Lager beer is the most widely consumed fermented beverage worldwide and is produced by the yeast Saccharomyces pastorianus, which originated from hybridization between S. cerevisiae (Sc) and S. eubayanus (Se). Although the genetic factors underlying fermentation performance in de novo hybrids are well understood, how chimeric genomes evolve in relation to the parental background and stabilize under industrial stresses remains unclear. Here, we subjected representative novel hybrids derived from distinct parental lineages to long-term experimental evolution for 250 generations under conditions mimicking high-gravity brewing. We observed a clear pattern of the "rule of declining adaptability", driven by diminishing-returns epistasis, whereby hybrids with lower initial fitness exhibited the greatest evolutionary gains. Overall, 38 of 69 evolved lines produced more CO2 than their ancestral strains. Adaptive trajectories were strongly influenced by the Sc genetic background, while the Se contribution appeared comparatively limited, highlighting the dominant role of the Sc subgenome in determining brewing performance and evolvability. Genomic analysis of representative hybrids revealed different evolutionary strategies. The evolved HB3-I line (beer Sc parent) showed a large-scale chromosomal rearrangement with few coding-sequence changes, consistent with fine-tuning of an already adapted background. In contrast, HB44-II (bioethanol Sc parent) accumulated multiple gene-disruptive mutations affecting multiple pathways, indicating broader regulatory rewiring. These genomic changes were accompanied by transcriptional shifts in carbon metabolism, nutrient-responsive pathways and membrane-related functions. Together, our results provide a controlled framework for replaying yeast domestication, demonstrating that the adaptive potential of synthetic lager hybrids depends on their lineage, which is mainly shaped by the S. cerevisiae parental background.
SARS-CoV-2 has significantly impacted people's lives worldwide. The viral genome has undergone numerous unanticipated changes that have led to the creation of new varieties and raised concerns across the globe. As the bioactive phytochemicals from both natural and synthetic origins have become a promising therapeutic approach due to their high ability to suppress pathogenic viruses. The current work reports 9 novel isatin hydrazide conjugates as inhibitors of the SARSCoV- 2 spike protein, using in vitro and in silico approaches. It's interesting to note that, except for compounds 3a, 3e, and 3h all the remaining compounds showed significant to high inhibition, with inhibitory values ranging from 91.50 to 77.40 %. Here, compounds 3b (86.80 %), 3c (89.30 %), 3d (81.60 %), 3f (87.30 %), 3g (84.40 %), 3i (91.50 %), and compound 3l (85.49 %) resulted in high inhibitory potential. While 3j compound with 77.40 % inhibition significantly inhibited the SARS-CoV-2 spike protein. With docking scores ranging from -7.1 to -9.1 kcal/mol, the molecular docking of these compounds showed an excellent fit of molecules in the spike protein receptor binding domain (RBD) with good interactions with the RBD's essential residues. Additionally, a 100 ns molecular dynamics simulation showed that the complexes 3i-6M0J and Narlaprevir-6M0J were highly stable. This study highlights the prospective therapeutic potential of new isatin hydrazide conjugates for the treatment of COVID-19 by identifying them as strong SARS-CoV-2 spike protein inhibitors that have been confirmed by docking and molecular dynamics. The results of these in vitro and in silico experiments suggest their medical potential in treating SARS-CoV-2 infection with high potency. Therefore, these tiny compounds have the potential to be therapeutic agents.
Multifunctional polyurethanes face a fundamental design trade-off: enhanced cross-linking and rigidity improve mechanical robustness but inevitably restrict chain mobility, thereby compromising essential features such as self-healing and recyclability. This inherent compromise severely constrains their multifunctional application. Here, we report a supramolecular polyurethane (COPUSL) comprising a "dynamic switch" constructed from dynamic disulfide bonds and hydrogen bonds, together with a "rigid-flexible balanced network" composed of castor oil long fatty chains and polyphenol-functionalized lignin. This design endows COPUSL with excellent mechanical properties while also enabling rapid self-healing (self-healing efficiency: 87%) and efficient recyclability. Furthermore, COPUSL with introduced aromatic structures and extended conjugate systems exhibits 100% ultraviolet-blocking efficiency and a high photothermal conversion capability (surface temperature: 153 °C) due to the electron transition and energy release of the lignin structure after absorbing light energy. By systematically investigating the relaxation kinetics, dynamic behavior, and macroscopic properties, we elucidate the distinct roles of the "dynamic switch" and "rigid-flexible balanced network" in regulating the polymer architecture and connecting dynamic behavior with mechanical and functional performance. These findings provide molecular-level insights for the design of high-performance, bio-based polyurethane with tailored multifunctional responsiveness.
The development and establishment of a broader range of coffee species and hybrids is likely to play a key role in coffee farming sustainability in an era of accelerated climate change. We investigated hybridization between Coffea liberica (Liberica) and C. dewevrei (excelsa) utilizing 7,618 SNPs from 113 accessions, sampled across three continents. Our analyses demonstrated that these two species have hybridized readily in cultivation and produced fertile progeny with a wide range of genomic admixture. We revealed extensive genomic admixture in farmed accessions from Sarawak. The identified hybrids exhibited intermediate characteristics and overlapping values for key agronomic traits, including seed size and parchment thickness. These traits, which influence yield,  outturn and post-harvest processing, can be transferred via hybridization. The hybrids also have the potential to broaden the climate envelope for successful coffee cultivation and transfer disease resistance. Improved genotypes resulting from hybrids between C. liberica and C. dewevrei could be brought into production relatively quickly, as a means of developing both species. We propose the formal name Coffea × libex for the interspecies hybrid.
Five new quinazoline-sulfonamide hybrids 4a (MZ-13), 4b (MZ-20), 4c (MZ-25), 4d (MZ-26), and 6a (MZ-29) were designed, synthesized, and investigated for their in vitro and in vivo antidiabetic activities. The in vivo screening was conducted in a mouse model of type II diabetes induced by streptozotocin (STZ), using glibenclamide as the positive control. The in vitro model was performed by measuring the activity of these compounds against the PPARγ enzyme. Furthermore, the total antioxidant capacity (TAC) was measured for these compounds to assess their ability to neutralize a wide range of free radicals. A physicochemical study was conducted to demonstrate the drugability of these compounds. Additionally, the in silico ADMET and toxicity studies illustrated good pharmacokinetic properties and a low toxicity profile. Likewise, a comprehensive molecular modeling study was performed to examine the binding modes of the new compounds. Compound MZ-29 showed 27.1% reduction in blood glucose (BG) levels, and the standard glibenclamide showed 17.2% reduction in BG. The in vitro assay of the compounds MZ-13 and MZ-29 demonstrated superior or comparable activity to the reference glibenclamide. The study identified MZ-29 and MZ-26 as the most promising candidates in the series. These two compounds achieved docking scores and binding orientations closely mimicking the native ligand.
Benzofuran, an oxygen-containing fused heterocyclic aromatic compound, occurs naturally as a secondary metabolite from various plant sources like Rutaceae, Asteraceae, Cyperaceae, and Liliaceae. The derivatives of benzofuran possess a wide range of biological activities, including anticancer, anti-inflammatory, antioxidant, antibiotic, analgesic, anti-Alzheimer's, and immunosuppressive effects. Various synthetic methods are currently used to prepare various benzofuran derivatives. Its therapeutic significance is highlighted by the presence of the benzofuran core in several FDA-approved drugs. Due to the development of resistance in existing anticancer therapies, there is an urgent need for novel, effective, and safe therapeutic approaches. Many heterocyclic moieties and their structural hybrids have been explored for their potential biological activity and have attracted considerable attention as anticancer agents. Substituted benzofuran derivatives are emerging as lead candidates to meet the global challenges of cancer and its available treatment. Benzofuran has the chemical formula C8H6O. Structurally, it consists of a fused ring system: a benzene ring fused to a five-membered furan ring (with one oxygen). Both benzene and furan contribute to its aromatic character. Benzofuran heterocycle plays an important role in drug design and drug discovery due to its versatile nature. The current review summarizes the recent progress in the design, development, drug discovery, and pharmacological actions of benzofuran derivatives as anticancer agents; their molecular docking studies; and structure-activity relationships reported over the past five to six years, emphasizing fused heterocyclic analogues and their prospects to develop as lead molecules. A thorough understanding of the structure-activity relationship will provide a valuable framework for novel drug discovery and design.
Gene therapy cargo delivery to specific cell types in the central nervous system (CNS) remains a major challenge for the development of adeno-associated virus (AAV) vectors as a therapeutic modality. Here, we leverage high-plex in situ transcriptomics (10× Genomics Xenium) to spatially map barcoded AAV payloads at subcellular resolution in the intact mouse brain while preserving anatomical context. Tropism profiling of 22 barcoded AAV variants including novel AAV9 derivatives and CNS-targeted capsids revealed that established vectors, AAV9-PHP.eB and AAV9-CAP-B10, demonstrated distinct neuronal and non-neuronal subtype preferences, recapitulating previous findings. Several novel AAV variants candidates predominantly transduced endothelial and vascular cells, suggesting limited blood-brain barrier (BBB) penetration. Notably, three novel variants (AAV9-BTX166, -BTX168, and -BTX175) exhibited enhanced endothelial and mural cell tropism, despite robust CNS activity in bulk assays. Intriguingly, the novel variants AAV9-BTX149 and -BTX001 displayed selective targeting of specific inhibitory neuron subtypes, with a single Trp503Arg substitution in the capsid being sufficient to redirect tropism. Our results establish in situ spatial transcriptomics as a powerful tool for resolving AAV biodistribution and BBB traversal capacities at high resolution, providing a blueprint for capsid engineering to achieve precise cell subtype targeting in the CNS.
Diagnosis omission in discharge diagnosis lists is common in electronic medical records (EMRs), leading to inaccurate documentation, incorrect Diagnosis Related Group (DRG) assignments, and reduced reimbursements from overlooked Complications and Comorbidities (CC) or Major Complications and Comorbidities (MCC). To address this, we propose a data and knowledge cross-level fusion-driven learning framework for automated identification of missed diagnoses. Evaluated on real-world EMRs from six hospitals across various provinces in China, our model outperforms expert system method, BERT-based method, and multiple LLM-based baseline methods, demonstrating superior F1 scores. Results show 37.8% of EMRs predicted to have missed diagnoses, with 9.0% experiencing altered DRG groupings, subsequently affecting 3.2% of insurance reimbursement. To minimize alert fatigue, we adopted a hybrid approach combining our model with expert system, boosting precision by 6.7-13.4%. We also designed two human-machine coupling modes to demonstrate the utility of our methods in the real world.
Dysarthria is a neuromotor disorder that occurs as a clinical manifestation of neurovascular and neurodegenerative diseases, including stroke, traumatic brain injury, Parkinson's disease, and lateral sclerosis. These conditions disrupt neuronal and vascular pathways involved in motor speech control, leading to reduced speech intelligibility and communication barriers between dysarthric speakers and the public. Deep Learning (DL)-based approaches are investigated for automated classification of dysarthria severity. Multiple methodologies are experimented with to identify an effective configuration, including baseline Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) models using cepstral features, pretrained networks for feature extraction, and hybrid approaches that combine deep features with Support Vector Machine (SVM) classifiers. An attention-based fusion strategy is further employed on the two best-performing pretrained models to further enhance performance. Attention-based feature fusion framework achieves accuracies of 97.90% ± 0.47 on the TORGO database and 95.31% ± 0.34 on the UA-Speech corpus under utterance-level evaluation, and 62.77% ± 2.38 and 56.26% ± 3.24, respectively, under speaker-independent evaluation. Experimental results indicate that the proposed automated framework consistently outperforms baseline approaches, providing a more robust objective metric for assessing dysarthric speech. Various experiments demonstrate that using pretrained networks as feature extractors, combined with feature engineering, optimally enhances the performance of dysarthria severity classification. The proposed framework serves as a speech-based functional computational proxy for dysarthria severity assessment, enabling objective analysis of motor speech impairment.
Early diagnosis and successful treatment require accurate identification of brain tumors using magnetic resonance imaging (MRI). However, MRI slices often exhibit low contrast, blurred boundaries, and noise, which can negatively affect automated classification performance. Traditional improvement processes can result in excess sharpening of edges or altered structural continuity, which could affect subsequent feature extraction. In this regard, a hybrid method combining fractional Laplacian-based image enhancement with vision transformer (ViT) is developed in two stages. The fractional Laplacian processing enhances the boundaries between structures and preserves structural continuity in the first stage. The second stage is the vision transformer (ViT), which captures local and global dependencies in order to do multi-class classification effectively. The structural similarity (SSIM) and the entropy are used to assess the frameworks by enhancement metrics, and the Accuracy, Precision, Recall, F1-score, and ROC-AUC are used to determine the classification performance. Experiments are carried out using more than one random seed and reported as mean as standard deviation and statistically tested. The highest configuration had a mean test accuracy of [Formula: see text] and the statistical test showed no significant difference between raw and enhanced inputs ([Formula: see text]). Competitive classification performance is shown to be achieved with experimental results under controlled multi-seed evaluation, and therefore, systematic analysis of the influence of fractional improvement on transformer-based classification models.
Foundation models, including language models, e.g., GPT, and vision models, e.g., CLIP, have significantly advanced numerous biomedical tasks. Despite these advancements, the high inference latency and the "overthinking" issues in model inference impair the efficiency and effectiveness of foundation models, thus limiting their application in real-time clinical settings. To address these challenges, we proposed EPEE (Entropy- and Patience-based Early Exiting), a novel hybrid strategy designed to improve the inference efficiency of foundation models. The core idea was to leverage the strengths of entropy-based and patience-based early exiting methods to overcome their respective weaknesses. To evaluate EPEE, we conducted experiments on three core biomedical tasks-classification, relation extraction, and event extraction-using eight foundation models (i.e., BERT, ALBERT, GPT-2, ViT, Qwen, GPT-oss, BioMistral, and Meditron3) across twelve datasets, including clinical notes and medical images. The results showed that EPEE significantly reduced inference time while maintaining or improving accuracy, demonstrating its adaptability to diverse datasets and tasks. EPEE addressed critical barriers to deploying foundation models in healthcare by balancing efficiency and effectiveness. It potentially provided a practical solution for real-time clinical decision-making with foundation models, supporting reliable and efficient workflows.
Anterior controllable antedisplacement fusion (ACAF) is widely used for cervical ossification of the posterior longitudinal ligament, but long-term complications, such as adjacent segment degeneration (ASD), pseudarthrosis, cage subsidence, and implant failure, remain nonnegligible. This study aimed to explore the influence of the number of fusion levels (NFL) on these complications through finite element (FE) analysis, providing a biomechanical basis for optimizing surgical strategies for ACAF. Three FE ACAF models (two-level, three-level, and four-level) were established on the basis of a validated C2-T1 cervical spine model. A hybrid loading protocol with a 75 N follower load and physiological moments was applied to simulate physiological motions. Key parameters, including the range of motion (ROM) of the surgical and adjacent segments, disc stress, facet joint force (FJF), endplate stress, and the plate, screw, and screw-bone interface stresses, were compared among the three models. An increase in the NFL led to significant increases in the ROM, disc stress, and FJF of adjacent segments, with the upper adjacent segment showing more prominent changes than the lower segment. The ROM of the surgical segment gradually increased with increasing NFL, and the fusion space micromotion correspondingly increased. Endplate stress and implant-related stresses (plate, screw, and screw-bone interface stresses) all tended to increase steadily with increasing NFL, reflecting a continuous increase in the mechanical load at the surgical site and in the adjacent segments. The NFL is a potential risk factor for long-term complications of ACAF. An increase in the NFL raises the mechanical load in the surgical and adjacent segments, thereby potentially increasing the risks of ASD, pseudarthrosis, cage subsidence, and implant failure.
Severe dengue (SD) represents a life-threatening progression of dengue virus infection. Early identification of patients at risk of transitioning from dengue fever (DF) to SD remains a major clinical challenge. Unraveling the transcriptomic changes underlying this progression may aid in developing timely therapeutic interventions. RNA-seq datasets comprising 103 samples (62 SD and 41 DF) were retrieved from the GEO repository. Following normalization using DESeq2, differentially expressed genes (DEGs) were identified between the 2 disease stages. Functional enrichment analysis was performed to uncover dysregulated biological processes. A hybrid computational framework combining classical machine learning (Logistic Regression, Support Vector Machine, and Random Forest) and deep learning models (Artificial Neural Network, Convolutional Neural Network, and Transformer-based architectures) were applied to classify SD and DF samples. Model performance was evaluated using ROC-AUC and balanced accuracy metrics. Differential expression analysis identified 55 significantly dysregulated genes distinguishing severe dengue from dengue fever. These genes were enriched in pathways related to metal ion homeostasis, platelet signaling, ferroptosis, and oxidative stress. Among multiple machine learning and deep learning models, the Transformer-CNN achieved the best performance (test AUC = 0.85; balanced accuracy = 0.89). SHAP-based interpretation highlighted ILDR2, TCP1, HNRNPUL1, SEC14L5, ATP2C2, LOXL3, ACVRL1, STEAP3, and ST8SSIA5 as key discriminative features. Integrated network analyses further implicated coordinated regulation of iron metabolism, calcium signaling, and platelet dysfunction in severe dengue. This study integrates RNA-seq and hybrid Artificial Intelligence modeling to identify transcriptomic signatures associated with dengue severity. The study highlights candidate genes and pathways that provide a hypothesis-generating foundation; further increasing the sample size and experimental validation will support early risk stratification in severe dengue.
Over the past two decades, the Student Council Symposium (SCS), the flagship event of the ISCB Student Council, has grown into a vital forum for early-career researchers in computational biology. Since its inception in 2005, the SCS has served as a platform for scientific exchange, skill development, and community building in a student-led, globally inclusive environment. The 20th edition, held in 2024 in Montréal, Canada, continued the symposium's tradition of global engagement and hybrid accessibility, reaffirming a commitment to in-person dialogue. This article presents a comprehensive retrospective of the evolution of computational biology through the lens of SCS. We trace key advances from genome-scale analyses and structural modeling to single-cell and AI-driven bioinformatics. Based on SCS2024 talks and keynotes, we illustrate how emerging interdisciplinary methods have reshaped the field. We also highlight parallel efforts in global education, regional expansion, and equity, diversity, and inclusion initiatives. This retrospective shows how SCS has not only reflected the transformation of the field but also played a key role in shaping emerging leaders in bioinformatics.
The Anterior Cruciate Ligament Injury Severity Scale (ACLISS) was developed to classify the magnitude of damage to knee structures beyond the anterior cruciate ligament (ACL) (meniscus, cartilage, collateral ligaments, etc) at the time of ACL rupture. However, its validity in predicting clinical outcomes after ACL reconstruction (ACLR) has never been assessed. To determine whether ACLISS correlates with reoperation and patient-reported functional outcomes after ACLR. Cohort study; Level of evidence, 3. The records of all patients who underwent primary ACLR at a single institution between 2019 and 2022 with minimum follow-up of 2 years were reviewed. Patients were excluded if they had concomitant collateral ligament or posterior cruciate ligament repair/reconstruction or prior ipsilateral ACLR. ACLISS scores (0-12) and grades (grade 1: scores 0-3; grade 2: scores 4-7; grade 3: scores 8-12) were determined using preoperative magnetic resonance imaging and intraoperative arthroscopic findings based on the original published technique. The primary outcome was reoperation after ACLR. Secondary outcomes included International Knee Documentation Committee (IKDC) subjective scores and Marx activity scores. Bivariable and multivariable logistic regression analyses were performed to identify predictors of reoperation. Cox proportional hazards modeling and Kaplan-Meier survival analysis were used to evaluate time to reoperation. Statistical significance was defined as a P value <.05. A total of 324 patients met the inclusion criteria. The mean age was 29.3 ± 13.6 years, and 50.9% of the patients were male. The mean follow-up was 5.1 ± 0.8 years. Of the patients, 177 (54.6%) were classified as ACLISS grade 1 damage, with a mean score of 2.3 ± 0.9; 141 (43.5%) as grade 2, with mean score of 4.8 ± 0.9, and 6 (1.9%) as grade 3, with mean score of 8.2 ± 0.4. Overall, 87 (26.9%) patients required medial meniscus repair, and 82 (25.3%) patients required lateral meniscus repair. The overall ACL revision rate was 4.0%. A total of 34 (10.5%) patients had reoperation for any reason. The mean IKDC score was 84.4 ± 14.2, and the mean Marx score was 8.6 ± 5.4. There was no significant association between ACLISS grade and reoperation rate (grade 1: 10.2%; grades 2 and 3: 10.9%; P = .832). In multivariable analysis, hybrid autograft with allograft augmentation was significantly associated with increased reoperation risk (OR, 7.68; 95% CI, 1.82-32.4; P = .006). Survival analysis revealed that patients with grades 2 and 3 experienced earlier reoperations, with 69% occurring between 5 and 15 months compared to 22% for grade 1 (P = .0086). IKDC and Marx scores did not differ significantly by ACLISS score. While ACLISS grade does not predict overall reoperation rates or functional outcomes when concomitant injuries are appropriately managed, patients with higher grades experienced earlier reoperation.
Diabetes Mellitus (DM) is a metabolic disorder that can be defined as sustained hyperglycemia, a state in which the glucose level in the blood is consistently elevated. This condition results from either the cells of the body becoming resistant to insulin or the secretion of insulin being insufficient. In recent years, hybrid drug design has shown potential for treating complex diseases, as it combines the effects of two or more pharmacophores within a single molecular structure. Coumarin and its derivatives have shown considerable attention in medicinal chemistry due to their versatility and designing of potential compounds. In recent years, coumarin derivatives have been prepared by linking the coumarin core with other pharmacophores and generating novel compounds with enhanced antidiabetic potential. In this review we have discussed various coumarin derivatives with conjugated rings, such as thiazolidinedione, benzimidazole, oxazole, and oxadiazole, their structural aspects, and structure-activity relationships for the generation of novel compounds. We also summarised the multiple mechanisms of antidiabetic action exhibited by coumarin derivatives and their conjugates, including inhibition of α-glucosidase, α-amylase, DPP-4, and aldose reductase, and the regulation of insulin sensitivity through PPAR-γ activation. The analysis of SAR revealed that the presence of electron-donating substitution in the coumarin nucleus, as well as the choice of heterocycle and the type of linker (alkyl, aryl, amides, or esters), were the major factors responsible for potency and selectivity against diabetes. The review may assist the medicinal chemist in the discovery of novel compounds as antidiabetic agents with improved efficacy, safety, and multi-target profiles.
In this work, two efficient hybrid transform methods known as the natural homotopy perturbation method (NHPM) and the [Formula: see text]homotopy analysis transform method ([Formula: see text]HATM) are utilised to investigate analytical solutions of the time-fractional coupled Korteweg-de Vries equations and time-fractional Kersten-Krasil'shchik coupled KdV-mKdV equations. These models are considered under the framework of the time-fractional Atangana-Baleanu derivative. The NHPM combines the natural transform method and the homotopy perturbation method, while the [Formula: see text]HATM combines the [Formula: see text]homotopy analysis method and the Laplace transform method. These methods employ an iterative approach to generate rapidly convergent series solutions for the considered problems. We demonstrate the efficiency of both solution methodologies for a variety of test problems. Numerical results are obtained for the considered test problems when the fractional order ϱ is equal to 1. These results are then compared with the exact solutions. Error estimates are also obtained for each test problem with respect to the considered methods. The obtained numerical results are close approximations of the exact solutions, thus demonstrating their accuracy and validity. Furthermore, graphical representations in both 2D and 3D are presented for the considered problems to depict the dynamic behaviours of system wave profiles and surface plots for distinct values of the fractional parameter. These results further emphasise the simplicity and straightforwardness of the methods, which make them applicable to complex nonlinear time-fractional systems that model diverse real-life processes.
Molecular docking has emerged as a cornerstone methodology in computational drug discovery, enabling the prediction of ligand-receptor interactions with considerable accuracy and efficiency. This article provides a comprehensive overview of docking fundamentals, including its workflow, scoring functions, and various types, ranging from rigid to flexible and ensemble docking approaches. Docking serves as an essential tool for virtual screening, lead optimization, and structure-based drug design, significantly reducing experimental costs and accelerating the identification of therapeutic candidates. The review details contemporary scoring strategies such as force-field-based, empirical, knowledge-based, and consensus scoring, highlighting their respective strengths and documented limitations. Additionally, a comparative evaluation of widely used docking platforms such as AutoDock, MOE, GOLD, Glide, and MVD is presented, incorporating recent benchmarking results and practical considerations. Special emphasis is placed on the integration of molecular docking with machine learning, artificial intelligence, molecular dynamics simulations, and other computational methods. Innovations such as deep learning architectures, AlphaFold-based structural modeling, reinforcement learning, and cloud-based high-throughput screening are redefining the predictive power, scalability, and clinical relevance of docking. Applications extend across drug discovery, drug repurposing, natural product research, and personalized medicine. The article also discusses critical challenges such as protein flexibility and scoring inaccuracies, and reviews emerging hybrid solutions designed to enhance accuracy and reliability. The review underscores the transformative impact of molecular docking in modern drug development.
This paper proposes a radio frequency (RF)/free space optics (FSO) communications hybrid system that will improve security and reliability of future sixth generation (6G) wireless communication network links with practical channel conditions. The system uses the composite Weibull-Lognormal (WLN) turbulence model for modeling the free-space-optics (FSO) link and incorporates the effects of both local fade events and global weather phenomena; it also uses Nakagami-m/Rayleigh fading to model the RF link. A hybrid link selection algorithm (HLA) is used to select the best available transmission link as a function of real-time channel characteristics. The performance of the proposed hybrid system is analyzed from three perspectives: secrecy capacity, bit-error-rate (BER), and outage probability under different fading/turbulence conditions through an exhaustive Monte-Carlo simulation process and supported by analytical results. These analyses show that this hybrid system has significant advantages over single-link systems employing either RF or FSO alone; these advantages include reduced outage probability, improved BER performance, and higher secrecy capacity especially when operating under high-turbulence conditions. These results show that a composite-fading architecture provides a reliable and secure framework for the development of fifth-generation (5G)-like communication systems which can be used in future sixth-generation (6G) wireless communication networks.
To develop a motion-resolved acquisition and reconstruction framework for motion-robust and spectrally reliable abdominal CEST imaging under free-breathing conditions. A framework termed Golden-angle RAdial CEST MRI with MOtion-REsolved reconstruction (GRACE-MORE) was developed to improve acquisition, respiratory-phase binning, and reconstruction. An interleaved steady-state saturation module with golden-angle radial sampling was adopted to enhance saturation efficiency and ensure uniform k-space coverage. Respiratory-phase binning was performed using a self-gated strategy, in which respiratory amplitude and phase were extracted from central k-space via principal component analysis, followed by hybrid empirical mode decomposition-continuous wavelet transform sorting to correct baseline drift and irregular breathing. Reconstruction was performed using a deep unrolled network constrained by low-rank and sparsity priors, which incorporated a modified U-Net with channel attention and a sliding-window grouping scheme to capture spatio-spectral correlations across saturation offsets. Validation on simulated, preclinical, and clinical datasets demonstrated more than a threefold improvement in binning accuracy compared with conventional methods. GRACE-MORE reduced reconstruction error, yielded a higher structural similarity index and peak signal-to-noise ratio in simulations, and achieved improved anatomical fidelity, effective motion suppression, and up to a fourfold increase in Z-spectral signal-to-noise ratio in vivo. GRACE-MORE enables motion-robust and spectrally reliable abdominal CEST imaging under free-breathing conditions.