Abstract: Solid-state fermentation (SSF) involves the growth of microorganisms on solid substrates, mimicking natural environments of many species. Due to sustainability concerns, transforming agro-industrial by-products into value-added products through SSF has been increasingly studied. Brewer’s spent grain (BSG), the main by-product of beer production, mostly consists of barley grain husks, making BSG a great support for microorganism cultivation. Although autoclaving remains the standard sterilization and pretreatment method of substrates, electric field technologies and its attendant ohmic heating (OH) have great potential as an alternative technology. In the present work, pretreatment of BSG by OH was explored in SSF with Aspergillus niger to produce commercially valuable enzymes. OH favored the solubilization of phenolic compounds, total protein and reducing sugars, significantly higher than autoclaving. SSF of treated BSG led to the production of lignocellulosic enzymes, with xylanases being the most active, reaching 540 U/g, a 1.5-fold increase in activity compared to autoclaved BSG. Protease activity was also improved 1.6-fold by OH, resulting in 49 U/g. Our findings suggest that OH treatment is an effective alternative to autoclaving and that its integration with SSF is a sustainable strategy to enhance by-products valorization through enzymes production with many industrial applications, according to circular economy guidelines.

Abstract: Medical terminology is an essential component of healthcare communication but poses significant challenges for patients, especially those with limited health literacy. This article examines the role of medical terminology in nurse-patient communication and how it impacts patient understanding and health outcomes. The study reviews existing literature on communication strategies in nursing, emphasizing the need to simplify medical language. Findings suggest that patients with low health literacy struggle to understand complex medical terms, which may lead to non-compliance with treatment and poor health outcomes. Key strategies to improve nurse-patient communication include using plain language, the "teach-back" method, and incorporating visual aids. The article concludes with recommendations for future research, particularly regarding digital health tools and culturally sensitive communication. The use of complex medical terminology remains a significant barrier to patient comprehension, especially among those with lower health literacy. Simplifying the language used in healthcare settings is vital not just for improving patient outcomes but also for fostering trust between nurses and patients. While medical terminology can be important for precise diagnoses and instructions, the healthcare system must prioritize clarity and accessibility to ensure that all patients, regardless of their literacy levels, can actively engage in their care. The "teach-back" method has proven to be a powerful tool in enhancing understanding, ensuring patients are not just passive recipients of information but active participants in their health journey.

Abstract: Keywords: P-GaN HEMT; UIS; failure analysis; degradation and recovery

Abstract: This study proposes that Agent is not only the core concept of artificial intelligence but also a key factor driving the significant paradigm shift in 21st-century science. Initially, from an information-processing perspective, we establish Standard Agent Model, positing as an axiom that any system with information input, output, storage, creation, and control capabilities constitutes an agent. Based on this premise, we derive two extreme states of agent and their evolutionary driving mechanisms: Absolute Zero Agent (α Point), corresponding to the state where all five essential capabilities are zero; and Omniscient and Omnipotent Agent (Ω Point), where all five essential capabilities approach infinity. The driving force behind agent evolution toward α Point is termed Alpha Gravity, and toward Ω Point is termed Omega Gravity. From this foundation, we further construct a General Model of Agent Evolution and an Intelligent Universe Evolution Model. The results reveal that the Universe is essentially a dynamically evolving agent, and agent constitute the fundamental units of the Universe. Based on this theoretical framework, the study achieves significant theoretical advancements in five key areas: (1) Establishing a unified formal description of agent to realize a unified theoretical framework for physical and intelligent systems; (2) Defining observers as agent with varying intelligence levels, thereby uncovering the essential reason behind the differing descriptions of natural laws by classical mechanics, relativity, and quantum mechanics; (3) Providing new theoretical interpretations for fundamental scientific concepts such as the dualism between objectivity and subjectivity, the unity of determinism and indeterminism, and the emergence of space-time structures, based on characteristics exhibited by Universe-agent at different evolutionary stages; (4) Proposing fundamental definitions of intelligence and consciousness grounded in the functional structure, evolutionary boundaries, and evolutionary dynamics of agent; (5) Compared to string theory and loop quantum gravity, the proposition that Agent constitutes the fundamental unit of the Universe not only integrates essential elements such as information, matter (objective reality), and mind (subjective non-reality) but also establishes a more comprehensive theoretical framework for the fundamental structure of the Universe.

Abstract: This study presents a comparative analysis of four topic modeling techniques—Latent Dirichlet Allocation (LDA), BERT, Probabilistic Latent Semantic Analysis (PLSA), and Non-negative Matrix Factorization (NMF)—applied to aviation safety reports from the ATSB dataset spanning 2003–2023 (53,275 records). The evaluation focuses on coherence, interpretability, generalization, computational efficiency, and scalability. Results indicate that NMF achieves the highest coherence score (0.7987), demonstrating its effectiveness in extracting well-defined topics from structured narratives. PLSA performs competitively (coherence: 0.7634) but lacks the scalability of NMF. LDA and BERTopic, while effective in generalization (perplexity: -6.471 and -4.638, respectively), struggle with coherence due to their probabilistic nature and reliance on contextual embeddings. Expert validation confirms the practical relevance of NMF-generated topics, highlighting its suitability for aviation safety analysis. These findings suggest that selecting a topic modeling technique should be context-driven, with future research exploring hybrid modeling approaches and real-time applications to enhance aviation safety analysis further. By refining topic modeling techniques, this study contributes to advancing automated safety monitoring in the aviation industry

Abstract: Streptococcus equi subsp. zooepidemicus (SEZ) is a significant concern in equine veterinary medicine. Typically, a commensal in horses, SEZ can cause severe diseases including respiratory infections, septicemia, and reproductive tract infections under certain conditions. Recent evidence suggests that SEZ may also cause severe diseases in humans through direct contact with animals or consumption of unpasteurized milk and dairy products. This study investigates SEZ strains isolated from nasal swabs of Equidae in central Italy in 2023 to describe the epidemiology and genomic characteristics of circulating strains. A sampling plan was implemented to randomly collect nasal swabs from equid farms in the Abruzzo and Molise regions. Additionally, a sampling form was designed to gather information on risk factors related to the presence of the bacterium and the potential development of respiratory disease. Relative risk was employed to measure the association between the presence of SEZ in the samples and various variables. The swabs were analyzed using real-time PCR and isolation. To confirm the identification and characterize the strains, the isolates were fully sequenced by next-generation sequencing (NGS) through the Illumina platform (Illumina, San Diego, CA, USA). A total of 478 animals from 99 farms were sampled with 144 animals tested positive for SEZ (79% horses, 20% donkeys, 1% mules). Forty-five percent of the farms examined were found to be positive for SEZ. Monitoring the clonal spread of SEZ is crucial for understanding the ecology of this emerging zoonotic pathogen, assessing the risk, and implementing effective control measures. Furthermore, genomic assessments are recommended to examine the pathogenicity of circulating strains. This study provides a comprehensive understanding of the epidemiology and genomic diversity of SEZ strains isolated in central Italy.

Abstract: Background: Left ventricular hypertrophy (LVH) is an adaptive response to hypertension. The Cornell Product (CP) criteria, combining Cornell voltage and QRS duration in Electrocardiogram (ECG), is a widely used method for diagnosing LVH due to its sensitivity in detecting changes in the left ventricle’s electrical activity and structure. The CP criteria remain one of the most accessible and effective ECG-based methods for LVH detection in hypertensive populations. However, research on the diagnostic performance remains limited. Methods: This study aimed to evaluate the diagnostic accuracy of the CP criteria in hypertensive patients. A cross-sectional design was employed with normal group (41) and LVH group (74). Results: Age was the most significant predictor of left ventricular geometry (LVG) (adjusted p-value 0.000, OR 9.01, 95% CI 2.81-28.9). The correlation between LVH detection using ECG with CP criteria and echocardiography was not significant (p-value 0.513). At the standard threshold of ≥2440 mmms, the CP criteria showed a sensitivity of 16.22% and high specificity of 88%. After modifying the cutoff to ≥1240 mmms, sensitivity improved to 55.41% and specificity to 58%. Conclusion: while the CP criteria show high specificity, their sensitivity for detecting LVH remains suboptimal, though adjusting the cutoff enhances diagnostic performance.

Abstract: We analyzed the dynamic equilibrium process between demand and supply in the international airline market by utilizing Granger causality and Bayesian Networks (BN) based on South Korea’s aviation performance data. To examine whether the interrelationship between demand and supply varies depending on the classification of external factors, we tested for changes in causality based on reasonable segmentation of sub-market, time window, and time lag. Based on the results of the Granger causality analysis, we constructed a BN model to determine whether economic factors influence changes in the causal relationship between demand and supply, as well as to track the dynamic equilibrium path of demand and supply. The international airline market was classified into national and foreign carriers, as well as full-service carriers (FSCs) and low-cost carriers (LCCs). Time windows were set on a monthly, quarterly, and annual basis, while time lags were set with the minimum duration based on the unit of time window and the maximum duration based on data availability. Supply variables included the number of operations, available seat capacity, and load factor, whereas demand was represented by the number of revenue passengers. Our findings support the hypothesis that airline supply and demand factors in South Korea’s international airline market exhibit mutual causality. Moreover, the causality from demand to supply was found to be somewhat clearer than the reverse case. As the time window shortened, the interrelationship became more evident, and the influence of demand on supply exhibited a shorter time lag while maintaining a longer duration compared to the opposite direction. In terms of market segmentation, the relationship between supply and demand was more distinct in the LCC market compared to the FSC market and in the national carrier market compared to the foreign carrier market. The BN model incorporating economic factors confirmed that the causal relationship between airline supply and demand could appear independently of economic influences when analyzing total monthly demand. Ultimately, our study confirms the existence of a mutual causal relationship between airline supply and demand in South Korea’s international airline market. From an academic perspective, we provide insights into the dynamic equilibrium characteristics and pathways of supply and demand in the airline industry.

Abstract: While traditionally tied to infections and the immune system, inflammation is now recognized to manifest distinctive markers across a broader array of diseases, as suggested by recent evidence. It encompasses a series of cellular and microvascular reactions aimed at eliminating damaged tissue and promoting the generation of new, healthy tissue. Ginger (Zingiber officinale Roscoe), a member of the Zingiberaceae family, has earned widespread popularity as a spice with ancient roots. Ginger contains predominantly gingerols, shogaols, and paradols as its main phenolic compounds. The primary phenolic compounds present in fresh ginger are gingerols, with 6-gingerol being the predominant form, accompanied by 4-, 5-, 8-, 10-, and 12-gingerols. Gingerols, acting as inhibitors of COX (Cyclooxygenase enzymes), have proven effective in a diverse array of pharmacological activities. Concerning a diverse spectrum of biological activities and documented mechanisms, the intricate interplay among three pivotal events—namely, inflammation, oxidative stress, and immunity—seems to contribute significantly to the myriad pharmacological effects of this compound. Through the inhibition of protein kinase B (Akt) and nuclear factor kappa B (NF-κB) signaling pathways, gingerols exhibit the capacity to mitigate inflammation, resulting in a reduction of proinflammatory cytokines and an elevation of anti-inflammatory cytokines. Through molecular modeling simulations, it was observed that gingerols preferentially interact with COX (cyclooxygenase enzymes) with a significant binding energy of -7 Kcal/mol. Exploration of hit compounds involves the application of tools such as ADMET@SAR, Discovery Studio, ADME/toxicity profiling, and molecular docking simulations. In conclusion, we utilized a computational technique to analyze interactions with drug targets.

Abstract: Corals in the Galápagos present diverse reef configurations from biogenic coral reefs to coral communities growing on rocks and sand. These corals have experienced decades of disturbances including recurring El Niño and mass bleaching events. However, traditional methods in ecology have limited capacity in describing coral demographic trends across large spatial scales. Therefore, the adverse effects of climate change on these reefs remains unclear. To bridge this gap we surveyed seven reef sites across the archipelago using underwater photogrammetry. We present new methods for 3D annotation and fractal dimension calculation. Our findings reveal variation in coral cover, diversity, and structural complexity across the archipelago. Our results align with previous studies in the region and add important information on reef structural complexity which was not measured here before. We release a unique dataset: Galápagos_3D, including seven 3D models and over 17,000 annotated images from the region. Our study provides an important baseline for long-term monitoring, research, and conservation in the Galápagos. Such studies can lead to new discoveries in coral ecology regarding for example recovery capacity and resilience, and help to establish evidence-based policies in the region and beyond.

Abstract: This review highlights the innovative development of orally disintegrating tablets (ODTs) derived from Siddha traditional herbal medicine dosage forms. It emphasizes the advancements and transformative approaches in Siddha medicine that enable its integration into modern ODT’s formulations, primarily aimed at improving patient compliance. The focus is particularly on pediatric and geriatric populations, addressing the unique challenges in adapting traditional formulations into contemporary dosage forms. Additionally, the incorporation of taste-masking techniques to enhance palatability for herbal drugs and enhance the patient compliance is discussed. Key aspects of preparation techniques, evaluation parameters, and strategies for ensuring the efficacy and stability of these formulations are comprehensively reviewed. This work underscores the potential of ODTs to enhance the accessibility and therapeutic acceptability of traditional herbal medicine Siddha across diverse age groups.

Abstract: High pathogenicity avian influenza viruses (AIV) are a constant worldwide threat to animals and humans. The ongoing spread of the H5N1 virus underlines the pressing need for enhanced monitoring and containment strategies. Here, we report the development and validation of a novel workflow for investigating AIV genes in environmental water. This approach integrates three key components: concentration via QuickConc, extraction using a COPMAN kit, and detection through reverse transcription-preamplification-quantitative PCR. Spike experiments demonstrated that our workflow exhibits a 100-fold increase in sensitivity for AIV gene detection, compared to a combination of hemagglutination assay, QIAamp RNA Blood Mini, and one-step RT-qPCR as a control method. The efficacy of this approach was further corroborated by the successful identification of influenza M and H5 genes in field samples, specifically from surface water volumes not exceeding 500 mL collected from a body of water frequented by migratory birds. This workflow represents a significant advancement in environmental AIV surveillance, potentially augmenting our capacity to monitor and track AIV dynamics in wild bird populations using aquatic ecosystems.

Abstract: Background and aims: Bariatric surgery (BS), drugs approved for type-2-diabetes (T2D), obesity, and liver fibrosis (resmetirom) announce the widespread use of fibrosis-tests in patients with metabolic liver disease (MASLD). An unmet need is to reduce the uncertainty of biomarkers for the diagnosis of the early stage of clinically significant fibrosis (eF). This can be achieved if three essential but neglected STARD methods (3M) are used—a more sensitive histological score than the standard comparator (five-tiers), the weighted area under the characteristic-curve (wAUROC) instead of the binary-AUROC, and biopsy length. We applied 3M to FibroTest-T2D to demonstrate this reduction of uncertainty, and constructed proxies predicting eF in large populations. Methods: For uncertainty, seven subsets were analyzed, four included biopsies (n=1,903), and to assess eF incidence, three MASLD-populations (n=299,098). FibroTest-T2D classification-rates after BS and in out-patients-T2D (n=402) were compared with and without 3M. In MASLD, trajectories of proxies and incidence against confounding-factors used hazard-ratios.Results: After BS (110 biopsies), reversal of eF was observed in 16/29 patients (84%) using seven-tier scores vs. 3/20 patients (47%) using five-tier scores (P=.005). When biopsy length was above the median, FibroTest-T2D wAUROC was .90 (SD=.01), and the wAUROC was .88 (SD=.1) when the length was below the median (P<.001). For the first time, obesity was associated with eF, before T2D (P<.001), and perimenopausal age with apoA1 and haptoglobin increases (P<.0001).Conclusion: Validations of circulating biomarkers need to assess their uncertainty. FibroTest-T2D predicts fibrosis regression after BS. Applying 3M and adjustments could avoid misinterpretations in MASLD surveillance.

Abstract: Background/Objectives: Lumbar spine surgery has undergone significant technological transformation in recent years, driven by the goals of minimizing invasiveness, improving precision, and enhancing clinical outcomes. Emerging tools—including robotics, augmented reality, computer-assisted navigation, and artificial intelligence—have complemented the evolution of minimally invasive surgical (MIS) approaches, such as endoscopic and lateral interbody fusions. Methods: This systematic review evaluates literature from February 2020 to February 2025 on technological and procedural innovations in LSS. Eligible studies focused on degenerative lumbar pathologies, advanced surgical technologies, and reported clinical or perioperative outcomes. Randomized controlled trials, comparative studies, meta-analyses, and large case series were included. Results: A total of 32 studies met inclusion criteria. Robotic-assisted surgery demonstrated high accuracy in pedicle screw placement (~92–94%) and reduced intraoperative blood loss and radiation exposure, although long-term clinical outcomes were comparable to conventional techniques. Intraoperative navigation improved instrumentation precision, while AR enhanced ergonomic workflow and reduced surgeon distraction. AI tools showed promise in surgical planning, guidance, and outcome prediction but lacked definitive evidence of clinical superiority. MIS techniques—including endoscopic discectomy and MIS-TLIF—offered reduced blood loss, shorter hospital stays, and faster recovery, with equivalent pain relief, fusion rates, and complication profiles compared to open procedures. Lateral and oblique approaches (XLIF/OLIF) further optimized alignment and indirect decompression, with favorable perioperative metrics. Conclusions: Recent innovations in lumbar spine surgery have enhanced technical precision and perioperative efficiency without compromising patient outcomes. While short-term benefits are clear, long-term clinical advantages and cost-effectiveness require further investigation. Integration of robotics, navigation, AI, and MIS into spine surgery reflects an ongoing shift toward personalized, data-driven, and less invasive care.

Abstract: Introduction: Fragmented practices need more interprofessional education. The development of interprofessional simulation in health careers has advanced undergraduate training in teamwork, roles, responsibilities, decision-making, and ethics. The study aims to analyze the effects of participating in a clinical simulation pilot on self-efficacy for interprofessional learning in health students. Methods: Final-year students from speech therapy, physical therapist, and occupational therapy programs participated in three consecutive interprofessional clinical simulation scenarios and were compared with a control group. All students completed the Self-Efficacy Scale for Interprofessional Experiential Learning. Scale scores were compared using the Mann-Whitney U test. This study was approved by the ethics committee (No. 149-22) of Universidad San Sebastián. Results: Twelve students participated in the study group and twelve in the control group. Significant differences were observed in the two dimensions of the self-efficacy scale: professional interaction (p< 0.001) and interprofessional team assessment and feedback (p< 0.001), with the study group scoring higher than the control group. Conclusions: Students participating in interprofessional simulation have higher self-efficacy for experiential learning than those not participating in the professional interaction and interprofessional team assessment and feedback dimensions. Confirms the effectiveness of interprofessional simulation as an educational strategy.

Abstract: Multi-person pose forecasting involves predicting the future body poses of multiple individuals over time, involving complex movement dynamics and interaction dependencies. Its relevance spans various fields, including computer vision, robotics, human-computer interaction, and surveillance. This paper introduces GCN-Transformer, a novel model for multi-person pose forecasting that leverages the integration of Graph Convolutional Network and Transformer architectures. We integrated novel loss terms during the training phase to enable the model to learn both interaction dependencies and the trajectories of multiple joints simultaneously. Additionally, we propose a novel pose forecasting evaluation metric called Final Joint Position and Trajectory Error (FJPTE), which assesses both local movement dynamics and global movement errors by considering the final position and the trajectory leading up to it, providing a more comprehensive assessment of movement dynamics. Comprehensive evaluations on the SoMoF Benchmark and ExPI datasets demonstrate that the proposed GCN-Transformer model consistently outperforms existing state-of-the-art (SOTA) models according to the VIM and MPJPE metrics. Specifically, GCN-Transformer shows a 5% improvement over the closest SOTA model on the SoMoF Benchmark’s MPJPE metric and a 2.6% improvement over the closest SOTA model on the ExPI dataset MPJPE metric. Unlike other models whose performance fluctuates across datasets, GCN-Transformer performs consistently, proving its robustness in multi-person pose forecasting and providing an excellent foundation for the application of GCN-Transformer in different domains. The code is available at https://github.com/RomeoSajina/GCN-Transformer.

Abstract: In western Jilin Province, China, the presence of soda saline-alkali soil poses a significant threat to the rice seedlings raising due to its harsh soil properties. The scarcity of suitable seedling-raising soil resources has become increasingly pronounced. A short-term soil improvement experiment was conducted using the original saline-alkali soil sourced from the rice-growing region of Jilin Province, followed by the rice seedlings raising test in the improved soil to identify an effective soil improvement strategy. Four distinct treatments were established: no amendment (JCK), gypsum and straw (JCW), gypsum, straw, and sulfuric acid (JCWH), and gypsum, straw, and chemical fertilizer (JCWF). The effects of these amendment treatments on soil physicochemical properties (pH, EC, exchangeable sodium, total alkalinity) ，as well as soil organic carbon and its components (SOC, HAC, and FAC)were evaluated. The results indicated that, compared to the control, all amendment treatments effectively reduced the average soil pH by 0.53 to 0.79 units and decreased exchangeable sodium by 56.7% to 74.8%. Furthermore, the average soil SOC, HAC, and FAC increased by 48.3%, 89.4%, and 56.0%, respectively. Among the treatments, JCWH proved to be the most effective. After two years of improvement, the rice seedlings in the JCWH treatment soil exhibited the highest dry weight and plant height, surpassing those grown in the farmer's seedling-raising soil.

Abstract: Background: Long non-coding RNAs (lncRNAs) are crucial factors affecting the occurrence, progression and prognosis of gastric cancer. Disulfidptosis, a type of programmed cell death, results from abnormal accumulation of disulfide bonds in high-SLC7A11 cells. Research have demonstrated that upregulated SLC7A11 is common in human cancers, but the effect of disulfidptosis on gastric cancer remains unclear. It is of great significance to recognize disulfidptosis -related lncRNAs (drlncRNAs) and develop a risk signature with prognostic value in gastric cancer. Methods: The transcriptome data and clinical information of gastric cancer patients were obtained from TCGA (the Cancer Genome Atlas) database. A 3 drlncRNAs risk model was built by three common regression analysis methods. Then we used ROC curves, independent prognostic analysis and other methods to assess the accuracy of the model. Furthermore, GO and KEGG enrichment analysis, immune infiltration analysis and drug sensitivity prediction were also performed in this study. Finally, TMB, MSI, and TIDE analyses were conducted to further explore the difference of high- and low- risk score group in immunotherapy response. Results: We constructed a prognostic model composed of 3 drlncRNAs (AC107021.2, AC016394.2 and AC129507.1). Univariate and multivariate Cox regression proved that the model was able to predict the prognosis of GC patients independently. GO and KEGG analyses suggested that the high-risk group mainly enriched in sulfur compound binding, canonical WNT signaling pathway, cell-substrate adherens junction, cAMP signaling pathway and so on. TME analysis indicated that ImmuneScores, StromalScores, and ESTIMATEScores were higher in the high-risk group. Meanwhile, the high-risk group showed higher levels of immune cell infiltration, while the low-risk group exhibited higher expression levels of immune checkpoints. Our research further revealed that, compared to patients in the high-risk group, patients in the low-risk group had higher tumor mutation burden, a higher proportion of MSI-H, and lower TIDE scores. Finally, gemcitabine, ABT.888 (veliparib) and other sensitive drugs were confirmed to be more effective in low-risk groups. Conclusion: The risk model we constructed can independently predict prognosis and provide precise and individual clinical treatment guidance for patients with GC.

Abstract: Dipalmitoylphosphatidylcholine (DPPC), is one of the key bilayer membranes of the phosphatidylcholine (PC) family which constitutes 40-50% of total cellular phospholipids in mammal cells. In this study, we investigate the behaviour of DPPC subjected to lateral pressures ranging from -200 to 150 bar at 323 K using microseconds-long simulations. We identify, with very high precision and through the employment of diverse metrics, the pressure range for the occurrence of critical phenomena, mainly buckling and rupture. Notably, under compression, the membrane initially thickens, leading to a structural phase transition from a uniformly thick state to an undulated phase between 40 and 50 bar, as gauged by sharp changes in area per lipid and headgroup dispersion. Stretching induces systematic membrane thinning, with rupture becoming probable at -170 bar and certain at -200 bar. Instant application of high pressure, corresponding to high loading, requires longer equilibration times than gradual pressure increments (low loading) and leads to slightly higher rupture probability during stretching. The reverse compression cycle shows pressure hysteresis with a 10-bar shift, while the reverse stretching cycle retraces the pathway. System size has a minimal impact on the trends. Under extreme mechanical stress, particularly near critical phenomena, systems may require up simulation times on the order of microsecond to accurately capture phase behaviour and structural alterations. This work could provide important insights to understand membrane behaviour under extreme conditions, of relevance to numerous biological and technological applications.

Abstract: Lowpass filters with bandwidths larger than several GHz are required in many applications, such as anti-aliasing filters in high-speed ADCs and pulse-shaping filters in high-speed DACs. In highly integrated applications, low area occupation and power consumption are key specifications, so that inductor-less implementations are to be preferred. Furthermore, full CMOS implementations provide an advantage in terms of technology availability and cost. In this paper we present an inductor-less CMOS biquad stage based on the super source follower topology, that provides 8GHz cut-off frequency and a low power consumption of 0.42mW per pole, showing remarkable performance also in terms of bandwidth and dynamic range. The availability of two separate current sources allows independent tuning of natural frequency and quality factor. The stage can be implemented in two complementary ways, exploiting NMOS and PMOS input devices respectively, thus simplifying cascadability. The two complementary biquads have been implemented in the STMicroelectronics FDSOI 28nm CMOS process and extensively simulated, and provide stable performance under PVT variations and mismatches. The area occupation is about 387.5mm2 per biquad, one of the lowest in the literature. The figures-of-merit are remarkable, as the filters achieve excellent power efficiency, very low area occupation, and good dynamic range.