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The efficacy of pH-responsive nanocarriers in targeted cancer therapy hinges on their stability in circulation (pH 7.4) and controlled drug release in the acidic tumor microenvironment (pH ~ 5-6.5). This study elucidates the molecular mechanism of doxorubicin (DOX) interaction with and release from chitosan–Eudragit nanocarriers using integrated all-atom (AA-MD) and coarse-grained (CG-MD) molecular dynamics simulations. Our simulations reveal a stark contrast in behavior between neutral (pH 7) and acidic (pH 5) conditions. At pH 7, the system exhibits remarkable stability, characterized by a lower average RMSD (5.25 nm vs. 5.84 nm at pH 5) and significantly stronger drug-carrier binding. This is evidenced by an approximately three-fold larger drug-nanocarrier contact surface area and a > 50% higher number of stabilizing hydrogen bonds compared to acidic conditions. The protonation of ionizable groups at pH 5 induces electrostatic repulsion, leading to nanocarrier deformation, a drastic reduction in interactions, and ultimately, DOX release. The consistency of these findings across both AA-MD and CG-MD simulations validates the robustness of our models. This work provides a fundamental molecular-level understanding of the pH-responsive behavior of chitosan–Eudragit carriers, confirming their high potential for targeted DOX delivery and establishing a computational framework for the rational design of next-generation smart nanocarriers.

Bone metastasis is considered as a considerable challenge for breast cancer patients. Various in vitro and in vivo models have been developed to examine this occurrence. In vitro models are employed to simulate the intricate tumor microenvironment, investigate the interplay between cells and their adjacent microenvironment, and evaluate the effectiveness of therapeutic interventions for tumors. The endeavor to replicate the latency period of bone metastasis in animal models has presented a challenge, primarily due to the necessity of primary tumor removal and the presence of multiple potential metastatic sites. The utilization of novel bone metastasis models, including three-dimensional (3D) models, has been proposed as a promising approach to overcome the constraints associated with conventional 2D and animal models. However, existing 3D models are limited by various factors, such as irregular cellular proliferation, autofluorescence, and changes in genetic and epigenetic expression. The imperative for the advancement of future applications of 3D models lies in their standardization and automation. The utilization of artificial intelligence exhibits the capability to predict cellular behavior through the examination of substrate materials' chemical composition, geometry, and mechanical performance. The implementation of these algorithms possesses the capability to predict the progression and proliferation of cancer. This paper reviewed the mechanisms of bone metastasis following primary breast cancer. Current models of breast cancer bone metastasis, along with their challenges, as well as the future perspectives of using these models for translational drug development, were discussed.

Radiosensitizers are compounds or nanostructures, which can improve the efficiency of ionizing radiation to kill cells. Radiosensitization increases the susceptibility of cancer cells to radiation‐induced killing, while simultaneously reducing the potentially damaging effect on the cellular structure and function of the surrounding healthy tissues. Therefore, radiosensitizers are therapeutic agents used to boost the effectiveness of radiation treatment. The complexity and heterogeneity of cancer, and the multifactorial nature of its pathophysiology has led to many approaches to treatment. The effectiveness of each approach has been proven to some extent, but no definitive treatment to eradicate cancer has been discovered. The current review discusses a broad range of nano‐radiosensitizers, summarizing possible combinations of radiosensitizing NPs with several other types of cancer therapy options, focusing on the benefits and drawbacks, challenges, and future prospects.

Genetic and agronomic advances consistently lead to an annual increase in global barley yield. Since abiotic stresses (physical environmental factors that negatively affect plant growth) reduce barley yield, it is necessary to predict barley resistance. Artificial intelligence and machine learning (ML) models are new and powerful tools for predicting product resilience. Considering the research gap in the use of molecular markers in predicting abiotic stresses, this paper introduces a new approach called GenPhenML that combines molecular markers and phenotypic traits to predict the resistance of barley genotypes to drought and salinity stresses by ML models. GenPhenML uses feature selection algorithms to determine the most important molecular markers. It then identifies the best model that predicts atmospheric resistance with lower MAE, RMSE, and higher R 2 . The results showed that GenPhenML with a neural network model predicted the salinity stress resistance score with MAE, RMSE and R 2 values of 0.1206, 0.0308 and 0.9995, respectively. Also, the NN model predicted drought stress scores with MAE, RMSE and R 2 values of 0.0727, 0.0105 and 0.9999, respectively. The GenPhenML approach was also used to classify barley genotypes as resistant and stress-sensitive. The results showed that the accuracy, accuracy and F1 score of the proposed approach for salinity and drought stress classification were higher than 97%.

To map the genomic regions and control chlorophyll fluorescence attributes under normal, salinity-, and drought-stress conditions in barley (Hordeum vulgare L.) at the seedling stage, an experiment was conducted in 2019–2020 using 106 F8 lines resulting from the cross between Badia × Kavir. Initially, the different chlorophyll fluorescence parameters were evaluated. Under drought stress, the highest decrease was related to REo/CSm (59.56%), and the highest increase was related to dV/dto (77.17%). Also, under salinity stress, the highest decrease was related to Fv/Fo (59.56%), and the highest increase was related to DIo/RC (77.17%). Linkage maps were prepared using 152 SSR polymorphic markers, 72 ISSR alleles, 7 IRAP alleles, 29 CAAT alleles, 27 Scot alleles, and 15 iPBS alleles. The obtained map accounted for 999.2 centi-Morgans (cM) of the barley genome length (92% of the whole barley genome). The results indicated the importance of chromosomes 3, 2, and 7 in controlling ABS/CSm, Area, ETo/CSm, Fm, Fv, and ETo/RC under drought stress. qEToRCD-7, as a major QTL, controlled 18.3% of ETo/RC phenotypic variation under drought stress. Under salinity stress, the regions of chromosomes 2 and 7 (102 cM and 126 cM) controlled the parameters ABS/CSo, Fm, Fo, Fv, TRo/SCo, Area, ETo/CSm, and ETo/CSo. The results showed that chlorophyll fluorescence is an important parameter in the study of drought and salinity effects on barley. This is the first report of the investigation of changes in the genetic structure of quantitative genes controlling the fluorescence parameters associated with barley response to drought and salinity stresses in the Iranian barley RILs population. According to the obtained results, it is possible to use HVPLASC1B and EBmac0713 in normal conditions, ISSR21-2 and ISSR30-4 in drought conditions, and Bmac0047, Scot5-B, CAAT6-C, and ISSR30iPBS2076-4 in saline stress conditions to select genotypes with higher photosynthetic capacity in marker-assisted selection programs.

Background Malaria is a major global health challenge, and for the elimination and eradication of this disease, transmission-blocking vaccines (TBVs) are a priority. Plasmodium falciparum Generative Cell Specific 1 (PfGCS1), a promising TBV candidate, is essential for gamete fertilization. The HAP2-GCS1 domain of this antigen as well as its cd loop could induce antibodies that partially inhibit transmission of P. falciparum. Methods In the current study, a new synthetic fusion antigen containing cd loop and HAP2-GCS1 domain (cd-HAP) of PfGCS1 was evaluated as a transmission blocking vaccine candidate. Initially, the profile of naturally acquired IgG antibodies to the cd-HAP antigen was analysed in Iranian individuals infected with P. falciparum , to confirm that this new fusion protein has the appropriate structure containing common epitopes with the native form of PfGCS1. Then, the immunogenicity of cd-HAP was evaluated in BALB/c mice, using different adjuvant systems such as CpG, MPL, QS-21, and a combination of them (CMQ). Furthermore, the blocking efficacy of polyclonal antibodies induced against these formulations was also assessed by oocyst intensity and infection prevalence in the Standard Membrane Feeding Assay (SMFA). Results The naturally acquired antibodies (dominantly IgG1 and IgG3 subclasses) induced in P. falciparum -infected individuals could recognize the cd-HAP antigen which implies that the new fusion protein has a proper conformation that mimics the native structure of PfGCS1. Concerning the immunogenicity of cd-HAP antigen, the highest IgG levels and titers, by a Th1-type immune profile, and elevated antibody avidity were induced in mice immunized with the cd-HAP antigen formulated with a combination of adjuvants ( P  < 0.0001). Additionally, cytokine profiling of the immunized mice displayed that a high level of IFN-γ response, a Th1-type immune response, was produced by splenocytes from immunized mice that received cd-HAP antigen in combination with CMQ adjuvants ( P  < 0.0001). This formulation of cd-HAP antigen with CMQ adjuvants could reduce oocyst intensity and infection prevalence by 82%, evidenced by the SMFA and hold significant implications for future malaria vaccine development. Conclusion Altogether, the results showed that cd-HAP antigen formulated with a combination of the adjuvants (CMQ), could be a promising formulation to develop a PfGCS1-based transmission-blocking vaccine.

The present systematic review was done to investigate the possible application of Extracellular vesicles (EVs) in the diagnosis, prognosis, and treatment response monitoring of gliomas using available literature to wrap up the final applicable conclusion in this regard. we searched PubMed/MEDLINE, Scopus, and ISI Web of Science databases. Authors evaluated the quality of the included studies by the QUADAS-2 tool. In total, 2037 published datasets were retrieved through systematic search. Upon screening for eligibility, 35 datasets were determined as eligible. Exosome was the EV-subtype described in the majority of studies, and most datasets used serum as the primary EVs isolation source. EVs isolation was primarily conducted by ultracentrifugation. 31 datasets reported that EVs hold considerable potential for being used in diagnostics, with the majority reporting different types of miRNAs as biomarkers. Besides, 8 datasets reported that EVs could be a potential source of prognostic biomarkers. And finally, 3 datasets reported that EVs might be a reliable strategy for monitoring therapy response in glioma patients. According to the findings of the current systematic review, it seems that miR-301, miR-21, and HOTAIR had the highest diagnostic accuracy. However, heterogeneous and limited evidence regarding prognosis and treatment response monitoring precludes us from drawing a practical conclusion regarding EVs.Graphic abstract

Seed yield is influenced by the combined effects of genes, including additive and non-additive interactions. Therefore, accurately predicting seed yield holds significant importance in rapeseed breeding. Nonetheless, limited information exists regarding yield estimation for canola using neural networks. This study employs multi-layer perceptron (MLP) neural network, radial basis function neural network and support vector machine, to forecast rapeseed yield. The models are trained using phenological, morphological, yield and yield-related data, as well as molecular marker information from 8 genotypes and 56 hybrids. Comparative analysis of the models reveals that the MLP model effectively forecasts hybrid yield with root mean square error (RMSE), mean absolute error (MAE) and coefficient of determination (R 2 ) values of 226, 183, and 92%, respectively. Among the 40 primers examined, the ISJ10 primer demonstrates superior discriminatory power compared to others. The use of molecular and phenotypic data as inputs in the model highlights the MLP model’s superiority, presenting lower RMSE and MAE values, along with a higher R 2 , compared to direct crosses in predicting the performance of reciprocal crosses. The proposed neural network model enables performance estimation of hybrids prior to crossing parent studied, thereby enabling spring rapeseed breeders to focus on the most promising hybrids.

To identify the genomic regions for the physiological and morphological traits of barley genotypes under normal salinity and drought, a set of 103 recombinant inbred line (RIL) populations, developed between Badia and Kavir crosses, was evaluated under phytotron conditions in a completely randomized design in 2019. Linkage maps were prepared using 152 SSR markers, 72 ISSR, 7 IRAP, 29 CAAT, 27 SCoT, and 15 iPBS alleles. The markers were assigned to seven barley chromosomes and covered 999.29 centimorgans (cM) of the barley genome. In addition, composite interval mapping showed 8, 9, and 26 quantitative trait loci (QTLs) under normal, drought, and salinity stress conditions, respectively. Our results indicate the importance of chromosomes 1, 4, 5, and 7 in salinity stress. These regions were involved in genes controlling stomata length (LR), leaf number (LN), leaf weight (LW), and genetic score (SCR). Three major stable pleiotropic QTLs (i.e., qSCS-1, qRLS-1, and qLNN-1) were associated with SCR, root length (RL), and root number (RN) in both treatments (i.e., normal and salinity), and two major stable pleiotropic QTLs (i.e., qSNN-3 and qLWS-3) associated with the stomata number (SN) and LW appeared to be promising for marker-assisted selection (MAS). Two major-effect QTLs (i.e., SCot8-B-CAAT5-D and HVM54-Bmag0571) on chromosomes 1 and 2 were characterized for their positive allele effect, which can be used to develop barley varieties concerning drought conditions. The new alleles (i.e., qLWS-4a, qSLS-4, qLNS-7b, qSCS-7, and qLNS-7a) identified in this study are useful in pyramiding elite alleles for molecular breeding and marker assisted selection for improving salinity tolerance in barley.

Abiotic stresses cause a significant decrease in productivity and growth in agricultural products, especially barley. Breeding has been considered to create resistance against abiotic stresses. Pyramiding genes for tolerance to abiotic stresses through selection based on molecular markers connected to Mega MQTLs of abiotic tolerance can be one of the ways to reach Golden Barley. In this study, 1162 original QTLs controlling 116 traits tolerant to abiotic stresses were gathered from previous research and mapped from various populations. A consensus genetic map was made, including AFLP, SSR, RFLP, RAPD, SAP, DArT, EST, CAPS, STS, RGA, IFLP, and SNP markers based on two genetic linkage maps and 26 individual linkage maps. Individual genetic maps were created by integrating individual QTL studies into the pre-consensus map. The consensus map covered a total length of 2124.43 cM with an average distance of 0.25 cM between markers. In this study, 585 QTLs and 191 effective genes related to tolerance to abiotic stresses were identified in MQTLs. The most overlapping QTLs related to tolerance to abiotic stresses were observed in MQTL6.3. Furthermore, three MegaMQTL were identified, which explained more than 30% of the phenotypic variation. MQTLs, candidate genes, and linked molecular markers identified are essential in barley breeding and breeding programs to develop produce cultivars resistant to abiotic stresses.