Generative models based on diffusion and flow matching have recently been applied to structure-based drug design, but their outputs often include unrealistic protein-ligand interactions that do not obey the laws of physics. We present an energy guidance framework that incorporates a molecular mechanics force field (MMFF94) directly into the sampling process. The method steers molecular generation toward more physically plausible and energetically stable conformations without retraining the underlying model. We evaluate this approach using two state-of-the-art architectures, SemlaFlow, a flow matching model and EDM, a diffusion model, on the PDBBind dataset. Across both models, energy guidance improves enthalpic interaction energy, improves strain energy by up to 75%, and generates over 1000 ligands with better docking scores than native ligands. These results demonstrate that lightweight, physics-based guidance can significantly enhance generative drug design while preserving chemical validity and diversity. SCIENTIFIC CONTRIBUTION: We introduce a novel, training-free force field guidance framework that steers ligand generation using empirical molecular mechanics (e.g., MMFF94) during diffusion or flow-based sampling-without modifying or retraining the base generative model (e.g., EDM or Semflaflow by [24]). Our method operates as a plug-in during inference time, leveraging energy feedback to generate poses with lower strain and having better predicted interactions with the protein structure. Our main contributions are as follows:Energy-based guidance without retraining: Unlike methods that require gradients from neural affinity predictors (e.g., BADGER [26]), our approach injects classical force field feedback (MMFF94) directly during the posterior sampling step.Improved docking and strain metrics: In benchmarks against unconditional EDM and Semflaflow, our guided inference yields consistently better AutoDock Vina scores and lower ligand strain energy, even after optimizing the final structures using the same force field.Compatibility and flexibility: Because the guidance module is external, it can be applied broadly to multiple generative backbones-without retraining or architecture modifications, and can be applied to arbitrary differentiable potential energy functions.Theoretical guarantee of stability. We demonstrate in Appendix B that the gradient correction step corresponds to a descent step on the energy under standard smoothness assumptions. While the full sampling update also includes model-driven (and, in the diffusion case, stochastic) components, this result formalizes how the guidance term locally biases the trajectory toward lower-energy regions and provides a principled justification for its stabilizing effect.
This study aimed to examine 8th-grade students' views on the concepts of nanotechnology and nanoscience through the use of the Metaverse in science courses. The study group sample consists of five students from both the before- and after-experience groups, all of whom are in 8th grade. This study employed a qualitative research method with a case study design. Observation, interview, and document analysis were used as data collection tools. Necessary measures have been taken to ensure the validity and reliability of the research within its scope. The data were analyzed using a content analysis approach. As a result of the interviews, data were collected and analyzed. As a result of the textual examinations, code, category, and theme were determined. The findings were presented in categories through tables, and the participants' answers were included in direct quotations. Upon reviewing the literature, it becomes apparent that most studies in nanotechnology and nanoscience are conducted for informational purposes, typically presented as presentations or reports. Given the limited availability of nanotechnology and metaverse education, the study was divided into two groups: a before-experience group and an after-experience group. As a result of the survey, 8th-grade students experience the metaverse and have future expectations for nanotechnology and nanoscience. Their cognitive and affective interests have increased, as evidenced by their questioning why these applications cannot be applied to all courses and by their correct expression of the concepts. At the same time, it has been concluded that using rich materials to concretize abstract concepts, such as nanotechnology, facilitates their teaching. The study provides qualitative evidence that Metaverse-based instruction can enhance both cognitive and affective dimensions of science learning, offering design implications for integrating immersive technologies into middle school curricula to teach abstract concepts.
Insurance fraud detection remains challenging to predict in reality because claims data is often uneven among classes, and the information concerning claims is often multidimensional and nonhomogeneous. The present research used a unified evaluation framework to assess the predictive and interpretive capabilities of three distinct model families: CatBoost (tree-based ensemble learning), Bi-GRU with Attention (sequence-oriented learning), and TabTransformer (categorical feature contextual). The model families were tested using a standardised experimental protocol.The study is novel in the sense of a cross-model interpretability framework that unites Shapley Additive Explanation (SHAP)-based feature attribution with attention-based contextual analysis to enable a clear comparison of model reasoning between the suggested frameworks. The data on which the experiments were done consisted of 4,000 life insurance claims that were characterized in terms of 83 attributes. Common preprocessing procedures like missing values, scaling numerical variables, and selecting highly correlated variables were used before training the models. Experimentally, CatBoost is proven to be the most precise on legitimate claims, Bi-GRU is the most recall on fraudulent claims, and TabTransformer is the best in terms of tradeoff between accuracy, interpretability, and computational efficiency. Practical characteristics such as the quantity of claim, tenure in a policy, and diagnosis were repeatedly emphasized in both SHAP and attention analyses. Combined, the current research study provides a consistent and explainable benchmark that may be applied to conduct fraud detection research reliably and assist practitioners in choosing models that are accurate and understandable.
Cis-regulatory elements (CREs) drive tissue- and cell-specific gene expression and are essential for safe, sustainable genetic control strategies in pest and vector insects, including the engineering of gene drives in the primary human-malaria vector Anopheles gambiae. Yet CREs remain poorly defined in mosquitoes due to limited computational tools and practical methods for identification and validation. We present a systematic in silico approach for CRE discovery, correlating targeted DNA-motif searches with gene expression, followed by frequency and distribution analysis within putative promoter regions. Applied to the A. gambiae germline, this approach identified hundreds of putative CREs significantly correlated with germline expression in one or both sexes, often linked to distinct sperm developmental stages and chromosomal locations, suggesting roles in broader regulatory mechanisms such as dosage compensation and meiotic silencing. When mapped onto pre-characterised germline promoters, CRE distribution aligned with regions associated with experimental expression patterns. Finally, we validated a top-ranked testis-enriched CRE using an in vivo dual-reporter assay, showing that mutation of conserved nucleotides drastically altered male germline expression. To the best of our knowledge this work provides the first nucleotide-resolution regulatory genome annotation of the A. gambiae germline, offering a transferable framework to aid promoter design for genetic control strategies against malaria mosquitoes and other insect pests.
Ranked Set Sampling (RSS) is known for its efficiency in parameter estimation, especially when ranking is more feasible than actual measurement. This study introduces a novel memory type estimator for RSS based on Hybrid Exponentially Weighted Moving Averages (HEWMA), using two auxiliary variables. The estimator aims to enhance efficiency by integrating current and past information along with secondary auxiliary data. Analytical expressions for the bias and mean square error (MSE) are derived, and the corresponding sample coefficients are obtained to simplify HEWMA weight calculations. A simulation study is conducted to evaluate the estimator under various conditions, including different sample sizes, distributional shapes (normal and skewed Weibull), and varying correlation levels among study and auxiliary variables. Results indicate that the proposed estimator consistently yields the highest relative efficiency (RE) compared to conventional and memory estimators using one or multiple auxiliary variables. Additionally, the estimator is applied to two real-world mortality datasets from the USA, involving deaths from tobacco- and alcohol-related cancers. Despite challenges such as zero values and small sample sizes, the estimator maintains superior performance. Overall, the proposed estimator offers an improvement in estimation performance for RSS, particularly in settings where auxiliary variables are available and memory type estimators are applicable.
Gob-side entry driving is widely applied in deep coal mines, where rapid unloading of surrounding rock on the gob side induces stress redistribution, and the coal pillar is consequently regarded as a key load-bearing structure. The stability of the roadway is governed by the competition between elastic elastic strain energy and dissipated energy within the coal pillar. To address the difficulty of identifying stability state transition points in coal pillar width design under deep burial and weak rock conditions, this study analyzes the surrounding rock response from an energy perspective and establishes an energy analysis framework based on the coupling of elastic elastic strain energy and dissipated energy, with the dissipated energy ratio introduced as an evaluation index. Based on FLAC3D numerical simulations, the spatial distribution and evolution of elastic strain energy, dissipated energy, and dissipated energy ratio under different coal pillar widths are investigated. The results indicate that when the coal pillar width increases from 4 to 6 m, the bearing mechanism gradually shifts from plastic dissipation-dominated behavior to an elastoplastic coordinated state dominated by elastic elastic strain energy, with the dissipated energy ratio decreasing from 1 to approximately 0.67. When the width further increases to 8 ~ 14 m, elastic strain energy rapidly accumulates in the central region of the coal pillar, resulting in the formation of a pronounced energy concentration zone. Compared with traditional indicators based on stress, displacement, and plastic zone distribution, the dissipated energy ratio is more effective in characterizing. Considering energy evolution characteristics, bearing capacity, and engineering economy, a 6 m coal pillar is considered to achieve the most favorable balance under the conditions of the studied mine. Field monitoring results further verify the engineering applicability of the proposed energy-based criterion and coal pillar width optimization scheme.
The transition to sustainable agriculture requires technologies that simultaneously enhance crop yields and reduce environmental impacts. Solar-driven nitrate valorization, when coupled with CO2 capture from industrial flue gas, presents a promising dual strategy for producing high-value fertilizers while mitigating carbon emissions. However, its practical implementation is hindered by two interrelated challenges: (i) the intermittent nature of solar irradiation and (ii) the competitive hydrogen evolution reaction (HER), which severely compromises Faradaic efficiency (FE) of desired nitrogenous products. Here, we address these challenges by designing a heterogeneous CuPd electrocatalyst featuring an amorphous/crystalline heterojunction. This catalyst suppresses HER across a broad potential window (-0.4 to -1.4 V), maintaining >80% FE(ammonia) for >100 h. The catalytic robustness enables stable solar-powered electrolysis even under low irradiation (0.4 sun), achieving >70% FE(ammonia) and 6% solar-to-fuel conversion efficiency, while catholyte simultaneously captures CO2 at a rate of 6-20 mg h-1. Techno-economic analysis demonstrates cost competitiveness against biological counterparts. When applied to plant cultivation, this artificial photosynthesis system boosts solar-to-biomass conversion efficiency by 3.5-fold compared to natural photosynthesis. By unifying solar energy harvesting, waste nitrate reduction, and carbon sequestration, our work provides a scalable blueprint for a closed-loop agrochemical ecosystem and advanced catalyst design for intermittent renewable-powered electrosynthesis.
The occipital interhemispheric transtentorial approach (OITT) is widely used for accessing lesions in the pineal region. Although reports are scarce, this approach can also be successfully applied to superior cerebellar lesions, involving the quadrangular lobule. We describe the OITT approach for cerebellar quadrangular lobe lesions, providing relevant anatomy, surgical technique, and key technical considerations. Gross total resection can be achieved while preserving normal brain tissue and functionOITT is a safe and low-morbidity route for lesions within the cerebellar quadrangular lobe, as it uses a natural anatomical corridor, avoids manipulation of eloquent areas, and minimizes injury to normal brain tissue.
To develop a semi-automated method to segment "black hole" lesions on post-gadolinium 2D T1-weighted images (GdT1) in multiple sclerosis (MS) that follows radiological intensity rules and perform multi-center validation. Multi-center spin-echo GdT1 images and accompanying proton-density (PD)/T2-weighted images and manual T2 lesion masks of the REFLEXION study (NCT00813709) of suspected/early MS were used. Briefly, the proposed method segments cortical gray matter (GM) to derive a T1-weighted intensity threshold, which is applied inside co-registered T2 lesion masks to segment black hole lesion voxels. It was optimized on a training set (N = 40, 57.5% female, mean age 31.4 ± 8.7 (standard deviation) years), and 274 patients formed the test set (61.3% female, age 31.8 ± 8.4 years). Performance was quantified by the Dice similarity coefficient (DSC) and the intraclass correlation coefficient (ICC) for absolute agreement with manual segmentations. Lesion-wise sensitivity and specificity were calculated. Optimization resulted in: (1) GM selection as minimally 0.8 total WM plus GM partial volume, masked by MNI cortex; (2) normalized mutual information-driven linear co-registration of T2 to GdT1 images, interpolating T2 lesion masks using trilinear interpolation and 0.6 threshold; (3) mean intensity inside GM mask used as upper intensity threshold. The optimized method had acceptable spatial accuracy (DSC: 0.39 ± 0.26) and good volumetric accuracy (ICC: 0.84, 95% CI [0.72, 0.90]. Lesion-wise sensitivity was 0.91 ± 0.19, and lesion-wise specificity was 0.62 ± 0.22. The proposed method to semi-automatically segment black holes from post-gadolinium T1-weighted images shows acceptable performance. As a potential aid to radiologists, the method is not recommended to be used entirely without human intervention. Question T1-hypointense "black hole" lesions reflect disease severity in multiple sclerosis but are not routinely quantified due to a lack of reliable analysis methods. Findings A rule-based semi-automated method for GdT1 "black hole" lesion segmentation was developed and optimized, and then validated in a large unseen multi-center test set. Clinical relevance This method adds quantitative information about GdT1 "black hole" lesions to the radiological assessment of multiple sclerosis disease severity, when false positives are manually removed. This can enhance the characterization of individual patients and advance the understanding of the disease.
This study investigates the identification of Benign Prostatic Hyperplasia (BPH) through a deep learning-based analysis of RGB prostate histopathological images. Adaptive Contrast Limited Adaptive Histogram Equalization (CLAHE) is selectively applied to the L-channel in the LAB color space to enhance tissue visibility while preserving chromatic fidelity. At the architectural level, Convolutional Neural Networks (CNNs) are integrated with Bidirectional Long Short-Term Memory (BiLSTM) layers, enhanced further through spatial and temporal attention mechanisms. This hybrid design facilitates both localized pattern recognition and the modeling of long-range contextual dependencies across tissue regions. To mitigate class imbalance and prevent overfitting, the training regime incorporates two key strategies: an adaptive focal loss function and a comprehensive image augmentation protocol. The proposed model achieved an AUC of 0.7220 on the validation set and an AUC of 0.73 on the test set. While the precision for normal tissue classification remained high, the recall for BPH detection highlighted the need for improvement in sensitivity. The proposed CNN-BiLSTM-Attention architecture demonstrates potential as a diagnostic aid in digital pathology, offering interpretable insights and forming a foundation for enhancing histological classification systems. Future work will focus on improving recall performance for BPH detection and expanding the architecture to support multi-class prostate disease grading frameworks. This study utilizes an RGB histopathological dataset consisting of 176 prostate images, each appropriately annotated. The model demonstrates moderate classification performance and a moderate true-positive rate for detecting Normal samples. The model, however, has a low sensitivity in the detection of the cases of BPH as indicated by the relatively low recall values.
This study investigates the impact of online gambling on problem behaviors among South Korean adolescents across three phases of the COVID-19 pandemic: pre-pandemic (2018), early pandemic (2020), and late pandemic (2022). We applied a doubly robust estimation approach that combines propensity score matching with regression analysis using nationally representative survey data from the Korea Problem Gambling Agency. Our findings indicate that online gambling significantly intensified adolescents' problem behaviors in all periods, with a more pronounced effect observed during the late pandemic phase in 2022 compared to the early phase in 2020. Sensitivity analysis further demonstrated that the estimated effects were substantially robust to unobserved confounding, particularly in 2018 and 2022. We conclude with a discussion of adolescents' heightened vulnerability to online gambling-related problem behaviors and the corresponding need for targeted interventions and policy responses.
As the primary living environment for disabled older adults, families play a crucial role in disease prevention and maintaining their health. However, research has found that both disabled older adults and their family members experience numerous physiological, psychological, and social adaptation problems when adjusting to the changes brought by disability, severely impacting the overall health status of the family. Therefore, guided by the ERG (Existence-Relatedness-Growth) theory, this study aims to understand the family health needs of families with disabled older adults in the community, providing a basis for improving the health level of these families and developing targeted intervention programs. From December 2024 to February 2025, this study employed purposive and snowball sampling to select 12 pairs of disabled older adults and their primary caregivers from communities under the jurisdiction of Zhengzhou City, Henan Province for semi-structured interviews. Thematic analysis was applied to organize and analyze the interview data. Deductive analysis indicated that the famliy health needs of families with disabled older adults in the community can be summarized into the following three themes: existence needs (daily living needs, economic support needs, environmental modification needs), relatedness needs (family communication needs, social resource connection needs, social participation needs), and growth needs (autonomy and dignity maintenance needs, family development needs, demand for technology-enabled solutions). The results show that the family health needs of families with disabled older adults in the community are unique and diverse. Community health workers and social workers can develop and implement effective strategies based on the different levels of family needs to promote the health level of families with disabled older adults and improve the overall quality of life of these families.
Ongoing neurodevelopmental care is essential for children with congenital heart disease (CHD). Understanding delivery and uptake of neurodevelopmental care pathways can inform implementation and resource planning. This study applied simulation modelling to explore outcomes from a neurodevelopmental care pathway for children with CHD. The model was developed using data from a Queensland program to explore health service interactions for neurodevelopmental screening, formal assessment, and early intervention, up to five years. Modelling was intended to provide a baseline understanding of the pathway, rather than evaluating against a reference standard. Hypothetical scenarios explored how changes in screening and referrals influenced the identification of developmental concerns, and how developmental concern severity affected intervention referrals. Based on available data, 58% of the cohort remained under routine surveillance and 25% had accessed early intervention for one or more developmental delays. Scenarios defined by increased screening projected up to 55% of the cohort having a developmental concern identified during screening and 45% having a developmental delay identified following assessment. Simulation modelling was useful for understanding outcomes from a neurodevelopmental pathway and how differences in screening and assessment affected health service interactions. Findings may inform policy and resource planning for future neurodevelopmental pathways. This study shows that simulation modelling is a useful approach for evaluating a neurodevelopmental care pathway for children with CHD, to understand movement through neurodevelopmental screening, assessment, and interventions. Scenario-based modelling provides insights into factors influencing pathway engagement, contributing evidence to strengthen understanding of service gaps and areas where improvements can most effectively impact engagement and resourcing. This study identifies neurodevelopmental screening as the most influential stage impacting downstream outcomes, underscoring its importance as a strategic intervention point. This study's approach provides a general framework for evaluating similar pathways and a potential baseline for assessing future policy or service changes.
Understanding the mechanisms of nickel (Ni) uptake by hyperaccumulator plants is essential for advancing sustainable phytomanagement. In this study, saponite materials containing either isotopically natural or 61Ni-enriched Ni were synthesised and applied in RHIZOtest experiments with Odontarrhena chalcidica. The amendments were mixed with two ultramafic soils differing in Ni content, alongside a serpentinite control. Ni bioavailability and uptake were evaluated via elemental and isotopic analysis of plant digests and diffusive gradients in thin films (DGT). Stable isotope spiking with 61Ni allowed tracing of amendment-derived Ni uptake into plant tissues, even though total Ni mass fractions in planted versus unplanted soils did not indicate significant mobilisation during the 14-day growth period. Isotope pattern deconvolution (IPD) revealed clear shifts in Ni isotopic composition in both plant and DGT samples. Tracer uptake was more pronounced in the low Ni soil, with amendment-derived Ni (xamendment) contributing 19.3 ± 5.0% of total Ni in shoots, compared to 7.7 ± 1.8% in the high-Ni soil. In standard solutions containing 50 ng g-1 total Ni, isotope pattern shifts were still detectable at enrichment levels as low as 0.01% xspike (≈ 5 pg g-1 61Ni). The findings demonstrate the sensitivity of stable isotope spiking combined with IPD in the detection of subtle uptake processes, even in short-term experiments. This approach enables the differentiation of various Ni sources in soil-plant systems that would not be achievable with quantification alone, and can thereby provide new insights into how soil mineralogy influences uptake dynamics in metal-hyperaccumulating species.
Health-related quality of life (HRQoL) is a vital indicator of evaluating care outcomes and prognosis, yet little is understood about its developmental trajectories in older patients with chronic pain. This study aimed to identify latent HRQoL trajectories and their predictors, and to develop explainable machine learning models for predicting HRQoL deterioration. This prospective cohort study assessed 608 older patients with chronic pain at admission and at 1, 3, and 6 months post-admission, collecting data on HRQoL, general characteristics, pain level, activities of daily living (ADL), depression, and perceived social support. Growth mixture modeling was applied to identify trajectories of physical and mental HRQoL. Predictors were selected using LASSO regression and SVM-RFE. Nine explainable machine learning models were developed for both components, and SHAP interpreted the outputs. An HRQoL decision-support dashboard was developed to facilitate potential clinical application. Three physical HRQoL trajectories were identified: Stable High, Decline and Low Stability, alongside two mental HRQoL trajectories: Improvement and Decline. Key predictors included education level, pain duration, pain level, ADL, depression, and perceived social support, with ADL and pain level being the most influential for physical and mental HRQoL, respectively. This dual-trajectory study identified five distinct HRQoL patterns in older patients with chronic pain, elucidating key predictors via explainable machine learning. The proposed HRQoL decision-support dashboard may provide an interpretable tool to support understanding of predictive relationships and assist healthcare professionals in HRQoL assessment. Not applicable.
Paris polyphylla Smith var. yunnanensis (Franch.) Hand.-Mazz. (P. polyphylla var. yunnanensis) is a perennial herb of the genus Paris. As an important medicinal resource, P. polyphylla var. yunnanensis is facing exhaustion due to the high demand and its specific growth characteristics. To efficiently utilize its resources, the response surface methodology (RSM) was utilized to optimize the pectinase-assisted extraction process of polyphyllins from its rhizome, with the total extraction content of polyphyllin I, II, and VII as the evaluation index. The optimal conditions were as follows: extraction temperature of 52 °C, extraction time of 34 min, and solid-to-liquid ratio of 1:19 g/mL. Under these conditions, the total content of the three polyphyllins was 29.70 mg/g, which was close to the predicted value of 29.90 mg/g and represented an increase of 27.63% over the control group. The analysis of variance (ANOVA) showed that the RSM model exhibited a good fit, and the Box-Behnken design (BBD) could be applied to optimize the extraction process of polyphyllins. This study provides a theoretical basis and a reference approach for the efficient utilization of P. polyphylla var. yunnanensis resources.
The Clinical Genome Resource (ClinGen) Von Hippel-Lindau (VHL) Variant Curation Expert Panel (VCEP) has created variant classification specifications tailored to the VHL gene, including phenotype-driven and evidence-based criteria, utilizing somatic and germline mutational hotspots, along with functional and in-silico data. Using the American College of Medical Genetics and Genomics (ACMG) guidance and the ClinGen Sequence Variant Interpretation (SVI) recommendations, the VCEP made substantial modifications to 8 evidence codes (PVS1, PS3, PS4, PM1, BS2, BS3, BS4, BP5), while 14 had minor changes, and 6 were not used (PM3, PP2, BP1, PP4, PP5/BP6). The VHL VCEP applied two literature sets of over >428 papers in Clinical Interpretations of Variants in Cancer (CIViC) and >8700 structured annotations using Hypothesis. From 31 pilot variants, 15 remained pathogenic/likely pathogenic, 9 resolved to benign through the stand-alone benign evidence code while 7 variants with initial uncertain classifications lacking additional evidence, remained uncertain. The versioned VHL VCEP specifications are publicly available in the ClinGen Criteria Specifications Registry and will enhance the transparency and consistency of variant classifications for this highly sequenced hereditary cancer gene.
Repetitive noxious stimulation can increase perceived pain intensity, a phenomenon known as Temporal Summation of Pain (TSP), thought to reflect central sensitization via neuronal "wind-up" in the spinal cord. As neuronal wind-up occurs only at stimulation frequencies above 0.2 Hz, we have tested whether TSP also appears at two different frequencies using our recently developed TSP protocol in healthy volunteers. In a randomized crossover design, 30 healthy male participants (27±4 years) underwent two experimental sessions involving 90 repetitive heat stimuli applied to the forearm at individually determined pain tolerance temperatures. Stimuli were delivered using a thermode at either 0.4 or 0.15 Hz. Pain intensity was rated using a computerized visual analog scale (0-100). TSP was assessed via linear mixed-effects model (LMM), with pain intensity as the dependent variable. All participants finished the study. LMM revealed a significant main effect of stimulation frequency (F 1, 540=14.20, p<0.001), indicating TSP. Pain intensity was higher at 0.4 Hz compared with 0.15 Hz (β=14.77, 95 % confidence intervals (CI) 6.87-22.68, p<0.001). The presence of TSP at 0.4 Hz but not at 0.15 Hz aligns with previous findings on neuronal wind-up, supporting its reliance to central sensitization. These findings enhance our understanding of the physiological basis of TSP and offer a robust platform for future investigations into pain modulation and therapeutic intervention strategies.
Internet gaming disorder (IGD) is associated with abnormal functional connectivity (FC) in brain networks. However, findings from resting-state functional magnetic resonance imaging studies are highly inconsistent, likely due to individual heterogeneity in IGD-related neural alterations-a feature commonly observed in other psychiatric disorders but understudied in IGD. We applied normative modeling to nucleus accumbens (NAcc) seed-to-voxel resting-state FC to derive individualized deviation (Z) maps for 173 IGD participants relative to age- and sex-adjusted normative ranges from 232 healthy controls. We then performed exploratory unsupervised clustering of network-level deviation features across three sample data, three atlas templates, and two clustering algorithms, selecting the optimal number of clusters using the silhouette criterion. IGD showed marked heterogeneity in FC deviations: voxel-level deviations were largely idiosyncratic in both spatial distribution and direction. When deviations were summarized at the network level, clustering consistently selected a two-cluster solution across data, atlases, and algorithms, separating a majority "low-deviation" stratum from a smaller "high-deviation" stratum. IGD is characterized by pronounced individual variability in FC deviations. Network-level deviations yield a robust higher- vs. lower-deviation stratification, although the present findings do not support interpreting this pattern as evidence for discrete subtypes. The present study highlights the utility of individualized deviation mapping beyond conventional case-control analyses for characterizing heterogeneity in IGD.
Sleep disorders include a range of common problems that affect the quality of sleep at night and, as a result, impact an individual's daily functioning. Treatment protocols vary from over-the-counter products to regulated pharmaceuticals. Melatonin and Tasimelteon are two compounds utilized for severe to moderate sleeping disorders. This study developed and validated a sensitive, simple bioanalytical LC-MS/MS method for the measurement of Melatonin and Tasimelteon in spiked rat brain tissue. Chromatographic analyses were conducted in isocratic mode, with Citalopram selected as an appropriate internal standard. The Supelco Ascentis® Express Phenyl-Hexyl column was used for the stationary phase, and the mobile phase comprised 0.2% formic acid in a mixture of acetonitrile and water (65:35, v/v). A response surface methodology is applied. The Box-Behnken design was used to optimize the influence of three independent factors (acetonitrile%, formic acid%, and flow rate (mL/min)) on the response. The study focused on finding the most significant factors influencing chromatographic separation, namely the resolution between Tasimelteon and Melatonin, as well as the tailing factors of both. Statistical analysis of variance provided the optimal conditions for separating the substances as well as the most influential factors. Validation of the analytical method was conducted in accordance with the International Council for Harmonization guideline M10 related to bioanalytical method validation. The method validated was precise and linear in 55.00-1650 (ng/mL) and 20-600 (ng/mL) for the Melatonin and Tasimelteon, respectively. The validated method's lower limit of quantification values was 55 and 20 ng/mL for Melatonin and Tasimelteon, respectively. For Melatonin, intraday accuracy (recovery, %) ranged from 96.53% to 102.68%, and precision (expressed as relative standard deviation) ranged from 0.26% to 0.96%. And inter-day accuracy ranged from 96.58% to 103.08%, and inter-day precision ranged from 0.33% to 3.55%. Intraday accuracy results for Tasimelteon 99.61%-103. 75% precision results were in the range 0.23%-0.93%; additionally, inter-day accuracy was 99.37-103.87%, and the precision range was 1.04-2.11%. The total run time was 3 min, with retention time for Melatonin and Tasimelteon at 1.9 and 2.5 min, respectively, achieving effective chromatographic separation under optimum conditions. The Red Green Blue 12 score for whiteness was determined to be 79.2%.