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Cancer remains a prominent cause to life expectancy, and targeted cancer therapy stands as a pivotal approach in contemporary therapy. Calcium (Ca2+) signalling plays a multifaceted role in cancer progression, such as proliferation, invasion and distant metastasis. Otherwise, it also exerts an important influence on the efficacy of clinical treatment, including cancer therapy resistance. In this review we discuss the role of the L‐type calcium channel CaV1.3 (calcium voltage‐gated channel subunit alpha1 D) in different types of cancers, highlighting its potential as a therapeutic target for certain cancer types. The development of selective blockers of the CaV1.3 channel has been of great interest and is expected to be a new option for the treatment of cancers such as prostate cancer and endometrial cancer. We present the pharmacological properties of CaV1.3 and the current status of selective blocker development, and analyse the challenges and possible directions for breakthroughs in the development of tailored medicines. [ABSTRACT FROM AUTHOR]

In order to control the grassland ecological environment, an application method of multisource data fusion technology in the construction of land ecological index is proposed. Due to the high requirements for grassland environmental monitoring, the use of traditional technologies to monitor grassland environmental conditions lacks certain effectiveness, has high investment costs, and consumes a lot of manpower and material resources. The use of sensors to dynamically monitor the grassland environment is conducive to monitoring the environment from a scientific and technological level. By understanding the fusion principle and process of three fusion methods, adaptive weighted average, BP neural network, and D-S evidence theory, the construction of Bashang grassland ecological energy big data platform based on multisource data fusion is proposed. A two-level data fusion model based on grassland environmental monitoring is proposed. Several environmental parameters in the experimental environment were monitored, and the validity of the two-level fusion model was verified by two evaluation indicators, the mean absolute percentage error and the corrosion error. This suggests that a combination of BP neural network and D-S proof theory improves system performance. It provides the possibility for more comprehensive monitoring of grassland ecological environment in the future. [ABSTRACT FROM AUTHOR]

As an effective network protection method, computer user behavior anomaly detection can detect unknown attack behaviors. In order to detect user behavior anomalies more efficiently, the authors propose a computer user behavior anomaly detection model based on the K-means algorithm. According to the actual characteristics of single-user behavior, the algorithm uses sliding time window to define transactions and uses the first location strategy to mine behavior patterns. On this basis, the fault-tolerant mode is adopted to compare the current behavior mode with the normal behavior mode, and the anomaly detection results are obtained. Experiments show that using data mining technology, the association rules of user commands, and the mining of sequence patterns, it can effectively discover the user's behavior pattern, and using sequence matching algorithms such as recursive correlation functions and calculating the similarity between the user's historical pattern and the current pattern, it provides the possibility to accurately judge user behavior. The following conclusions are obtained through experiments: the model training time is short, the accuracy is high, and it has certain robustness. [ABSTRACT FROM AUTHOR]

A key enzyme in human immunodeficiency virus type 1 (HIV-1) life cycle, integrase (IN) aids the integration of viral DNA into the host DNA, which has become an ideal target for the development of anti-HIV drugs. A total of 1785 potential HIV-1 IN inhibitors were collected from the databases of ChEMBL, Binding Database, DrugBank, and PubMed, as well as from 40 references. The database was divided into the training set and test set by random sampling. By exploring the correlation between molecular descriptors and inhibitory activity, it is found that the classification and specific activity data of inhibitors can be more accurately predicted by the combination of molecular descriptors and molecular fingerprints. The calculation of molecular fingerprint descriptor provides the additional substructure information to improve the prediction ability. Based on the training set, two machine learning methods, the recursive partition (RP) and naive Bayes (NB) models, were used to build the classifiers of HIV-1 IN inhibitors. Through the test set verification, the RP technique accurately predicted 82.5% inhibitors and 86.3% noninhibitors. The NB model predicted 88.3% inhibitors and 87.2% noninhibitors with correlation coefficient of 85.2%. The results show that the prediction performance of NB model is slightly better than that of RP, and the key molecular segments are also obtained. Additionally, CoMFA and CoMSIA models with good activity prediction ability both were constructed by exploring the structure-activity relationship, which is helpful for the design and optimization of HIV-1 IN inhibitors. [ABSTRACT FROM AUTHOR]

Zeolitic imidazolate framework‐8 (ZIF‐8) loading rhodamine‐B (ZIF‐8@rhodamine‐B) nanocomposites was proposed and used as ratiometric fluorescent sensor to detect copper(II) ion (Cu2+). Scanning electron microscopy, Fourier transform infrared spectroscopy, X‐ray powder diffraction, nitrogen adsorption/desorption isotherms and fluorescence emission spectroscopy were employed to characterize the ZIF‐8@rhodamine‐B nanocomposites. The results showed the rhodamine‐B was successfully assembled on ZIF‐8 based on the π‐π interaction and the hydrogen bond between the nitrogen atom of ZIF‐8 and –COOH of rhodamine‐B. The as‐obtained ZIF‐8@rhodamine‐B nanocomposites were octahedron with size about 150–200 nm, had good water dispersion, and exhibited the characteristic fluorescence emission of ZIF‐8 at 335 nm and rhodamine‐B at 575 nm. The Cu2+ could quench fluorescence of ZIF‐8 rather than rhodamine‐B. The ZIF‐8 not only acted as the template to assemble rhodamine‐B, but also was employed as the signal fluorescence together with the fluorescence of rhodamine‐B as the reference to construct a novel ratiometric fluorescent sensor to detect Cu2+. The resulted ZIF‐8@rhodamine‐B nanocomposite fluorescence probe showed good linear range (68.4 nM to 125 μM) with a low detection limit (22.8 nM) for Cu2+ combined with good sensitivity and selectivity. The work also provides a better way to design ratiometric fluorescent sensors from ZIF‐8 and other fluorescent molecules. [ABSTRACT FROM AUTHOR]

Abstract: Background: Cachexia is a multifactorial metabolic syndrome with high morbidity and mortality in patients with advanced cancer. The diagnosis of cancer cachexia depends on objective measures of clinical symptoms and a history of weight loss, which lag behind disease progression and have limited utility for the early diagnosis of cancer cachexia. In this study, we performed a nuclear magnetic resonance‐based metabolomics analysis to reveal the metabolic profile of cancer cachexia and establish a diagnostic model. Methods: Eighty‐four cancer cachexia patients, 33 pre‐cachectic patients, 105 weight‐stable cancer patients, and 74 healthy controls were included in the training and validation sets. Comparative analysis was used to elucidate the distinct metabolites of cancer cachexia, while metabolic pathway analysis was employed to elucidate reprogramming pathways. Random forest, logistic regression, and receiver operating characteristic analyses were used to select and validate the biomarker metabolites and establish a diagnostic model. Results: Forty‐six cancer cachexia patients, 22 pre‐cachectic patients, 68 weight‐stable cancer patients, and 48 healthy controls were included in the training set, and 38 cancer cachexia patients, 11 pre‐cachectic patients, 37 weight‐stable cancer patients, and 26 healthy controls were included in the validation set. All four groups were age‐matched and sex‐matched in the training set. Metabolomics analysis showed a clear separation of the four groups. Overall, 45 metabolites and 18 metabolic pathways were associated with cancer cachexia. Using random forest analysis, 15 of these metabolites were identified as highly discriminating between disease states. Logistic regression and receiver operating characteristic analyses were used to create a distinct diagnostic model with an area under the curve of 0.991 based on three metabolites. The diagnostic equation was Logit(P) = −400.53 – 481.88 × log(Carnosine) −239.02 × log(Leucine) + 383.92 × log(Phenyl acetate), and the result showed 94.64% accuracy in the validation set. Conclusions: This metabolomics study revealed a distinct metabolic profile of cancer cachexia and established and validated a diagnostic model. This research provided a feasible diagnostic tool for identifying at‐risk populations through the detection of serum metabolites. [ABSTRACT FROM AUTHOR]

Abstract: A novel ratiometric fluorescence nanosensor for superoxide anion (O2•−) detection was designed with gold nanoparticles‐bovine serum albumin (AuNPs‐BSA)@terbium/guanosine monophosphate disodium (Tb/GMP) nanoscale coordination polymers (NCPs) (AuNPs‐BSA@Tb/GMP NCPs). The abundant hydroxyl and amino groups of AuNPs‐BSA acted as binding points for the self‐assembly of Tb3+ and GMP to form core‐shell AuNPs‐BSA@Tb/GMP NCP nanosensors. The obtained probe exhibited the characteristic fluorescence emission of both AuNPs‐BSA and Tb/GMP NCPs. The AuNPs‐BSA not only acted as a template to accelerate the growth of Tb/GMP NCPs, but also could be used as the reference fluorescence for the detection of O2•−. The resulting AuNPs‐BSA@Tb/GMP NCP ratiometric fluorescence nanosensor for the detection of O2•− demonstrated high sensitivity and selectivity with a wide linear response range (14 nM–10 μM) and a low detection limit (4.7 nM). [ABSTRACT FROM AUTHOR]

Cotton provides us the most important natural fibre. High fibre quality is the major goal of cotton breeding, and introducing genes conferring longer, finer and stronger fibre from Gossypium barbadense to Gossypium hirsutum is an important breeding strategy. We previously analysed the G. barbadense fibre development mechanism by gene expression profiling and found two homoeologous fibre-specific α-expansins from G. barbadense, Gb EXPA2 and Gb EXPATR. Gb EXPA2 (from the DT genome) is a classical α-expansin, while its homoeolog, Gb EXPATR ( AT genome), encodes a truncated protein lacking the normal C-terminal polysaccharide-binding domain of other α-expansins and is specifically expressed in G. barbadense. Silencing EXPA in G. hirsutum induced shorter fibres with thicker cell walls. Gb EXPA2 overexpression in G. hirsutum had no effect on mature fibre length, but produced fibres with a slightly thicker wall and increased crystalline cellulose content. Interestingly, Gb EXPATR overexpression resulted in longer, finer and stronger fibres coupled with significantly thinner cell walls. The longer and thinner fibre was associated with lower expression of a number of secondary wall-associated genes, especially chitinase-like genes, and walls with lower cellulose levels but higher noncellulosic polysaccharides which advocated that a delay in the transition to secondary wall synthesis might be responsible for better fibre. In conclusion, we propose that α-expansins play a critical role in fibre development by loosening the cell wall; furthermore, a truncated form, Gb EXPATR, has a more dramatic effect through reorganizing secondary wall synthesis and metabolism and should be a candidate gene for developing G. hirsutum cultivars with superior fibre quality. [ABSTRACT FROM AUTHOR]

Fiber elongation is the key determinant of fiber quality and output in cotton ( Gossypium hirsutum). Although expression profiling and functional genomics provide some data, the mechanism of fiber development is still not well understood., Here, a gene encoding a calcium sensor, Gh Ca M7, was isolated based on its high expression level relative to other Gh Ca Ms in fiber cells at the fast elongation stage. The level of expression of Gh Ca M7 in the wild-type and the fuzzless/lintless mutant correspond to the presence and absence, respectively, of fiber initials., Overexpressing Gh Ca M7 promotes early fiber elongation, whereas Gh Ca M7 suppression by RNAi delays fiber initiation and inhibits fiber elongation. Reactive oxygen species ( ROS) play important roles in early fiber development. ROS induced by exogenous hydrogen peroxide ( H2 O2) and Ca2+ starvation promotes early fiber elongation. Gh Ca M7 overexpression fiber cells show increased ROS concentrations compared with the wild-type, while Gh Ca M7 RNAi fiber cells have reduced concentrations. Furthermore, we show that H2 O2 enhances Ca2+ influx into the fiber and feedback-regulates the expression of GhCaM7., We conclude that Gh Ca M7, Ca2+ and ROS are three important regulators involved in early fiber elongation. Gh Ca M7 might modulate ROS production and act as a molecular link between Ca2+ and ROS signal pathways in early fiber development. [ABSTRACT FROM AUTHOR]

In the binuclear title complex, [Dy(2)(C(7)H(3)N(2)O(6))(6)(C(12)H(8)N(2))(2)], the Dy(III) ions exhibit a distorted monocapped square-anti-prismatic geometry and are coordinated by seven O atoms of four 3,5-dinitrobenzoate (DNBA) anions and two N atoms of a phenanthroline ligand. The carboxylate groups of the DNBA anions exhibit three coordination modes: bidentate chelating, bidentate chelating-bridging and tridentate chelating-bridging. The center of the mol-ecule is located on a crystallographic center of inversion.