Protection of neutrophils by bone marrow mesenchymal stromal cells is enhanced by tumor-associated inflammatory cytokines.

Mesenchymal stromal/stem cells (MSCs), which are distributed in many tissues including bone marrow, have been reported to play a critical role in tumor development. While bone marrow, the primary site for hematopoiesis, is important for establishing the immune system, whether MSCs in the bone marrow can promote tumor growth via influencing hematopoiesis remains unclear. We observed that the numbers of MSCs and neutrophils were increased in bone marrow in tumor-bearing mice. Moreover, co-culture assay showed that MSCs strongly protected neutrophils from apoptosis and induced their maturation. G-CSF and GM-CSF have been well-documented to be associated with neutrophil formation. We found a remarkably increased level of G-CSF, but not GM-CSF, in the supernatant of MSCs and the serum of tumor-bearing mice. The G-CSF expression can be enhanced with inflammatory cytokines (IFNγ and TNFα) stimulation. Furthermore, we found that IFNγ and TNFα-treated MSCs enhanced their capability of promoting neutrophil survival and maturation. Our results indicate that MSCs display robustly protective effects on neutrophils to contribute to tumor growth in bone niches.

Enhanced PSO feature selection with Runge-Kutta and Gaussian sampling for precise gastric cancer recurrence prediction.

Gastric cancer (GC), characterized by its inconspicuous initial symptoms and rapid invasiveness, presents a formidable challenge. Overlooking postoperative intervention opportunities may result in the dissemination of tumors to adjacent areas and distant organs, thereby substantially diminishing prospects for patient survival. Consequently, the prompt recognition and management of GC postoperative recurrence emerge as a matter of paramount urgency to mitigate the deleterious implications of the ailment. This study proposes an enhanced feature selection model, bRSPSO-FKNN, integrating boosted particle swarm optimization (RSPSO) with fuzzy k-nearest neighbor (FKNN), for predicting GC. It incorporates the Runge-Kutta search, for improved model accuracy, and Gaussian sampling, enhancing the search performance and helping to avoid locally optimal solutions. It outperforms the sophisticated variants of particle swarm optimization when evaluated in the CEC 2014 test suite. Furthermore, the bRSPSO-FKNN feature selection model was introduced for GC recurrence prediction analysis, achieving up to 82.082 % and 86.185 % accuracy and specificity, respectively. In summation, this model attains a notable level of precision, poised to ameliorate the early warning system for GC recurrence and, in turn, advance therapeutic options for afflicted patients.

Quantitative Analysis of Nanoplastics in Single Cells by Subcellular Chromatography.

The accumulation and spatial distribution of intracellular nanoplastic particles provide useful information about their spatiotemporal toxicological effects mediated by the physicochemical parameters of nanoplastics in living cells. In this study, a sample injection-transfer method was designed with an accuracy of up to femtoliters to attoliters to match the volume required for ultranarrow-bore open-tubular liquid chromatography. The separation and concentration quantification of mixed polystyrenes in different regions in living cells were achieved by directly transferring picoliter/femtoliter volumes of intracellular cytoplasm to an ultranarrow-bore open-tubular chromatographic column. The measurement of pollutant concentration in different areas of a small-volume target (single cell) was realized. This method is expected to be used in the qualitative and quantitative analyses of complex, mixed, and label-free nanoplastics (a few nm in size) in the subregions of living cells.

Synergistic effect-triggered fluorescence quenching enables rapid and sensitive detection of alkaline phosphatase.

The development of biosensors mediated by synergistic quenching effect is of great significance for rapid and accurate clinical diagnosis. Hence, we prepared a cyan-emitting fluorescent Si dots for alkaline phosphatase (ALP) detection through the synergistic quenching effect of inner filter effect (IFE) and photo-induced electron transfer (PET). Si dots were prepared by microwave-assisted method, which displayed high quantum yield (28.7%), as well as good physiochemical properties, such as photo-stability, pH stability, and chemical stability. As the hydrolysate of 4-nitrophenyl phosphate disodium salt hexahydrate catalyzed by ALP, both IFE and PET of 4-nitrophenyl to Si dots were used for the turn-off mode detection of ALP. The linear relationships were established between the change of fluorescence intensity and ALP concentration in the range of 0.05 U L-1 to 5.0 U L-1, and 5.0 U L-1 to 80.0 U L-1, respectively. The detection limit was 0.01 U L-1. The synergistic quenching effect caused the turn-off mode detection to be more sensitive, and it can also be used for the accurate detection of ALP in human serum, thereby showing great anti-interference ability in complex environments.

An efficient multi-threshold image segmentation for skin cancer using boosting whale optimizer.

Due to the terrible manifestations of skin cancer, it seriously disturbs the quality of life status and health of patients, so we needs treatment plans to detect it early and avoid it causing more harm to patients. Medical disease image threshold segmentation technique can well extract the region of interest and effectively assist in disease recognition. Moreover, in multi-threshold image segmentation, the selection of the threshold set determines the image segmentation quality. Among the common threshold selection methods, the selection based on metaheuristic algorithm has the advantages of simplicity, easy implementation and avoidable local optimization. However, different algorithms have different performances for different medical disease images. For example, the Whale Optimization Algorithm (WOA) does not give a satisfactory performance for thresholding skin cancer images. We propose an improved WOA (LCWOA) in which the Levy operator and chaotic random mutation strategy are introduced to enhance the ability of the algorithm to jump out of the local optimum and to explore the search space. Comparing with different existing WOA variants on the CEC2014 function set, our proposed and improved algorithm improves the efficiency of the search. Experimental results show that our method outperforms the extant WOA variants in terms of optimization performances, improving the convergence accuracy and velocity. The method is also applied to solve the threshold selection in the skin cancer image segmentation problem, and LCWOA also gives excellent performance in obtaining optimal segmentation results.

Current Progress, Challenges and Perspectives in the Microalgal-Bacterial Aerobic Granular Sludge Process: A Review.

Traditional wastewater treatment technologies have become increasingly inefficient to meet the needs of low-consumption and sustainable wastewater treatment. Researchers are committed to seeking new wastewater treatment technologies, to reduce the pressure on the environment caused by resource shortages. Recently, a microalgal-bacterial granular sludge (MBGS) technology has attracted widespread attention due to its high efficiency wastewater treatment capacity, low energy consumption, low CO2 emissions, potentially high added values, and resource recovery capabilities. This review focused primarily on the following aspects of microalgal-bacterial granular sludge technology: (1) MBGS culture and maintenance operating parameters, (2) MBGS application in different wastewaters, (3) MBGS additional products: biofuels and bioproducts, (4) MBGS energy saving and consumption reduction: greenhouse gas emission reduction, and (5) challenges and prospects. The information in this review will help us better understand the current progress and future direction of the MBGS technology development. It is expected that this review will provide a sound theoretical basis for the practical applications of a MBGS technology in environmentally sustainable wastewater treatment, resource recovery, and system optimization.

An efficient rotational direction heap-based optimization with orthogonal structure for medical diagnosis.

The heap-based optimizer (HBO) is an optimization method proposed in recent years that may face local stagnation problems and show slow convergence speed due to the lack of detailed analysis of optimal solutions and a comprehensive search. Therefore, to mitigate these drawbacks and strengthen the performance of the algorithm in the field of medical diagnosis, a new MGOHBO method is proposed by introducing the modified Rosenbrock's rotational direction method (MRM), an operator from the grey wolf optimizer (GWM), and an orthogonal learning strategy (OL). The MGOHBO is compared with eleven famous and improved optimizers on IEEE CEC 2017. The results on benchmark functions indicate that the boosted MGOHBO has several significant advantages in terms of convergence accuracy and speed of the process. Additionally, this article analyzed the diversity and balance of MGOHBO in detail. Finally, the proposed MGOHBO algorithm is utilized to optimize the kernel extreme learning machines (KELM), and a new MGOHBO-KELM is proposed. To validate the performance of MGOHBO-KELM, seven disease diagnostic questions were introduced for testing in this work. In contrast to advanced models such as HBO-KELM and BP, it can be concluded that the MGOHBO-KELM model can achieve optimal results, which also proves that it has practical significance in solving medical diagnosis problems.

Lupus nephritis diagnosis using enhanced moth flame algorithm with support vector machines.

Systemic lupus erythematosus is a chronic autoimmune disease that affects the kidney in most patients. Lupus nephritis (LN) is divided into six categories by the International Society of Nephrology/Renal Pathology Society (ISN/RPS). The purpose of this research is to build a framework for discriminating between ISN/RPS pure class V(MLN) and classes III ± V or IV ± V (PLN) using real clinical data. The framework is developed by merging a hybrid stochastic optimizer, moth-flame algorithm (HMFO), with a support vector machine (SVM), dubbed HMFO-SVM. The HMFO is constructed by enhancing the original moth-flame algorithm (MFO) with a bee-foraging learning operator, which guarantees that the algorithm speeds convergence and departs from the local optimum. The HMFO is used to optimize parameters and select features simultaneously for SVM on clinical SLE data. On 23 benchmark tests, the suggested HMFO method is validated. Finally, clinical data from LN patients are analyzed to determine the efficacy of HMFO-SVM over other SVM rivals. The statistical findings indicate that all measures have predictive capabilities and that the suggested HMFO-SVM is more stable for analyzing systemic LN. HMFO-SVM may be used to analyze LN as a feasible computer-assisted technique.

Enhanced Flexibility of the Segmented Honey Bee Tongue with Hydrophobic Tongue Hairs.

Fibrous surfaces in nature have already exhibited excellent functions that are normally ascribed to the synergistic effects of special structures and material properties. The honey bee tongue, foraging liquid food in nature, has a unique segmented surface covered with dense hairs. Since honey bees are capable of using their tongue to adapt to possibly the broadest range of feeding environments to exploit every possible source of liquids, the surface properties of the tongue, especially the covering hairs, would likely represent an evolutionary optimization. In this paper, we show that their tongue hairs are stiff and hydrophobic, the latter of which is highly unexpected as the structure is designed for liquid capturing. We found that such hydrophobicity can prevent those stiff hairs from being adhered to the soft tongue surface, which could significantly enhance the deformability of the tongue when honey bees feed at various surfaces and promote their adaptability to different environments. These findings bridge the relationship between surface wettability and structural characteristics, which may shed new light on designing flexible microstructured fiber systems to transport viscous liquids.

Investigation of metformin hydrochloride-bovine serum albumin interaction by narrow-bore capillary zone electrophoresis.

A narrow-bore capillary (inner diameter = 2 µm) zone electrophoresis method was developed to study the interaction between metformin hydrochloride and bovine serum albumin. Free metformin hydrochloride, free bovine serum albumin, and their complex were completely separated in a volume of tens of picoliters. The association constant (Ka = 1.04 × 103 L mol-1) and the number of binding sites (n = 0.9789) were then obtained.

Determination of Nanoplastics Using a Novel Contactless Conductivity Detector with Controllable Geometric Parameters.

Plastic waste in the environment is continuously degraded to form nanoplastic particles, and its harm has attracted widespread attention. At present, the identification and quantification of nanoplastics are performed by visual observation and using some spectroscopy methods, which are time-consuming and lack accuracy. Therefore, this study proposes a contactless conductivity detector (C4D) based on a glass microfluidic chip with controllable geometric parameters to quantify nanoplastics. We found that when the insulating layer thickness was 15 µm, the electrode spacing was 1 mm, and the shielding method was on-chip shielding, the detector displayed the best performance. The detector possesses a simple structure with high sensitivity and outstanding reproducibility, that is, the limit of detection of KCl solutions can reach the micromolar level, and the intraday RSD is 0.2% (n = 5). This work uses a microfluidic chip C4D to study nanoplastics for the first time, and the limit of detection is 0.25 µg/mL and the quantitative limit is 0.8 µg/mL. In addition, plant experiments have verified that terrestrial plants can absorb nanoplastics in water, expanding the application of contactless conductivity detectors and providing a new method for the quantitative analysis of nanoplastics.

Hydrophilic and opened canals in honey bee tongue rods endow elastic structures with multiple functions.

The honey bee (Apis mellifera L.) tongue is a sophisticated and dexterous probing device that can bend and twist, adapting to various surfaces for liquid imbibition and/or gustatory sensing. The tongue exhibits remarkable extendibility, flexibility, and durability, which may be essentially ascribed to the internal elastic rod that supports the entire tongue. However, neither the material composition nor the structural features of the rod, especially a peculiar inner canal that facilitates feeding, have been studied in relation to their function. Herein, by combining a set of imaging techniques, including optical microscopy, high-speed videography, scanning electron microscopy, micro-computed tomography (micro-CT), and confocal laser scanning microscopy, we characterize the spatial morphology, surface wettability and material composition of honey bee tongue rods. By performing mechanical testing, including atomic force microscopy, fracture testing, and finite element analysis, we provide the first evidence that the internal canal of the rod may represent a specialized structure for water retention due to the specific chemistry of resilin, which is an elastomeric protein that dominates the entire rod and renders it highly elastic, compliant and robust. Numerical simulations also suggest that the opening of the canal may facilitate larger deformations in twisting, extending the flexibility of the rod. STATEMENT OF SIGNIFICANCE: The honey bee is one of the most important pollinators around the world and is capable of foraging a wide spectrum of liquid sources by dipping into them with a miniature hairy tongue. However, there are no direct muscles distributed inside the tongue, instead, there is a conspicuous elastic rod with a hollow core. The rod extends for its full length and, according to our study, structurally reinforces the entire tongue to achieve functional versatility, and suggests a water containing function of the rod canal for maintaining the elasticity of the protein (resilin) that constitutes the rod. Our results broaden understandings of the relationship among morphology, materials science, and function of a honey bee tongue.

[The expression of DJ-1 gene in human hepatocellular carcinoma and its relationship with tumor invasion and metastasis].

OBJECTIVE: To study the expression of DJ-1 in human hepatocellular carcinoma and its relationship with tumor invasion and metastasis. METHODS: Reverse transcription polymerase chain reaction (RT-PCR) and immunohistochemical methods were used to detect the expression of DJ-1 mRNA and protein in tumor tissue and para-tumor tissue of 46 cases of hepatocellular carcinoma. The relationship between the expression of DJ-1 and clinicopathologic parameters was analyzed. RESULTS: DJ-1 mRNA and protein were expressed in 69.6% and 58.7% of the tumor tissues, which were significantly higher than those in para-tumor tissues (39.1% and 34.8%), chi2 = 8.587, P < 0.05. The increased expression of DJ-1 protein was not correlated with sex of patients, size of tumor, AFP, HBsAg, differentiation level of tumor and hepatocirrhosis (P > 0.05), but with the capsula of tumor, thrombus in the portal vein (P < 0.05). CONCLUSIONS: DJ-1 gene expression may play an important role in the invasion and metastasis of HCC.