The management of renal cell carcinoma (RCC) has been revolutionized over the past two decades with several practice-changing treatments. Treatment for RCC often requires a multimodal approach: Local treatment, such as surgery or ablation, is typically recommended for patients with localized tumors, while stage IV cancers often require both local and systemic therapy. The treatment of advanced RCC heavily relies on immunotherapy and targeted therapy, which are highly contingent upon histological subtypes. Despite years of research on biomarkers for RCC, the standard of care is to choose systemic therapy based on the risk profile according to the International Metastatic RCC Database Consortium and Memorial Sloan Kettering Cancer Centre models. However, many questions still need to be answered. Should we consider metastatic sites when deciding on treatment options for metastatic RCC? How do we choose between dual immunotherapy and combinations of immunotherapy and tyrosine kinase inhibitors? This review article aims to answer these unresolved questions surrounding the concept of personalized medicine. Full article

The evolutionarily conserved nonsense-mediated mRNA decay (NMD) pathway is a quality control mechanism that degrades aberrant mRNA containing one or more premature termination codons (PTCs). Recent discoveries indicate that NMD also differentially regulates mRNA from wild-type protein-coding genes despite lacking PTCs. Together with studies showing that NMD is involved in development and adaptive responses that influence health and longevity, these findings point to an expanded role of NMD that adds a new layer of complexity in the post-transcriptional regulation of gene expression. However, the extent of its control, whether different types of NMD play different roles, and the resulting physiological outcomes remain unclear and need further elucidation. Here, we review different branches of NMD and what is known of the physiological outcomes associated with this type of regulation. We identify significant gaps in the understanding of this process and the utility of genetic tools in accelerating progress in this area. Full article

This systematic review evaluates the clinical outcomes and molecular predictors of response to pembrolizumab in patients with advanced and metastatic cervical cancer. We adhered to the PRISMA guidelines for systematic reviews, conducting a database search in PubMed, Scopus, and Embase. The eligibility criteria centered on clinical outcomes, including the overall survival (OS), progression-free survival (PFS), and immune-related biomarkers post-pembrolizumab therapy. We included both prospective and retrospective studies that detailed clinical outcomes and molecular characteristics predictive of therapeutic response. Our search yielded six studies involving 846 patients treated with pembrolizumab from 2017 to 2022. The meta-analysis of these studies showed that pembrolizumab, used as monotherapy or in combination with chemotherapy, extended the OS by a weighted median of 10.35 months and the PFS by 8.50 months. The treatment demonstrated a pooled objective response rate (ORR) of 22.39%, although the I² test result of 67.49% showed a high heterogeneity among the studies. Notably, patients with high PD-L1 expression (CPS ≥ 10) experienced improved outcomes in terms of the PFS and OS. The most common complications were fatigue, diarrhea, and immune-related adverse events. Pembrolizumab significantly enhances clinical outcomes in metastatic cervical cancer, particularly among patients with high PD-L1 expression. The drug maintains a good safety profile, reinforcing its treatment potential for patients with advanced and metastatic cervical cancer. Future studies should explore long-term effects and strategies to integrate pembrolizumab optimally into current treatment regimens, aiming to maximize patient benefits and effectively manage side effects. Full article

Osteopontin (OPN) is a glycoprotein involved in Th1 and Th17 differentiation, and inflammation and tissue remodeling. OPN is a biomarker of disease activity in patients with autoimmune inflammatory conditions. This study aimed to assess the diagnostic and prognostic value of OPN in interstitial lung diseases (ILDs). Between May 2016 and October 2019, 344 patients with ILD were recruited at the Hospital Universitario Marqués de Valdecilla (Spain) and were prospectively followed-up. This study involved the determination of OPN serum levels by ELISA and OPN RNA expression quantified using qPCR. Six genetic polymorphisms in OPN (rs28357094, rs2853749, rs2853750, rs11728697, rs7695531, and rs1126616) were genotyped using TaqMan assays. OPN serum levels were also assessed in 140 healthy controls. OPN serum levels (median [interquartile range]) were significantly higher in ILD patients than in controls (1.05 [0.75–1.51] ng/mL versus 0.81 [0.65–0.98] ng/mL in healthy controls; p < 0.01). OPN serum levels were inversely correlated with the forced vital capacity. OPN serum levels were also higher in ILD patients who died or underwent lung transplantation when compared with the remaining ILD patients (1.15 [0.80–1.72] ng/mL versus 0.99 [0.66–1.32] ng/mL; p = 0.05). Survival worsened in ILD patients with OPN > 1.03 ng/mL at 1, 3, and 5 years. No statistically significant differences in the genetic frequencies of OPN polymorphisms or the RNA expression were found among the different ILD groups. Elevated levels of OPN in the serum may be a useful indicator in identifying patients with ILD who are more likely to experience poor outcomes. Full article

Background: Alzheimer’s disease (AD) presents a significant global health concern, characterized by neurodegeneration and cognitive decline. Neuroinflammation is a crucial factor in AD development and progression, yet effective pharmacotherapy remains elusive. Sulforaphane (SFN), derived from cruciferous vegetables and mainly from broccoli, has shown a promising effect via in vitro and in vivo studies as a potential treatment for AD. This study aims to investigate the possible prophylactic mechanisms of SFN against prefrontal cortex (PFC)-related recognition memory impairment induced by lipopolysaccharide (LPS) administration. Methodology: Thirty-six Swiss (SWR/J) mice weighing 18–25 g were divided into three groups (n = 12 per group): a control group (vehicle), an LPS group (0.75 mg/kg of LPS), and an LPS + SFN group (25 mg/kg of SFN). The total duration of the study was 3 weeks, during which mice underwent treatments for the initial 2 weeks, with daily monitoring of body weight and temperature. Behavioral assessments via novel object recognition (NOR) and temporal order recognition (TOR) tasks were conducted in the final week of the study. Inflammatory markers (IL-6 and TNF), antioxidant enzymes (SOD, GSH, and CAT), and pro-oxidant (MDA) level, in addition to acetylcholine esterase (AChE) activity and active (caspase-3) and phosphorylated (AMPK) levels, were evaluated. Further, PFC neuronal degeneration, Aβ content, and microglial activation were also examined using H&E, Congo red staining, and Iba1 immunohistochemistry, respectively. Results: SFN pretreatment significantly improved recognition memory performance during the NOR and TOR tests. Moreover, SFN was protected from neuroinflammation and oxidative stress as well as neurodegeneration, Aβ accumulation, and microglial hyperactivity. Conclusion: The obtained results suggested that SFN has a potential protective property to mitigate the behavioral and biochemical impairments induced by chronic LPS administration and suggested to be via an AMPK/caspase-3-dependent manner. Full article

The removal of dental plaque from the gingival margins of the teeth is essential to maintaining periodontal health. Whilst it has been established that electric toothbrushes demonstrate a greater ability to remove plaque, no systematic review has specifically investigated which technology is better for removing plaque from the interdental tooth surfaces, where plaque control may be more difficult. Three databases were searched until October 2023: MEDLINE and DOSSS via EBSCOhost and Embase. Data extraction was carried out on studies which met the inclusion criteria, and a risk of bias assessment was completed. The study findings were combined via a narrative synthesis and a meta-analysis where appropriate. A total of 77 studies were found, out of which 14 were selected and included in the analysis. The mean difference in interproximal plaque reduction, measured using the Rustogi Modified Naval Plaque Index (RMNPI) at 8 weeks, was 0.09 (p < 0.00001) in favor of the oscillating–rotating toothbrush. At 6 and 12 weeks, the mean difference in plaque reduction (RMNPI) was 0.05 (p = 0.0008) and 0.04 (p = 0.0001) in favor of the oscillating–rotating toothbrush, respectively. The studies show a tendency for oscillating–rotating toothbrushes to remove more interproximal plaque than oscillating toothbrushes, especially in a short time (8 weeks). Full article

Non-specific low back pain (NSLBP) in children and adolescents has increased in recent years, and the evidence of the physiotherapy interventions in back care needs to be updated. Our main goal was to quantify the effects of preventive physiotherapy interventions on improving behavior and knowledge related to back care and prevention of NSLBP in children and adolescents. Based on two previous meta-analyses, Cochrane Library, MEDLINE, PEDro, Web of Science, LILACS, IBECS, PsycINFO, and IME databases and several journals were searched. Two researchers independently extracted data and assessed the risk of bias in the studies using the RoB2 tool. Data were described according to PRISMA guidelines. A total of 24 studies (28 reports) were included. In the posttest, the behavior variable obtained an overall effect size of d₊ = 1.48 (95%CI: 0.40 to 2.56), and the knowledge variable obtained an effect size of d₊ = 1.41 (95%CI: 1.05 to 1.76). Physiotherapy has demonstrated beneficial impacts on behavior and knowledge concerning back care and to prevent NSLBP in children and adolescents. Interventions focusing on postural hygiene and exercise should be preferred, especially those that are shorter in number of weeks, more intense, and incorporate as many intervention hours as possible. Full article

Recently, community detection has emerged as a prominent research area in the analysis of complex network structures. Community detection models based on non-negative matrix factorization (NMF) are shallow and fail to fully discover the internal structure of complex networks. Thus, this article introduces a novel constrained symmetric non-negative matrix factorization with deep autoencoders (CSDNMF) as a solution to this issue. The model possesses the following advantages: (1) By integrating a deep autoencoder to discern the latent attributes bridging the original network and community assignments, it adeptly captures hierarchical information. (2) Introducing a graph regularizer facilitates a thorough comprehension of the community structure inherent within the target network. (3) By integrating a symmetry regularizer, the model’s capacity to learn undirected networks is augmented, thereby facilitating the precise detection of symmetry within the target network. The proposed CSDNMF model exhibits superior performance in community detection when compared to state-of-the-art models, as demonstrated by eight experimental results conducted on real-world networks. Full article

This study theoretically investigates the temperature and velocity spatial distributions in the flow of a copper–water nanofluid induced by a rotating rigid disk in a porous medium. Unlike previous work on similar systems, we assume that the disk surface is well polished (coated); therefore, there are velocity and temperature slips between the nanofluid and the disk surface. The importance of considering slip conditions in modeling nanofluids comes from practical applications where rotating parts of machines may be coated. Additionally, this study examines the influence of heat generation on the temperature distribution within the flow. By transforming the original Navier–Stokes partial differential equations (PDEs) into a system of ordinary differential equations (ODEs), numerical solutions are obtained. The boundary conditions for velocity and temperature slips are formulated using the effective viscosity and thermal conductivity of the copper–water nanofluid. The dependence of the velocity and temperature fields in the nanofluid flow on key parameters is investigated. The major findings of the study are that the nanoparticle volume fraction significantly impacts the temperature distribution, particularly in the presence of a heat source. Furthermore, polishing the disk surface enhances velocity slips, reducing stresses at the disk surface, while a pronounced velocity slip leads to distinct changes in the radial, azimuthal, and axial velocity components. The study highlights the influence of slip conditions on fluid velocity as compared to previously considered non-slip conditions. This suggests that accounting for slip conditions for coated rotating disks would yield more accurate predictions in assessing heat transfer, which would be potentially important for the practical design of various devices using nanofluids. Full article

Particle image velocimetry (PIV) measurements were performed inside a low-pressure turbine cascade operating at engine-relevant high-speed and low-Re conditions to investigate the near-endwall flow. Of particular research interest was the dominant periodic disturbance of the flow field by incoming wakes, which were generated by moving cylindrical bars at a frequency of 500 Hz. Two PIV setups were utilized to resolve both (1) a large blade-to-blade plane close to the endwall as well as midspan and (2) the wake effects in an axial flow field downstream of the blade passage. The measurements were performed using a phase-locked approach in order to align and compare the results with comprehensive CFD data that are also available for this test case. The experimental results not only support a better understanding and even a quantification of the wake-induced over/under-turning inside and downstream of the passage, they also enable the tracing of the `negative-jet-effect’, which is widely known in the CFD branch of the turbomachinery community but is seldom visualized in experiments. The results also reveal that the bar wake periodically widens the blade wake by up to 165%, while the secondary flow is less affected and exhibits a phase lag with respect to the 2D-flow effects. The results presented here are an essential basis for the subsequent investigation of the near-endwall blade suction surface effects using unsteady pressure-sensitive paint in the second part of this two-part publication. Full article

Heat transfer affects a motor’s sizing, its performance, and, ultimately, the overall vehicle’s range and endurance. However, the thermal literature does not have early-stage models for outrunner brushless DC (BLDC) motors found in small unmanned aerial systems (UASs). To address this gap, we have developed a non-dimensional heat transfer model (Nusselt correlation). Parametric experiments of four different-sized BLDC motors under load in Reynolds-matched wind tunnel tests generated data for model correlation. The motors’ aspect ratios (diameter/length) ranged from 0.9 to 1.5. The freestream Reynolds number of the axial flow over the motors ranged from 20,000 to 40,000. The rotational Reynolds number ranged from 10,000 to 20,000. The results showed that aspect ratio had the largest influence on heat transfer, followed by rotational and freestream Reynolds numbers. A steady-state model used the correlation to predict the motor’s ambient temperature differential within 10 K of experimental data. A case study applied the correlation to predict a hypothetical motor’s continuous torque in different environments. The correlation enables conceptual designers to capture thermally-driven trade-offs in early design stages and reduce costly revisions in later stages. Full article

Based on the quasi-six-degree-of-freedom flight dynamic equations, considering the changes in the elevation angle caused by an increase in the rolling angle during maneuvering turns, which leads to a rise in the radar cross-section. A computational model for the radar detection probability of aircraft in complex environments was constructed. By comprehensively considering flight parameters such as turning angle, rolling angle, Mach number, and radar power factor, this study quantitatively analyzed the influence of these factors on the radar detection probability. It revealed the variation patterns of radar detection probability under different flight conditions. The results provide theoretical support for the Radar Valley Radius and Turning Maneuver Method (RVR-TM) based on decision trees, and lay the foundation for the development of subsequent intelligent decision-making models. To further optimize the trajectory selection of aircraft in complex environments, this study combines theoretical analysis with reinforcement learning algorithms to establish an intelligent decision-making model. This model is trained using the Proximal Policy Optimization (PPO) algorithm, and through precisely defining the state space and reward functions, it accomplishes intelligent trajectory planning for stealth aircraft under radar threat scenarios. Full article

The long-term record of ecological, limnological and climatological parameters that were documented in the Kinneret drainage basin was statistically evaluated. The dependent relations between environmental parameters and a change in climate conditions open a consequence dispute between three optional definitions: long-term instability, climate change impact and ecosystem resiliency. The Kinneret drainage basin during the Anthropocene era is marked by intensive anthropogenic involvement: Increase in population size, drainage of the wetlands and old lake Hula, agricultural development, enhancement of lake Kinneret utilization for water supply, hydrological management, fishery and recreation. Therefore, the impact of a combination of natural and anthropogenic environmental factors confounded each other, and the uniqueness of climate change is unclear. Full article

The effect of plant cultivar on the degree of mycorrhization and the benefits mediated by arbuscular mycorrhizal fungi (AMF) have been documented in many crops. In apple, a wide variety of rootstocks are commercially available; however, it is not clear whether some rootstock genotypes are more susceptible to mycorrhization than others and/or whether AMF species identity influences rootstock compatibility. This study addresses these questions by directly testing the ability/efficacy of four different AMF species (Rhizophagus irregularis, Septoglomus deserticola, Claroideoglomus claroideum or Claroideoglomus etunicatum) to colonize a variety of commercially available Geneva apple rootstock genotypes (G.11, G.41, G.210, G.969, and G.890). Briefly, micropropagated plantlets were inoculated with individual species of AMF or were not inoculated. The effects of the rootstock genotype/AMF interaction on mycorrhization, plant growth, and/or leaf nutrient concentrations were assessed. We found that both rootstock genotype and the identity of the AMF are significant sources of variation affecting the percentage of colonization. However, these factors largely operate independently in terms of the extent of root colonization. Among the AMF tested, C. etunicatum and R. irregularis represented the most compatible fungal partners, regardless of apple rootstock genotype. Among the rootstocks tested, semi-dwarfing rootstocks appeared to have an advantage over dwarfing rootstocks in regard to establishing and maintaining associations with AMF. Nutrient uptake and plant growth outcomes were also influenced in a rootstock genotype/AMF species-specific manner. Our findings suggest that matching host genetics with compatible AMF species has the potential to enhance agricultural practices in nursery and orchard systems. Full article

Nitrogen (N) is the most important nutrient in coffee, with a direct impact on productivity, quality, and sustainability. N uptake by the roots is dominated by ammonium (NH₄⁺) and nitrates (NO₃⁻), along with some organic forms at a lower proportion. From the perspective of mineral fertilizer, the most common N sources are urea, ammonium (AM), ammonium nitrates (AN), and nitrates; an appropriate understanding of the right balance between N forms in coffee nutrition would contribute to more sustainable coffee production through the better N management of this important crop. The aim of this research was to evaluate the influences of different NH₄-N/NO₃-N ratios in coffee from a physiological and agronomical perspective, and their interaction with soil water levels. Over a period of 5 years, three trials were conducted under controlled conditions in a greenhouse with different growing media (quartz sand) and organic soil, with and without water stress, while one trial was conducted under field conditions. N forms and water levels directly influence physiological responses in coffee, including photosynthesis (Ps), chlorophyll content, dry biomass accumulation (DW), nutrient uptake, and productivity. In all of the trials, the plants group in soils with N ratios of 50% NH₄-N/50% NO₃-N, and 25% NH₄-N/75% NO₃-N showed better responses to water stress, as well as a higher Ps, a higher chlorophyll content, a higher N and cation uptake, higher DW accumulation, and higher productivity. The soil pH was significantly influenced by the N forms: the higher the NO₃⁻-N share, the lower the acidification level. The results allow us to conclude that the combination of 50% NH₄-N/50% NO₃-N and 25% NH₄-N/75% NO₃-N N forms in coffee improves the resistance capacity of the coffee to water stress, improves productivity, reduces the soil acidification level, and improves ion balance and nutrient uptake. Full article

Pithiness is one of the physiological diseases of radishes, which is accompanied by the accumulation of reactive oxygen species (ROS) during the sponging of parenchyma tissue in the fleshy roots. A respiratory burst oxidase homolog (Rboh, also known as NADPH oxidase) is a key enzyme that catalyzes the production of ROS in plants. To understand the role of Rboh genes in radish pithiness, herein, 10 RsRboh gene families were identified in the genome of Raphanus sativus using Blastp and Hmmer searching methods and were subjected to basic functional analyses such as phylogenetic tree construction, chromosomal localization, conserved structural domain analysis, and promoter element prediction. The expression profiles of RsRbohs in five stages (Pithiness grade = 0, 1, 2, 3, 4, respectively) of radish pithiness were analyzed. The results showed that 10 RsRbohs expressed different levels during the development of radish pithiness. Except for RsRbohB and RsRbohE, the expression of other members increased and reached the peak at the P2 (Pithiness grade = 2) stage, among which RsRbohD1 showed the highest transcripts. Then, the expression of 40 genes related to RsRbohD1 and pithiness were analyzed. These results can provide a theoretical basis for improving pithiness tolerance in radishes. Full article

Studies show that feedback greatly improves student learning outcomes, but achieving this level of personalization at scale is a complex task, especially in the diverse and open environment of Massive Open Online Courses (MOOCs). This research provides a novel method for using cutting-edge artificial intelligence technology to enhance the feedback mechanism in MOOCs. The main goal of this research is to leverage AI’s capabilities to automate and refine the MOOC feedback process, with special emphasis on courses that allow students to learn at their own pace. The combination of LangChain—a cutting-edge framework specifically designed for applications that use language models—with the OpenAI API forms the basis of this work. This integration creates dynamic, scalable, and intelligent environments that can provide students with individualized, insightful feedback. A well-organized assessment rubric directs the feedback system, ensuring that the responses are both tailored to each learner’s unique path and aligned with academic standards and objectives. This initiative uses Generative AI to enhance MOOCs, making them more engaging, responsive, and successful for a diverse, international student body. Beyond mere automation, this technology has the potential to transform fundamentally how learning is supported in digital environments and how feedback is delivered. The initial results demonstrate increased learner satisfaction and progress, thereby validating the effectiveness of personalized feedback powered by AI. Full article

In the digital age, data transmission has become a key component of globalization and international cooperation. However, it faces several challenges in protecting the privacy and security of data, such as the risk of information disclosure on third-party platforms. Moreover, there are few solutions for personal data protection in cross-border transmission scenarios due to the difficulty of handling sensitive information between different countries and regions. In this paper, we propose an approach, personal data protection based on homomorphic encryption (PDPHE), to creatively apply the privacy computing technology homomorphic encryption (HE) to cross-border personal data protection. Specifically, PDPHE reconstructs the classical full homomorphic encryption (FHE) algorithm, DGHV, by adding support for multi-bit encryption and security level classification to ensure consistency with current data protection regulations. Then, PDPHE applies the reconstructed algorithm to the novel cross-border data protection scenario. To evaluate PDPHE in actual cross-border data transfer scenarios, we construct a prototype model based on PDPHE and manually construct a data corpus called PDPBench. Our evaluation results on PDPBench demonstrate that PDPHE cannot only effectively solve privacy protection issues in cross-border data transmission but also promote international data exchange and cooperation, bringing significant improvements for personal data protection during cross-border data sharing. Full article

This paper proposes a control strategy for grid-following inverter control and grid-forming inverter control developed for a Solar Photovoltaic (PV)–battery-integrated microgrid network. A grid-following (GFL) inverter with real and reactive power control in a solar PV-fed system is developed; it uses a Phase Lock Loop (PLL) to track the phase angle of the voltages at the PCC and adopts a vector control strategy to adjust the active and reactive currents that are injected into the power grid. The drawback of a GFL inverter is that it lacks the capability to operate independently when the utility grid is down due to outages or disturbances. The proposed grid-forming (GFM) inverter control with a virtual synchronous machine provides inertia to the grid, generates a stable grid-like voltage and frequency and enables the integration of the grid. The proposed system incorporates a battery energy storage system (BESS) which has inherent energy storage capability and is independent of geographical areas. The GFM control includes voltage and frequency control, enhanced islanding and black start capability and the maintenance of the stability of the grid-integrated system. The proposed model is validated under varying irradiance conditions, load switching, grid outages and temporary faults with fault ride-through (FRT) capability, and fast frequency response and stability are achieved. The proposed model is validated under varying irradiance conditions, load switching, grid outages and line faults incorporating fault ride-through capability in GFM-based control. The proposed controller was simulated in a 100 MW solar PV system and 60 MW BESS using the MATLAB/Simulink 2023 tool, and the experimental setup was validated in a 1 kW grid-connected system. The percentage improvement of the system frequency and voltage with FRT-capable GFM control is 69.3% and 70%, respectively, and the percentage improvement is only 3% for system frequency and 52% for grid voltage in the case of an FRT-capable GFL controller. The simulation and experimental results prove that GFM-based inverter control achieves fast frequency response, and grid stability is also ensured. Full article

In this paper, an analysis of existing models for describing surfaces of various types is performed, and the possibilities of their application at the level of mathematical modeling are analyzed. Moreover, due to the large number of models and the complexity of selecting the appropriate model, e.g., when conducting a practical experiment, an algorithm for choosing a specific model depending on the initial data is proposed. According to the algorithm, a software prototype that implements this algorithm (written in Python) is proposed. Full article

Navigation systems are extensively used in everyday life, but the conventional A* algorithm has several limitations in path planning applications within these systems, such as low degrees of freedom in path planning, inadequate consideration of the effects of special regions, and excessive nodes and turns. Addressing these limitations, an enhanced A* algorithm was proposed using regular hexagonal grid mapping. First, the approach to map modeling using hexagonal grids was described. Subsequently, the A* algorithm was refined by optimizing the calculation of movement costs, thus allowing the algorithm to integrate environmental data more effectively and flexibly adjust node costs while ensuring path optimality. A quantitative method was also introduced to assess map complexity and adaptive heuristics that decrease the number of traversed nodes and increase the search speed. Moreover, a turning penalty measure was implemented to minimize unnecessary turns on the planned paths. Simulation results confirmed that the improved A* algorithm exhibits superior performance, which can dynamically adjust movement costs, enhance search efficiency, reduce turns, improve overall path planning quality, and solve critical path planning issues in navigation systems, greatly aiding the development and design of these systems and making them better suited to meet modern navigation requirements. Full article

Microarray experiments, a mainstay in gene expression analysis for nearly two decades, pose challenges due to their complexity. To address this, we introduce DExplore, a user-friendly web application enabling researchers to detect differentially expressed genes using data from NCBI’s GEO. Developed with R, Shiny, and Bioconductor, DExplore integrates WebGestalt for functional enrichment analysis. It also provides visualization plots for enhanced result interpretation. With a Docker image for local execution, DExplore accommodates unpublished data. To illustrate its utility, we showcase two case studies on cancer cells treated with chemotherapeutic drugs. DExplore streamlines microarray data analysis, empowering molecular biologists to focus on genes of biological significance. Full article