH ∼ 5.5). The lysine-rich cationic peptide inhibited biofilm formation in MRSA via electrostatic interaction with the negatively charged bacterial cell surface and, thus, provided a reinforcing antibacterial effect with curcumin. In vitro antibacterial experiments showed that the co-assembled system considerably reduced the minimum inhibitory concentration of curcumin against MRSA by 10-fold and promoted wound healing in a mouse model of MRSA-infected wounds. This study provides a simple and promising strategy to treat drug-resistant bacterial infections in wounds.

Comparing visible and near infrared 'point' spectroscopy and hyperspectral imaging techniques to visualize the variability of apple firmness.

In this work, visible and near-infrared 'point' (Vis-NIR) spectroscopy and hyperspectral imaging (Vis-NIR-HSI) techniques were applied on three different apple cultivars to compare their firmness prediction performances based on a large intra-variability of individual fruit, and develop rapid and simple models to visualize the variability of apple firmness on three apple cultivars. Apples with high degree of intra-variability can strongly affect the prediction model performances. The apple firmness prediction accuracy can be improved based on the large intra-variability samples with the coefficient variation (CV) values over 10%. The least squares-support vector machine (LS-SVM) models based on Vis-NIR-HSI spectra had better performances for firmness prediction than that of Vis-NIR spectroscopy, with the with the Rc2 over 0.84. Finally, The Vis-NIR-HSI technique combined with least squares-support vector machine (LS-SVM) models were successfully applied to visualize the spatial the variability of apple firmness.

Boosting the immunogenicity of the CoronaVac SARS-CoV-2 inactivated vaccine with Huoxiang Suling Shuanghua Decoction: a randomized, double-blind, placebo-controlled study.

Introduction: In light of the public health burden of the COVID-19 pandemic, boosting the safety and immunogenicity of COVID-19 vaccines is of great concern. Numerous Traditional Chinese medicine (TCM) preparations have shown to beneficially modulate immunity. Based on pilot experiments in mice that showed that supplementation with Huoxiang Suling Shuanghua Decoction (HSSD) significantly enhances serum anti-RBD IgG titers after inoculation with recombinant SARS-CoV-2 S-RBD protein, we conducted this randomized, double-blind, placebo-controlled clinical trial aimed to evaluate the potential immunogenicity boosting effect of oral HSSD after a third homologous immunization with Sinovac's CoronaVac SARS-CoV-2 (CVS) inactivated vaccine. Methods: A total of 70 participants were randomly assigned (1:1 ratio) to receive a third dose of CVS vaccination and either oral placebo or oral HSSD for 7 days. Safety aspects were assessed by recording local and systemic adverse events, and by blood and urine biochemistry and liver and kidney function tests. Main outcomes evaluated included serum anti-RBD IgG titer, T lymphocyte subsets, serum IgG and IgM levels, complement components (C3 and C4), and serum cytokines (IL-6 and IFN-γ). In addition, metabolomics technology was used to analyze differential metabolite expression after supplementation with HSSD. Results: Following a third CVS vaccination, significantly increased serum anti-RBD IgG titer, reduced serum IL-6 levels, increased serum IgG, IgM, and C3 and C4 levels, and improved cellular immunity, evidenced by reduce balance deviations in the distribution of lymphocyte subsets, was observed in the HSSD group compared with the placebo group. No serious adverse events were recorded in either group. Serum metabolomics results suggested that the mechanisms by which HSSD boosted the immunogenicity of the CVS vaccine are related to differential regulation of purine metabolism, vitamin B6 metabolism, folate biosynthesis, arginine and proline metabolism, and steroid hormone biosynthesis. Conclusion: Oral HSSD boosts the immunogenicity of the CVS vaccine in young and adult individuals. This trial provides clinical reference for evaluation of TCM immunomodulators to improve the immune response to COVID-19 vaccines.

Gut microbiota and female health.

The gut microbiota is recognized as an endocrine organ with the capacity to influence distant organs and associated biological pathways. Recent advancements underscore the critical role of gut microbial homeostasis in female health; with dysbiosis potentially leading to diseases among women such as polycystic ovarian syndrome, endometriosis, breast cancer, cervical cancer, and ovarian cancer etc. Despite this, there has been limited discussion on the underlying mechanisms. This editorial explores the three potential mechanisms through which gut microbiota dysbiosis may impact the development of diseases among women, namely, the immune system, the gut microbiota-estrogen axis, and the metabolite pathway. We focused on approaches for treating diseases in women by addressing gut microbiota imbalances through probiotics, prebiotics supplementation, and fecal microbiota transplantation (FMT). Future studies should focus on determining the molecular mechanisms underlying associations between dysbiosis of gut microbiota and female diseases to realize precision medicine, with FMT emerging as a promising intervention.

Adsorption properties and mechanism of Cu(II) on virgin and aged microplastics in the aquatic environment.

Microplastics (MPs) migrate by adsorbing heavy metals in aquatic environments and act as their carriers. However, the aging mechanisms of MPs in the environment and the interactions between MPs and heavy metals in aquatic environments require further study. In this study, two kinds of materials, polyamide (PA) and polylactic acid (PLA) were used as target MPs, and the effects of UV irradiation on the physical and chemical properties of the MPs and the adsorption behavior of Cu(II) were investigated. The results showed that after UV irradiation, pits, folds and pores appeared on the surface of aged MPs, the specific surface area (SSA) increased, the content of oxygen-containing functional groups increased, and the crystallinity decreased. These changes enhanced the adsorption capacity of aged MPs for Cu(II) pollutants. The adsorption behavior of the PA and PLA MPs for Cu(II) conformed to the pseudo-second-order model and Langmuir isotherm model, indicating that the monolayer chemical adsorption was dominant. The maximum amounts of aged PA and PLA reached 1.415 and 1.398 mg/g, respectively, which were 1.59 and 1.76 times of virgin MPs, respectively. The effects of pH and salinity on the adsorption of Cu(II) by the MPs were significant. Moreover, factors such as pH, salinity and dosage had significant effects on the adsorption of Cu(II) by MPs. Oxidative complexation between the oxygen-containing groups of the MPs and Cu(II) is an important adsorption mechanism. These findings reveal that the UV irradiation aging of MPs can enhance the adsorption of Cu(II) and increase their role as pollutant carriers, which is crucial for assessing the ecological risk of MPs and heavy metals coexisting in aquatic environments.