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On soil type calcic chernozem, in the period 2018-2022. an experiment with common winter wheat (Triticum aestivum L.) variety \"Venka 1\" was carried out. The experiment was set up in 4 replications in a randomized block design. The size of the experimental plot is 10 m-2. In October, the sowing was carried out after the predecessor sugar beet (Beta vulgaris L.), with 500 germinating seeds/m-2. Treatments in 4 variants are applied in the appropriate periods according to Maria Thun's calendar. The first variant is an untreated Control; the second was treated only with biodynamic preparations: VD (biodynamic preparation) 500 + VD 501 + Fladen preparation; The third option is treated only with biological preparations: Free N + Heliosulfur, and the treatment in the fourth option is a combination of the above two options: VD (biodynamic preparation) 500 + VD 501 + Flaten preparation + Free N + Heliosulfur. Growth phases indicated are according to the Zadoks scale. Data were processed using SPSS using the method of variance statistics. Student's criterion was used for assessment. It was found that wheat treated variants performed better in terms of some biochemical parameters.

Blood biochemical indicators play a crucial role in assessing an individual’s overall health status and metabolic function. In this study, we measured five blood biochemical indicators, including total cholesterol (CHOL), low-density lipoprotein cholesterol (LDL-CH), triglycerides (TG), high-density lipoprotein cholesterol (HDL-CH), and blood glucose (BG), as well as 19 growth traits of 206 male chickens. By integrating host whole-genome information and 16S rRNA sequencing of the duodenum, jejunum, ileum, cecum, and feces microbiota, we assessed the contributions of host genetics and gut microbiota to blood biochemical indicators and their interrelationships. Our results demonstrated significant negative phenotypic and genetic correlations (r =  − 0.20 ~  − 0.67) between CHOL and LDL-CH with growth traits such as body weight, abdominal fat content, muscle content, and shin circumference. The results of heritability and microbiability indicated that blood biochemical indicators were jointly regulated by host genetics and gut microbiota. Notably, the heritability of HDL-CH was estimated to be 0.24, while the jejunal microbiability for BG and TG reached 0.45 and 0.23. Furthermore, by conducting genome-wide association study (GWAS) with the single-nucleotide polymorphism (SNPs), insertion/deletion (indels), and structural variation (SV), we identified RAP2C, member of the RAS oncogene family (RAP2C), dedicator of cytokinesis 11 (DOCK11), neurotensin (NTS) and BOP1 ribosomal biogenesis factor (BOP1) as regulators of HDL-CH, and glycerophosphodiester phosphodiesterase domain containing 5 (GDPD5), dihydrodiol dehydrogenase (DHDH), and potassium voltage-gated channel interacting protein 1 (KCNIP1) as candidate genes of BG. Moreover, our findings suggest that cecal RF39 and Clostridia_UCG_014 may be linked to the regulation of CHOL, and jejunal Streptococcaceae may be involved in the regulation of TG. Additionally, microbial GWAS results indicated that the presence of gut microbiota was under host genetic regulation. Our findings provide valuable insights into the complex interaction between host genetics and microbiota in shaping the blood biochemical profile of chickens.Key points• Multiple candidate genes were identified for the regulation of CHOL, HDL-CH, and BG.• RF39, Clostridia_UCG_014, and Streptococcaceae were implicated in CHOL and TG modulation.• The composition of gut microbiota is influenced by host genetics.

This study aimed to develop a comprehensive machine learning (ML)-based prediction model for intrahepatic cholestasis of pregnancy (IHCP) by integrating multi-modal data including demographic characteristics, laboratory biochemical indicators, and ultrasonic echocardiographic parameters. The model was designed to stratify ICP severity and remain applicable in settings lacking total bile acid (TBA) testing, which addresses current diagnostic gaps and may support the reduction of adverse perinatal outcomes. A retrospective cohort of 750 pregnant women (525 in training, 225 in testing) between July 2020 and October 2023 from the Central Hospital of Enshi Tujia and Miao Autonomous Prefecture was recruited for the study. Seven ML algorithms (Logistic regression, Decision Tree, Random Forest [RF], Extreme Gradient Boosting [XGBoost], Regularized Support Vector Machine [RSVM], Multilayer Perceptron [MLP], and Elastic Net [ENET]). The RF model exhibited superior performance, achieving ROC-AUC of 0.90 (training) and 0.86 (testing), with sensitivity and specificity both ≥0.93 in the testing cohort. Key predictors included pruritus, TBA, glycocholic acid, alkaline phosphatase, and ultrasonic indicators (ventricular wall mean thickness, myocardial echogenicity). Notably, the model retained efficacy without TBA, maintaining precision ≥0.75 across recall values of 0.6-0.9. The multi-modal RF model effectively predicts IHCP, enables severity stratification, and enhances accessibility in resource-limited settings, providing valuable support for targeted clinical interventions and may support the reduction of adverse perinatal outcomes.

The cold tolerance of Litopenaeus vannamei is important for breeding in specific areas. To explore the cold tolerance mechanism of L. vannamei , this study analyzed biochemical indicators, cell apoptosis, and metabolomic responses in cold-tolerant (Lv-T) and common (Lv-C) L. vannamei under low-temperature stress (18 °C and 10 °C). TUNEL analysis showed a significant increase in apoptosis of hepatopancreatic duct cells in L. vannamei under low-temperature stress. Biochemical analysis showed that Lv-T had significantly increased levels of superoxide dismutase (SOD) and triglycerides (TG), while alanine aminotransferase (ALT), alkaline phosphatase (ALP), lactate dehydrogenase (LDH-L), and uric acid (UA) levels were significantly decreased compared to Lv-C (p < 0.05). Metabolomic analysis displayed significant increases in metabolites such as LysoPC (P-16:0), 11beta-Hydroxy-3,20-dioxopregn-4-en-21-oic acid, and Pirbuterol, while metabolites such as 4-Hydroxystachydrine, Oxolan-3-one, and 3-Methyldioxyindole were significantly decreased in Lv-T compared to Lv-C. The differentially regulated metabolites were mainly enriched in pathways such as Protein digestion and absorption, Central carbon metabolism in cancer and ABC transporters. Our study indicate that low temperature induces damage to the hepatopancreatic duct of shrimp, thereby affecting its metabolic function. The cold resistance mechanism of Lv-T L. vannamei may be due to the enhancement of antioxidant enzymes and lipid metabolism.

Background Serum biochemical indicators are often regarded as direct reflections of animal metabolism and health. The molecular mechanisms underlying serum biochemical indicators metabolism of chicken (Gallus Gallus) have not been elucidated. Herein, we performed a genome-wide association study (GWAS) to identify the variation associated with serum biochemical indicators. The aim of this research was to broaden the understanding of the serum biochemical indicators in chickens. Results A GWAS of serum biochemical indicators was carried out on 734 samples from an F2 Gushi× Anka chicken population. All chickens were genotyped by sequencing, 734 chickens and 321,314 variants were obtained after quality control. Based on these variants, a total of 236 single-nucleotide polymorphisms (SNPs) on 9 chicken chromosomes (GGAs) were identified to be significantly (-log 10 ( P ) > 5.72) associated with eight of seventeen serum biochemical indicators. Ten novel quantitative trait locis (QTLs) were identified for the 8 serum biochemical indicator traits of the F2 population. Literature mining revealed that the ALPL , BCHE , GGT2 / GGT5 genes at loci GGA24, GGA9 and GGA15 might affect the alkaline phosphatase (AKP), cholinesterase (CHE) and γ-glutamyl transpeptidase (GGT) traits, respectively. Conclusion The findings of the present study may contribute to a better understanding of the molecular mechanisms of chicken serum biochemical indicator regulation and provide a theoretical basis for chicken breeding programs.

This paper examines various machine learning methods for assessing risk factors for cardiovascular diseases. To build predictive models, two approaches were used: the extreme gradient boosting (XGBoost) algorithm and a convolutional neural network (CNN). The focus is on analyzing the performance of each model in classification and regression tasks, as well as their ability to identify key biomarkers and risk factors such as cholesterol, ferritin, homocysteine and aspartate aminotransferase (AST) levels. XGBoost parameters have been optimized for working with tabular data, demonstrating high accuracy in risk prediction. The CNN model, despite the initial reduction in error on the training set, showed signs of overfitting when analyzing validation data. Performance evaluation using the metrics of mean squared error (MSE), coefficient of determination (R²), Akaike information criterion (AIC), and Bayesian information criterion (BIC) revealed significant differences between the models. The study results confirm the effectiveness of XGBoost in analyzing tabular data and summarizing risk factor knowledge, while the CNN model needs further optimization to handle sparse data. The work demonstrates the importance of choosing the right model architecture and training parameters to ensure reliable diagnosis of cardiovascular diseases.

Purpose This study aimed to establish suitable threshold values for biochemical indicators in low-risk pregnant women who underwent second trimester screening and design strategies for consecutive prenatal testing to increase trisomy 21 detection. Methods This study examined singleton pregnant women who underwent double, triple, or quadruple screening in the second trimester over six years. To obtain adequate detection efficiency for low-risk pregnancies, threshold values for serum biochemical indicators were established, and a cost-effectiveness assessment of the improved contingent screening strategy was conducted. Results Participants were included in serum double- ( n  = 88,550), triple- ( n  = 29,991), and quadruple-screening ( n  = 15,004) groups. Threshold values were defined as having a free beta subunit of human chorionic gonadotropin (free β-hCG) multiple of the median (MoM) ≥ 2.50, alpha-fetoprotein (AFP) MoM ≤ 0.50, or unconjugated estriol (uE3) MoM ≤ 0.70 for low risk. Low-risk pregnancies, comprising 1.35% (988/73,183), 4.45% (1,171/26,286), and 11.91% (1,559/13,085) of the double-, triple-, and quadruple-screening groups, respectively, underwent further non-invasive prenatal screening. In the double-, triple-, and quadruple-screening groups, we detected 11.76% (2/17), 40.00% (2/5), and 66.67% (2/3) of trisomy 21 cases with false negative results, respectively, with the overall detection rates of 85.00% (85/100), 90.63% (29/32), and 95.24% (20/21), respectively, and decreased ratio of overall costs of 5.26%, 16.63%, and 24.36%, respectively. Conclusion Utilizing threshold values of AFP, free β-hCG, and uE3 to trigger further non-invasive prenatal screening may increase trisomy 21 detection in pregnancies deemed low risk in the second trimester while reducing the overall costs of screening strategies.

Background: The fibrous root of ginseng (GFR) is the dried thin branch root or whisker root of Ginseng ( Panax ginseng C. A. Mey). It is known for its properties such as tonifying qi, producing body fluid, and quenching thirst. Clinically, it is used to treat conditions such as cough, hemoptysis, thirst, stomach deficiency, and vomiting. While GFR and Ginseng share similar metabolites, they differ in their metabolites ratios and efficacy. Furthermore, the specific role of GFR in protecting the body remains unclear. Methods: We employed ultra-high performance liquid chromatography-triple quadrupole mass spectrometry to examine alterations in brain neurotransmitters and elucidate the impact of GFR on the central nervous system. Additionally, we analyzed the serum and brain metabolic profiles of rats using ultra-high performance liquid chromatography-quadrupole-orbitrap mass spectrometry to discern the effect and underlying mechanism of GFR in delaying aging in naturally aged rats. Results: The findings of the serum biochemical indicators indicate that the intervention of GFR can enhance cardiovascular, oxidative stress, and energy metabolism related indicators in naturally aging rats. Research on brain neurotransmitters suggests that GFR can augment physiological functions such as learning and memory, while also inhibiting central nervous system excitation to a certain degree by maintaining the equilibrium of central neurotransmitters in aged individuals. Twenty-four abnormal metabolites in serum and seventeen abnormal metabolites in brain could be used as potential biomarkers and were involved in multiple metabolic pathways. Among them, in the brain metabolic pathways, alanine, aspartate and glutamate metabolism, arginine and proline metabolism, histidine metabolism, and tyrosine metabolism were closely related to central neurotransmitters. Butanoate metabolism improves energy supply for life activities in the aging body. Cysteine and methionine metabolism contributes to the production of glutathione and taurine and played an antioxidant role. In serum, the regulation of glycerophospholipid metabolism pathway and proline metabolism demonstrated the antioxidant capacity of GFR decoction. Conclution: In summary, GFR plays a role in delaying aging by regulating central neurotransmitters, cardiovascular function, oxidative stress, energy metabolism, and other aspects of the aging body, which lays a foundation for the application of GFR.

The excessive application of herbicides on farmlands can substantially reduce labor costs and increase crop yields, but can also have undesirable effects on terrestrial ecosystems. To evaluate the ecological toxicity of herbicides, metolachlor and fomesafen, two typical herbicides that are extensively used worldwide were chosen as target pollutants, and the endogeic earthworm Pheretima guillelmi , which is widely distributed in China, was selected as the test organism. A laboratory-scale microcosmic experiment was set, and energy resources, enzymes, and the composition and connections of intestinal microorganisms in earthworms were determined. Both herbicides depleted the energy resources of the earthworms, especially glycogen contents; increased the levels of antioxidant enzymes; and inhibited acetylcholinesterase. Moreover, the richness and diversity of the intestinal bacterial community of the earthworms were suppressed. Additionally, the bacterial composition at the genus level changed greatly and the connections between dominant bacteria increased dramatically. Most interactions among the bacterial genera belonging to the same and different phyla showed mutualism and competition, respectively. Importantly, metolachlor with higher toxicity had a transitory effect on these indicators in earthworms, whereas fomesafen, with lower toxicity but stronger bioaccumulation potential, exerted a sustaining impact on earthworms. Collectively, these results indicate that the toxic effects of herbicides on terrestrial organisms should be comprehensively considered in combination with biological toxicity, persistence, bioaccumulation potential, and other factors.

Ischemic cerebral infarction is the most prevalent type of stroke, causing significant disability and death worldwide. Transient ischemic attack (TIA) is a strong predictor of subsequent stroke. Individuals with dysmetabolism, such as hypertension, hypercholesterolemia, and diabetes, are at increased risk for cerebral infarction (CI) and TIA. In resource-limited settings, diagnosing CI and TIA can be particularly difficult due to a lack of advanced imaging and specialized expertise. Therefore, we aim to develop a simple, convenient, blood-based approach that could assist clinicians in diagnosing CI and TIA, especially in regions where advanced imaging or stroke-specific expertise is limited. All study subjects were patients admitted to the First Hospital of Xiamen University and healthy check-up populations between January 2018 and September 2023. This study employed five machine learning methods alongside 21 blood routine indicators, 30 blood biochemical indicators, age, and gender to construct predictive models for CI and TIA in both healthy individuals and those with dysmetabolism. The Area Under the Receiver Operating Characteristic (ROC) Curve (AUC) served as the metric to assess the comprehensive predictive capability of the models. Subsequently, the SHAP package was employed for model interpretation. Extreme Gradient Boosting (XGBoost) outperforms other models in all predictive models. In the models predicting CI and TIA among healthy, the AUC is 0.9958 (0.9947–0.9969) and 0.9928 (0.9899–0.9951), respectively. Among the nine shared key features of the two models are indicators of glucose metabolism, lipid metabolism, and liver metabolism. In the models for predicting CI and TIA among patients with hypertension, hypercholesterolemia, diabetes, and those with all three metabolic disorders combined, the AUCs ranged from 0.6990 to 0.8591. We found that the indicators K significantly contributed to predict CI and TIA from those with dysmetabolism. Additionally, metabolic-related indicators, such as glucose (GLU) and high-density lipoprotein cholesterol (HDL-C), are ranked highly among the top ten contributing features. XGBoost performed the best in all models. It can effectively differentiate CI and TIA from healthy and dysmetabolic patients by combining blood routine and blood biochemical indicators. Moreover, it can also differentiate CI from TIA. Although any suspicious findings from this model would still require confirmatory imaging, the simplicity and low cost of blood-based testing may offer a practical adjunct for clinicians—particularly in areas lacking advanced imaging or extensive stroke expertise—and could facilitate earlier diagnostic decision-making.