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Background: Deoxyribozymes or DNAzymes represent artificial short DNA sequences bearing many catalytic properties. In particular, DNAzymes able to cleave RNA sequences have a huge potential in gene therapy and sequence-specific analytic detection of disease markers. This activity is provided by catalytic cores able to perform site-specific hydrolysis of the phosphodiester bond of an RNA substrate. However, the vast majority of existing DNAzyme catalytic cores have low efficacy in in vivo experiments, whereas SELEX based on in vitro screening offers long and expensive selection cycle with the average success rate of ~ 30%, moreover not allowing the direct selection of chemically modified DNAzymes, which were previously shown to demonstrate higher activity in vivo. Therefore, there is a huge need in in silico approach for exploratory analysis of RNA-cleaving DNAzyme cores to drastically ease the discovery of novel catalytic cores with superior activities. Results: In this work, we develop a machine learning based open-source platform SequenceCraft allowing experimental scientists to perform DNAzyme exploratory analysis via quantitative observed rate constant (kobs) estimation as well as statistical and clustering data analysis. This became possible with the development of a unique curated database of > 350 RNA-cleaving catalytic cores, property-based sequence representations allowing to work with both conventional and chemically modified nucleotides, and optimized kobs predicting algorithm achieving Q2 > 0.9 on experimental data published to date. Conclusions: This work represents a significant advancement in DNAzyme research, providing a tool for more efficient discovery of RNA-cleaving DNAzymes. The SequenceCraft platform offers an in silico alternative to traditional experimental approaches, potentially accelerating the development of DNAzymes. [ABSTRACT FROM AUTHOR]

Abstract Background The Phlebotomus papatasi salivary protein PpSP15 was shown to protect mice against Leishmania major, suggesting that incorporation of salivary molecules in multi-component vaccines may be a viable strategy for anti-Leishmania vaccines. Methods Here, we investigated PpSP15 predicted amino acid sequence variability and mRNA profile of P. papatasi field populations from the Middle East. In addition, predicted MHC class II T-cell epitopes were obtained and compared to areas of amino acid sequence variability within the secreted protein. Results The analysis of PpSP15 expression from field populations revealed significant intra- and interpopulation variation.. In spite of the variability detected for P. papatasi populations, common epitopes for MHC class II binding are still present and may potentially be used to boost the response against Le. major infections. Conclusions Conserved epitopes of PpSP15 could potentially be used in the development of a salivary gland antigen-based vaccine.

This study has been performed at the Alton-Kobry region to indicate the suitability of the ground water which is important source for domestic uses. The samples taken from (19) wells at different locations and tested at laboratories of north gas company in Nov.-2010.The tests included (electrical conductivity, total dissolved solids, pH-value, concentration of some anions) were all conducted for each sample. The results revealed that surveyed ground water were free from pollution and almost suitable for domestic use. Some investigated parameters appeared to be near the recommended limits set by Iraqi standards, while others exceed the recommended values that the chemical treatments are necessary.

For studying the physiological response of two different Dianthus caryophyllus cultivars on Cd stress, pot experiment was carried out to measure proline and glutathione in leaves, five types of organic acids in root exudates (oxalic acid, malic acid, acetic acid, tartaric acid, citric acid), soluble sugars and free amino acids, root length, root surface area, root volume, root projected area and Cadmium content in soil, plant roots and aboveground. According to the effects of cadmium stress on the physiological and biochemical characteristics of two Dianthus caryophyllus, the results showed that: the growth of two cultivars are affected, “Master” and “Xiao Yan” are manifested as plant height, leaf, flower buds and biomass decreased, but the “Master” by the stronger inhibitory effect. The root length, total root surface area, total root projected area, and average root diameter of the “Master” increased under cadmium stress, but the root volume decreased. However, the root length, total root surface area, root volume, and root projected area of the “Xiao Yan” under cadmium stress decreased, while the average root diameter increased. The glutathione in the leaves of the two cultivars decreased, the proline content of the leaves of the “Xiao Yan” increased, while that of the “Master” decreased. In the root exudates, the free amino acid content of the two cultivars are reduced, and the secretion of organic acids is also inhibited (except for the citric acid secreted in the “Master”), while the soluble sugar content in the root exudates is expressed as “Xiao Yan” increased, and the “Master” decreased. According to the physiological response to the two cultivars under cadmium stress, the “Xiao Yan” is more suitable for soil restoration in the mining area of Lanping area.

The integration of textile-based flexible sensors and electronic devices has accelerated the development of wearable textiles for posture monitoring. The complexity of the processes required to create a complete monitoring product is currently reflected in three main areas. The first is the sensor production process, which is complex. Second, the integration of the sensor into the garment requires gluing or stitching. Finally, the production of the base garment requires cutting and sewing. These processes deteriorate the user experience and hinder the commercial mass production of wearable textiles. In this paper, we knitted a one-piece seamless knitted vest (OSKV) utilizing the one-piece seamless knitting technique and positioned an embedded needle sensing zone (EHSZ) with good textile properties and electrical performance for monitoring human shoulder activity. The EHSZ was knitted together with the OSKV, eliminating the need for an integration process. The EHSZ exhibited good sensitivity (GF = 2.23), low hysteresis (0.29 s), a large stretch range (200%), and excellent stability (over 300 cycles), satisfying the requirement to capture a wide range of deformation signals caused by human shoulder movements. The OSKV described the common vest process structure without the stitching process. Furthermore, OSKV fulfilled the demand for seamless and trace-free monitoring while effortlessly and aesthetically satisfying the knitting efficiency of commercial garments.

Objective: Mesenchymal stem cells (MSCs) derived exosomes offers several advantages as a cell-free therapeutic agents. In this study, Umbilical cord mesenchymal stem cells exosomes (UC-MSCs-exos) effects on oral squamous cell carcinoma (OSCC) cell line was evaluated., Methods: UC-MSCs-exos were isolated and co-cultured with OSCC cells and their impact on OSCC was explored by various tests. Comet assay and western blot for cleaved caspase-3 and immunocytochemistry for caspase-8 were used for apoptosis assessment. HO-1 and Nrf2 were used to determine antioxidant levels. Tumor necrosis factor-α and interleukin-6 were assessed as inflammatory biomarkers. HOX transcript antisense intergenic long noncoding RNA (HOTAIR) expression was also evaluated., Results: In a dose-dependent manner, UC-MSCs-exos reduced the levels of pro-inflammatory cytokines (IL-6 and TNF-α) and induced apoptosis of OSCC in vitro. Meanwhile, we found that UC-MSCs-exos downregulate HOTAIR., Conclusion: UC-MSCs-exos conferred a suppressive role on OSCC in vitro, highlighting a promising therapeutic role. However, the exact potentially involved molecules and molecular mechanisms need to be investigated in further studies.

Organic chemistry has seen colossal progress due to machine learning (ML). However, the translation of artificial intelligence (AI) into materials science is challenging, where biological behavior prediction becomes even more complicated. Nanotoxicity is a critical parameter that describes their interaction with the living organisms screened in every bio-related research. To prevent excessive experiments, such properties have to be pre-evaluated. Several existing ML models partially fulfill the gap by predicting whether a nanomaterial is toxic or not. Yet, this binary categorization neglects the concentration dependencies crucial for experimental scientists. Here, an ML-based approach is proposed to the quantitative prediction of inorganic nanomaterial cytotoxicity achieving the precision expressed by 10-fold cross-validation (CV) Q 2  = 0.86 with the root mean squared error (RMSE) of 12.2% obtained by the correlation-based feature selection and grid search-based model hyperparameters optimization. To provide further model flexibility, quantitative atom property-based nanomaterial descriptors are introduced allowing the model to extrapolate on unseen samples. Feature importance is calculated to find an interpretable model with optimal decision-making. These findings allow experimental scientists to perform primary in silico candidate screening and minimize the number of excessive, labor-intensive experiments enabling the rapid development of nanomaterials for medicinal purposes., (© 2023 Wiley-VCH GmbH.)