Exploration of identifying individual tumor tissue based on probabilistic model.

Variations in the tumor genome can result in allelic changes compared to the reference profile of its homogenous body source on genetic markers. This brings a challenge to source identification of tumor samples, such as clinically collected pathological paraffin-embedded tissue and sections. In this study, a probabilistic model was developed for calculating likelihood ratio (LR) to tackle this issue, which utilizes short tandem repeat (STR) genotyping data. The core of the model is to consider tumor tissue as a mixture of normal and tumor cells and introduce the incidence of STR variants (φ) and the percentage of normal cells (Mxn) as a priori parameters when performing calculations. The relationship between LR values and φ or Mxn was also investigated. Analysis of tumor samples and reference blood samples from 17 colorectal cancer patients showed that all samples had Log 10(LR) values greater than 1014. In the non-contributor test, 99.9% of the quartiles had Log 10(LR) values less than 0. When the defense's hypothesis took into account the possibility that the tumor samples came from the patient's relatives, LR greater than 0 was still obtained. Furthermore, this study revealed that LR values increased with decreasing φ and increasing Mxn. Finally, LR interval value was provided for each tumor sample by considering the confidence interval of Mxn. The probabilistic model proposed in this paper could deal with the possibility of tumor allele variability and offers an evaluation of the strength of evidence for determining tumor origin in clinical practice and forensic identification.

Carbon-based molecular properties efficiently predicted by deep learning-based quantum chemical simulation with large language models.

The prediction of thermodynamic properties of carbon-based molecules based on their geometrical conformation using fluctuation and density functional theories has achieved great success in the field of energy chemistry, while the excessive computational cost provides both opportunities and challenges for the integration of machine learning. In this work, a deep learning-based quantum chemical prediction model was constructed for efficient prediction of thermodynamic properties of carbon-based molecules. We constructed a novel framework - encoding the 3D information into a large language model (LLM), which in turn generates a 2D SMILES string, while embedding a learnable encoding designed to preserve the integrity of the original 3D information, providing better structural information for the model. Additionally, we have designed an equivariant learning module to encompass representations of conformations and feature learning for conformational sampling. This framework aims to predict thermodynamic properties more accurately than learning from 2D topology alone, while providing faster computational speeds than conventional simulations. By combining machine learning and quantum chemistry, we pioneer efficient practical applications in the field of energy chemistry. Our model advances the integration of data-driven and physics-based modeling to unlock novel insights into carbon-based molecules.

The Role of Gut Microbiota in Male Erectile Dysfunction of Rats.

PURPOSE: Erectile dysfunction (ED) is a common male sexual dysfunction. Gut microbiota plays an important role in various diseases. To investigate the effects and mechanisms of intestinal flora dysregulation induced by high-fat diet (HFD) on erectile function. MATERIALS AND METHODS: Male Sprague-Dawley rats aged 8 weeks were randomly divided into the normal diet (ND) and HFD groups. After 24 weeks, a measurement of erectile function was performed. We performed 16S rRNA sequencing of stool samples. Then, we established fecal microbiota transplantation (FMT) rat models by transplanting fecal microbiota from rats of ND group and HFD group to two new groups of rats respectively. After 24 weeks, erectile function of the rats was evaluated and 16S rRNA sequencing was performed, and serum samples were collected for the untargeted metabolomics detection. RESULTS: The erectile function of rats and the species diversity of intestinal microbiota in the HFD group was significantly lower, and the characteristics of the intestinal microbiota community structure were also significantly different between the two groups. The erectile function of rats in the HFD-FMT group was significantly lower than that of rats in the ND-FMT group. The characteristics of the intestinal microbiota community structure were significantly different. In the HFD-FMT group, 27 metabolites were significantly different and they were mainly involved in the several inflammation-related pathways. CONCLUSIONS: Intestinal microbiota disorders induced by HFD can damage the intestinal barrier of rats, change the serum metabolic profile, induce low-grade inflammation and apoptosis in the corpus cavernosum of the penis, and lead to ED.

Regulating Schwann Cell Growth by Nanosecond Pulsed Electric Field for Peripheral Nerve Regeneration In Vitro.

Schwann cells (SCs) are myelinating cells of the peripheral nervous system, playing a crucial role in peripheral nerve regeneration. Nanosecond Pulse Electric Field (nsPEF) is an emerging method applicable in nerve electrical stimulation that has been demonstrated to be effective in stimulating cell proliferation and other biological processes. Aiming to assess whether SCs undergo significant changes under nsPEF and help explore the potential for new peripheral nerve regeneration methods, cultured RSC96 cells were subjected to nsPEF stimulation at 5 kV and 10 kV, followed by continued cultivation for 3-4 days. Subsequently, some relevant factors expressed by SCs were assessed to demonstrate the successful stimulation, including the specific marker protein, neurotrophic factor, transcription factor, and myelination regulator. The representative results showed that nsPEF significantly enhanced the proliferation and migration of SCs and the ability to synthesize relevant factors that contribute positively to the regeneration of peripheral nerves. Simultaneously, lower expression of GFAP indicated the benign prognosis of peripheral nerve injuries. All these outcomes show that nsPEF has great potential as an efficient treatment method for peripheral nerve injuries by stimulating SCs.

Association between the triglyceride-glucose index and thyroid disorders: a cross-sectional survey and Mendelian randomization analysis.

BACKGROUND: Metabolic diseases are associated with thyroid disorders. Insulin resistance is the common pathological basis of metabolic diseases. We explored the relationship between the triglyceride-glucose (TyG) index, a simple insulin-resistance marker, and thyroid disorders. METHODS: Eligible TIDE (Thyroid Diseases, Iodine Status and Diabetes Epidemiology) subjects (n = 47,710) were screened with inclusion/exclusion criteria. Thyroid disorder prevalence among different TyG index groups was stratified by sex. Logistic regression evaluated the correlation between the TyG index and thyroid disorders. Multiple linear regression evaluated the association between the TyG index and TSH. Additionally, two-sample Mendelian randomization (MR) using published genome-wide association study data evaluated causality in the association between the TyG index and TSH. RESULTS: Men and women with greater TyG indices had a significantly greater prevalence of thyroid disorders than individuals with the lowest quartile (Q1) of TyG index (p < 0.05). Following adjustment for confounding factors, we observed that a greater TyG index significantly increased the risk of subclinical hypothyroidism in men and women (men: Q2: odds ratio (OR) [95% confidence interval (CI)] = 1.22 [1.07-1.38], p = 0.002; Q3: OR [95% CI] = 1.28 [1.12-1.45], p < 0.001; Q4: OR [95% CI] = 1.29 [1.12-1.50], p = 0.001; women: Q2: OR [95% CI] = 1.25 [1.12-1.39], p < 0.001; Q3: OR [95% CI] = 1.47 [1.31-1.64], p < 0.001; Q4: OR [95% CI] = 1.61 [1.43-1.82], p < 0.001). Only among women was the highest TyG index quartile associated with hypothyroidism (OR [95% CI] = 1.70 [1.15-2.50], p = 0.007). Additionally, in men, the association exists only in the more than adequate iodine intake population. In women, the relationship between the TyG index and thyroid disorders disappears after menopause. Furthermore, the TyG index exhibited a linear positive correlation with TSH levels. The MR analysis results revealed a causal relationship between a genetically determined greater TyG index and increased TSH (inverse-variance weighting (IVW): OR [95% CI] = 1.14 [1.02-1.28], p = 0.020); however, this causal relationship disappeared after adjusting for BMI in multivariable MR (MVMR) analysis (MVMR-IVW: OR 1.03, 95% CI 0.87-1.22, p = 0.739). CONCLUSIONS: A greater TyG index is associated with hypothyroidism and subclinical hypothyroidism and varies by sex and menopausal status. MR analysis demonstrated that the causal relationship between a genetically determined greater TyG index and elevated TSH levels is confounded or mediated by BMI.

MLKL promotes hepatocarcinogenesis through inhibition of AMPK-mediated autophagy.

The pseudokinase mixed lineage kinase domain-like (MLKL) is an essential component of the activation of the necroptotic pathway. Emerging evidence suggests that MLKL plays a key role in liver disease. However, how MLKL contributes to hepatocarcinogenesis has not been fully elucidated. Herein, we report that MLKL is upregulated in a diethylnitrosamine (DEN)-induced murine HCC model and is associated with human hepatocellular carcinomas. Hepatocyte-specific MLKL knockout suppresses the progression of hepatocarcinogenesis. Conversely, MLKL overexpression aggravates the initiation and progression of DEN-induced HCC. Mechanistic study reveals that deletion of MLKL significantly increases the activation of autophagy, thereby protecting against hepatocarcinogenesis. MLKL directly interacts with AMPKα1 and inhibits its activity independent of its necroptotic function. Mechanistically, MLKL serves as a bridging molecule between AMPKα1 and protein phosphatase 1B (PPM1B), thus enhancing the dephosphorylation of AMPKα1. Consistently, MLKL expression correlates negatively with AMPKα1 phosphorylation in HCC patients. Taken together, our findings highlight MLKL as a novel AMPK gatekeeper that plays key roles in inhibiting autophagy and driving hepatocarcinogenesis, suggesting that the MLKL-AMPKα1 axis is a potential therapeutic target for HCC.

AuNi bimetallic aerogel with ultra-high stability applied in smart and portable biosensing.

Glucose detection is of significant importance in providing information to the human health management. However, conventional enzymatic glucose sensors suffer from a limited long-term stability due to the losing activity of the enzymes. In this work, the AuNi bimetallic aerogel with a well-defined nanowire network is synthesized and applied as the sensing nanomaterial in the non-enzymatic glucose detection. The three-dimensional (3D) hierarchical porous structure of the AuNi bimetallic aerogel ensures the high sensitivity of the sensor (40.34 µA mM-1 cm-2). Theoretical investigation unveiled the mechanism of the boosting electrocatalytic activity of the AuNi bimetallic aerogel toward glucose. A better adhesion between the sensing nanomaterial and the screen-printing electrodes (SPEs) is obtained after the introduction of Ni. On the basis of a wide linearity in the range of 0.1-5 mM, an excellent selectivity, an outstanding long-term stability (90 days) as well as the help of the signal processing circuit and an M5stack development board, the as-prepared glucose sensor successfully realizes remote monitoring of the glucose concentration. We speculate that this work is favorable to motivating the technological innovations of the non-enzymatic glucose sensors and intelligent sensing devices.

Micro-Scale Particle Tracking: From Conventional to Data-Driven Methods.

Micro-scale positioning techniques have become essential in numerous engineering systems. In the field of fluid mechanics, particle tracking velocimetry (PTV) stands out as a key method for tracking individual particles and reconstructing flow fields. Here, we present an overview of the micro-scale particle tracking methodologies that are predominantly employed for particle detection and flow field reconstruction. It covers various methods, including conventional and data-driven techniques. The advanced techniques, which combine developments in microscopy, photography, image processing, computer vision, and artificial intelligence, are making significant strides and will greatly benefit a wide range of scientific and engineering fields.

Mechanism of Microwave Radiation-Induced Learning and Memory Impairment Based on Hippocampal Metabolomics.

The brain is complex and metabolically active, and the detection of metabolites plays an important role in brain development and diseases. Currently, there is a lack of research on the metabolic spectrum changes in learning and memory impairment, and hippocampal damage induced by microwave radiation from the metabolic perspective. Aiming to provide sensitive indicators for microwave radiation-induced brain damage and establish a foundation for understanding its injury mechanisms, this study employed non-targeted metabolomics to investigate metabolic fluctuations and key metabolic pathway alterations in rats' hippocampal tissue after microwave radiation. The memory and spatial exploration abilities of rats decreased after radiation. The postsynaptic densities were thickened in the MW group. The cholesterol sulfate, SM(d16:1/24:1(15Z)), and linoelaidylcarnitine were significantly increased after radiation, whereas etrahydrocorticosterone, L-phenylalanine, and histamine were significantly decreased after radiation. These metabolites were enriched in signaling pathways related to the inflammatory mediator regulation of transient receptor potential (TRP) channels, neuroactive ligand-receptor interaction, steroid hormone biosynthesis, and phenylalanine, tyrosine, and tryptophan biosynthesis. These findings indicate that microwave radiation causes spatial learning and memory dysfunction in rats and structural damage to hippocampal tissue.

Metallofullerenol alleviates alcoholic liver damage via ROS clearance under static magnetic and electric fields.

Alcoholic liver disease (ALD) is a common chronic redox disease caused by increased alcohol consumption. Abstinence is a major challenge for people with alcohol dependence, and approved drugs have limited efficacy. Therefore, this study aimed to explore a new treatment strategy for ALD using ferroferric oxide endohedral fullerenol (Fe3O4@C60(OH)n) in combination with static magnetic and electric fields (sBE). The primary hepatocytes of 8-9-week-old female BALB/c mice were used to evaluate the efficacy of the proposed combination treatment. A mouse chronic binge ethanol feeding model was established to determine the alleviatory effect of Fe3O4@C60(OH)n on liver injury under sBE exposure. Furthermore, the ability of Fe3O4@C60(OH)n to eliminate •OH was evaluated. Alcohol-induced hepatocyte and mitochondrial damage were reversed in vitro. Additionally, the combination therapy reduced liver damage, alleviated oxidative stress by improving antioxidant levels, and effectively inhibited liver lipid accumulation in animal experiments. Here, we used a combination of magnetic derivatives of fullerenol and sBE to further improve the ROS clearance rate, thereby alleviating ALD. The developed combination treatment may effectively improve alcohol-induced liver damage and maintain redox balance without apparent toxicity, thereby enhancing therapy aimed at ALD and other redox diseases.

Bioinformatics analysis and experimental verification of the cancer-promoting effect of DHODH in clear cell renal cell carcinoma.

Clear cell renal cell carcinoma (ccRCC) is a malignant tumor of the urinary system. To explore the potential mechanisms of DHODH in ccRCC, we analyzed its molecular characteristics using public databases. TCGA pan-cancer dataset was used to analyze DHODH expression in different cancer types and TCGA ccRCC dataset was used to assess differential expression, prognosis correlation, immune infiltration, single-gene, and functional enrichment due to DHODH. The GSCALite and CellMiner databases were employed to explore drugs and perform molecular docking analysis with DHODH. Protein-protein interaction networks and ceRNA regulatory networks of DHODH were constructed using multiple databases. The effect of DHODH on ccRCC was confirmed in vitro. DHODH was highly expressed in ccRCC. Immune infiltration analysis revealed that DHODH may be involved in regulating the infiltration of immunosuppressive cells such as Tregs. Notably, DHODH influenced ccRCC progression by forming regulatory networks with molecules, such as hsa-miR-26b-5p and UMPS and significantly enhanced the malignant characteristics of ccRCC cells. Several drugs, such as lapatinib, silmitasertib, itraconazole, and dasatinib, were sensitive to DHODH expression and exhibited strong molecular binding with it. Thus, DHODH may promote ccRCC progression and is a candidate effective therapeutic target for ccRCC.

Transparent grating-based metamaterials for dynamic infrared radiative regulation smart windows.

Dynamic infrared radiation regulation has been widely explored for smart windows because of its vital importance for comfortable and energy-efficient buildings. However, it remains a great challenge to synchronously achieve high visible transmittance and pronounced infrared tunability. Here, we propose a dynamic infrared tunable metamaterial composed of indium tin oxide (ITO) gratings, an air insulator, and an ITO reflector. The ITO grating-based infrared radiation regulator exhibits a high emissivity tunability of 0.73 at 8-13 µm while maintaining a high visible transmittance of 0.65 and 0.72 before and after actuation, respectively. By adjusting the geometric parameters, the tunable bandwidth can be further extended to 3-30 µm and the ultra-broadband tunability reaches 0.62. The excellent infrared tunable performance arises from the insulator thickness-dependent effect of Fabry-Pérot and propagating surface plasmon resonance coupling and decoupling, which lead to perfect and low absorption, respectively. This work provides potential for the advancement of smart window technology and makes a significant contribution to sustainable buildings.

Association of pretreatment neutrophil-to-lymphocyte ratio with clinical outcomes in cancer immunotherapy: An evidence synthesis from 30 meta-analyses.

BACKGROUND: The Neutrophil-to-lymphocyte ratio (NLR) holds relevance in cancer immunotherapy outcomes, yet its validation remains limited. Thus, we conducted an umbrella review to comprehensively assess the association between pretreatment NLR and immunotherapy outcomes, along with evaluating their credibility and strength. METHODS: Electronic databases, including PubMed, Web of Science, Embase, Scopus, and Cochrane, were systematically searched for eligible systematic reviews and meta-analyses. Quality assessment and evidence grading utilized AMSTAR, GRADE, and additional classification criteria, following PRISMA and PRIOR guidelines. RESULTS: Thirty unique meta-analyses were included, with 24 associations (80%) exhibiting statistical significance. Notably, associations between pretreatment NLR and the prognosis of renal cell carcinoma, hepatocellular carcinoma, melanoma, and non-small cell lung cancer garnered highly suggestive or convincing evidence grading. CONCLUSIONS: Elevated pretreatment NLR correlates with poor outcomes in cancer immunotherapy, suggesting its potential as a biomarker for identifying appropriate treatment populations and predicting clinical outcomes. Nevertheless, further validation through prospective cohort studies is warranted.

Arbuscular Mycorrhizal Fungi-Mediated Modulation of Physiological, Biochemical, and Secondary Metabolite Responses in Hemp (Cannabis sativa L.) under Salt and Drought Stress.

The increasing impact of global climate change has resulted in adversity stresses, like salt and drought, gradually becoming the main factors that limit crop growth. Hemp, which contains numerous medicinal active components and multiple bioactive functions, is widely used in the agricultural, industrial, and medical fields, hence promoting the rapid development of related industries. Arbuscular mycorrhizal fungi (AMF) can establish a symbiotic relationship with 80% of vascular plants. This symbiosis promotes host plant growth, regulates plant physiology and biochemistry, facilitates secondary metabolite synthesis, and enhances resistance to abiotic stresses. However, the effects of salt stress, drought stress, and AMF interaction in hemp are not well understood. In this study, to investigate this, we performed a study where we cultured hemp that was either inoculated or uninoculated with Funneliformis mosseae and determined changes in effective colonization rate, growth, soluble substances, photosynthesis, fluorescence, ions, and secondary metabolites by cultivating hemp under different concentrations of NaCl (0 mM, 100 mM, and 200 mM) and different soil moisture content (45%, 25%, and 15%). The results showed that salt, drought stress, or salt-drought interaction stress all inhibited colonization rate after stress, plant growth, mainly due to ion toxicity and oxidative damage. Inoculation with F. mosseae effectively alleviated plant growth inhibition under 100 mM NaCl salt stress, drought stress, and salt-drought interaction stress conditions. It also improved osmoregulation, photosynthetic properties, fluorescence properties, and ion homeostasis, and promoted the accumulation of secondary metabolites. However, under 200 mM NaCl salt stress conditions, inoculation with F. mosseae negatively affected plant physiology, biochemistry, and secondary metabolite synthesis, although it did alleviate growth inhibition. The results demonstrate that there are different effects of salt-drought interaction stress versus single stress (salt or drought stress) on plant growth physiology. In addition, we provide new insights about the positive effects of AMF on host plants under such stress conditions and the effects of AMF on plants under high salt stress.

Hierarchical Self-Assembly of Multidimensional Functional Materials from Sequence-Defined Peptoids.

Hierarchical self-assembly represents a powerful strategy for the fabrication of functional materials across various length scales. However, achieving precise formation of functional hierarchical assemblies remains a significant challenge and requires a profound understanding of molecular assembly interactions. In this study, we present a molecular-level understanding of the hierarchical assembly of sequence-defined peptoids into multidimensional functional materials, including twisted nanotube bundles serving as a highly efficient artificial light harvesting system. By employing synchrotron-based powder X-ray diffraction and analyzing single crystal structures of model compounds, we elucidated the molecular packing and mechanisms underlying the assembly of peptoids into multidimensional nanostructures. Our findings demonstrate that incorporating aromatic functional groups, such as tetraphenyl ethylene (TPE), at the termini of assembling peptoid sequences promotes the formation of twisted bundles of nanotubes and nanosheets, thus enabling the creation of a highly efficient artificial light harvesting system. This research exemplifies the potential of leveraging sequence-defined synthetic polymers to translate microscopic molecular structures into macroscopic assemblies. It holds promise for the development of functional materials with precisely controlled hierarchical structures and designed functions.

Short-term forecasting approach of single well production based on multi-intelligent agent hybrid model.

The short-term prediction of single well production can provide direct data support for timely guiding the optimization and adjustment of oil well production parameters and studying and judging oil well production conditions. In view of the coupling effect of complex factors on the daily output of a single well, a short-term prediction method based on a multi-agent hybrid model is proposed, and a short-term prediction process of single well output is constructed. First, CEEMDAN method is used to decompose and reconstruct the original data set, and the sliding window method is used to compose the data set with the obtained components. Features of components by decomposition are described as feature vectors based on values of fuzzy entropy and autocorrelation coefficient, through which those components are divided into two groups using cluster algorithm for prediction with two sub models. Optimized online sequential extreme learning machine and the deep learning model based on encoder-decoder structure using self-attention are developed as sub models to predict the grouped data, and the final predicted production comes from the sum of prediction values by sub models. The validity of this method for short-term production prediction of single well daily oil production is verified. The statistical value of data deviation and statistical test methods are introduced as the basis for comparative evaluation, and comparative models are used as the reference model to evaluate the prediction effect of the above multi-agent hybrid model. Results indicated that the proposed hybrid model has performed better with MAE value of 0.0935, 0.0694 and 0.0593 in three cases, respectively. By comparison, the short-term prediction method of single well production based on multi-agent hybrid model has considerably improved the statistical value of prediction deviation of selected oil well data in different periods. Through statistical test, the multi-agent hybrid model is superior to the comparative models. Therefore, the short-term prediction method of single well production based on a multi-agent hybrid model can effectively optimize oilfield production parameters and study and judge oil well production conditions.

Characterization, antioxidant activity and in vitro digestion of hawthorn pectin prepared by gradient ethanol precipitation.

Four modified hawthorn pectin fractions (MHPs), named MHP-30, MHP-50, MHP-70 and MHP-90, were obtained by ultrasonic-assisted pectin methyl esterase modification and gradient ethanol precipitation. The results indicated that all four MHPs were composed of galacturonic acid, galactose, xylose, arabinose, glucose and mannose in different proportions. With the increase of the ethanol concentration, the molecular weight, esterification degree and galacturonic acid content of MHPs all decreased, whereas the arabinose content and branching degree increased. The structural characterization from XRD, SEM, and FT-IR showed that four MHPs exhibited amorphous structure, similar functional groups, diverse surface morphologies. Besides, in vitro antioxidant assays confirmed that MHP-70 and MHP-90 exhibited stronger total antioxidant activities than MHP-30 and MHP-50. The results of simulated saliva-gastrointestinal digestion showed that the molecular weight of MHP-70 and MHP-90 remained stable, yielded small amounts of reducing sugars, and were resistant to digestion in the human upper digestive tract. Overall, MHP-70 and MHP-90 shown great potential as novel natural antioxidants, which are expected to be good carbon sources for the utilization of intestinal microorganisms.

The Neglected Role of Asphaltene in the Synthesis of Mesophase Pitch.

This study investigates the synthesis of mesophase pitch using low-cost fluid catalytic cracking (FCC) slurry and waste fluid asphaltene (WFA) as raw materials through the co-carbonization method. The resulting mesophase pitch product and its formation mechanism were thoroughly analyzed. Various characterization techniques, including polarizing microscopy, softening point measurement, Fourier-transform infrared spectroscopy (FTIR), and thermogravimetric analysis (TGA), were employed to characterize and analyze the properties and structure of the mesophase pitch. The experimental results demonstrate that the optimal optical texture of the mesophase product is achieved under specific reaction conditions, including a temperature of 420 °C, pressure of 1 MPa, reaction time of 6 h, and the addition of 2% asphaltene. It was observed that a small amount of asphaltene contributes to the formation of mesophase pitch spheres, facilitating the development of the mesophase. However, excessive content of asphaltene may cover the surface of the mesophase spheres, impeding the contact between them and consequently compromising the optical texture of the mesophase pitch product. Furthermore, the inclusion of asphaltene promotes polymerization reactions in the system, leading to an increase in the average molecular weight of the mesophase pitch. Notably, when the amount of asphaltene added is 2%, the mesophase pitch demonstrates the lowest ID/IG value, indicating superior molecular orientation and larger graphite-like microcrystals. Additionally, researchers found that at this asphaltene concentration, the mesophase pitch exhibits the highest degree of order, as evidenced by the maximum diffraction angle (2θ) and stacking height (Lc) values, and the minimum d002 value. Moreover, the addition of asphaltene enhances the yield and aromaticity of the mesophase pitch and significantly improves the thermal stability of the resulting product.