共找到 20 条结果
Metamaterials characterized by a negative refractive index are being the object of intense research across a broad range of fields, from mathematics and physics to photonics and engineering. Nonetheless, the development of materials exhibiting this property in the visible spectral domain remains challenging. This situation, along with their potential transformative role in new technological advances, has motivated the scientific community to instrumentally employ computer graphics software to visually explore their interactions with light. In this work, we contribute to the initiatives in this area by describing a white-box methodology aimed at the physically-based simulation and visualization of optical phenomena elicited by these materials. We demonstrate its suitability to applications, both within and outside computer graphics, through the rendering of images depicting these phenomena under different optical scenarios.
Sensorless alignment of two-dimensional (2D) freehand ultrasound scans for three-dimensional US (3DUS) reconstruction offers significant advantages due to its ease of use. Prior approaches have used transducers with motion sensors, which are cumbersome and inconvenient in a clinical setting, linear wobblers, or motorized 2D scanning which suffer from a small field of view (FOV) and low volume acquisition rates. Freehand transverse B-mode data loops from 20 human volunteers (10 males, 10 females) were used for 3DUS reconstruction Our two-stream Physics inspired Learning-based Prediction of Pose Information (PLPPI) model explicitly integrates and utilizes speckle decorrelation as an inductive bias (temporal information) along with spatial information for alignment using 2D convolutions. A correlation layer then synergizes spatiotemporal cues for freehand frame alignment. A residual neural network (ResNet) predicted the spatial location of the input frames. PLPPI outperformed baseline deep learning networks (DLN), i.e. 2D CNN, ConvLSTM, and DC2-Net, with a 13% improvement in global pixel reconstruction error, 59.36% improvement in final drift, and 35.74% in final drift rate over the next best DLN, while requiring significantly less Graphics Processing Unit (GPU) memory. Our model has fewer parameters, requiring less GPU memory to train for freehand 3DUS reconstruction along with a major reduction in computation time (106% speedup and 131% reduction in GPU memory usage) compared to baseline DLN.
FilmQADose is an open-source, Python-based software for radiochromic film dosimetry, designed to provide medical physicists with a freely available, extensible tool for two-dimensional dose verification in radiotherapy. It covers the full film QA workflow, from calibration curve generation to plan-to-film comparison, targeting clinical and research centers that require an accessible alternative to commercial solutions. The software was developed in Python using open-source scientific libraries for image processing, curve fitting, DICOM handling, and gamma analysis. Core functionalities include rational and polynomial calibration models, multichannel dose reconstruction, normalized cross-correlation template matching for automatic alignment, and gamma analysis. Validation was performed using irradiated Gafchromic EBT3 films in square field, pyramidal, and breast IMRT plans. Measured dose maps were compared against treatment planning system calculations using 3%/3 mm gamma criteria. FilmQADose processes TIFF images acquired from flatbed scanners and DICOM RT Dose files exported from treatment planning systems. Output dose maps are generated in DICOM 2D format. The software is implemented in Python with standard scientific Python dependencies and includes a graphical user interface built using PySide6. The source code, documentation, and usage guides are publicly available at: eduardoh27.github.io/FilmQADose. The software supports clinical quality assurance in IMRT dose verification, as well as for research applications in radiochromic film dosimetry methodology. A limitation is the absence of lateral response artifact correction and recent machine-learning-based calibration methods.
暂无摘要（点击查看详情）
Besides improving drug solubility and stability, co-amorphous systems (COAMS) have recently been reported to enhance the pulmonary delivery efficiency of dry powder inhalation (DPI), offering a novel approach to the development of DPI. Conventional drug formulation development utilizes trial-and-error experiments, which require a laborious workload and resources. Moreover, the correlation of forming COAMS to enhanced pulmonary deposition remains underrepresented. Therefore, we proposed applying multiple machine learning (ML) models to the development of co-amorphous DPIs. In this study, we first constructed the database of COAMS through literature mining and then preprocessed the dataset with a molecular representation method. Subsequently, we successfully developed and evaluated the predictive performance of multiple ML models (i.e., logistic regression, random forests, XGBoost, LightGBM, and support vector machines) for forming a co-amorphous system. The five ML models' performance varied, yet all achieved satisfactory predictive accuracy (ACC) of around 0.80 in the testing subset. Specifically, LightGBM exhibited the highest ACC of 0.790 in cross-validation and 0.845 in the testing subset. In addition, SHapley Additive ex Planations (SHAP) analysis revealed that several molecular features (i.e., API_EState_VSA10, Co-former_BCUT2D_MRHI) are critical for models' prediction. More importantly, we conducted experimental validation by using salbutamol sulfate and indomethacin as model drugs to prepare co-amorphous DPIs based on the fine-tuned LightGBM model. The co-amorphous DPIs showed satisfactory aerodynamic performance with fine particle fractions of 41.87%-69.30%. In conclusion, we successfully demonstrated the feasibility of ML for guiding the formation of co-amorphous DPIs, further facilitating the development process in the future.
Patients with solid organ malignancy (SOM) constitute a high-risk group during the COVID-19 pandemic. Current evidence suggests that this population is more likely to experience severe clinical outcomes, higher hospitalization rates, and increased mortality following severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. This study aimed to investigate the clinical course of COVID-19 in patients with SOM and to determine its impact on mortality. This retrospective case-control study included patients aged 18 years and older who tested positive for SARS-CoV-2 by polymerase chain reaction (PCR). As a control group, patients without malignancy but with similar characteristics in terms of age, sex, comorbidities, and vaccination status were selected. Comparisons between the two groups were performed, and risk factors associated with mortality were analyzed. A total of 580 patients with COVID-19 were included in the study, comprising 144 patients with SOM and 436 patients without SOM. Patients with SOM had significantly higher rates of hospital admission (p<0.001), secondary infections (p<0.001), critical illness (p<0.001), intensive care unit requirements (p<0.001), oxygen therapy (p=0.016), and corticosteroid use (p=0.028). In multivariate logistic regression analysis, age (odds ratio [OR]: 1.052), male sex (OR: 2.276), solid organ malignancy (OR: 3.809), C-reactive protein (CRP) >54.7 mg/L (OR: 2.671), procalcitonin >0.110 ng/mL (OR: 2.298), D-dimer >502.5 ng/mL (OR: 2.750), and lactate dehydrogenase (LDH) >412.5 U/L (OR: 2.682) were found to significantly increase the risk of mortality. Notably, the presence of solid organ malignancy was associated with approximately a 3.8-fold higher risk of death. Patients with SOM carry a significantly increased risk of mortality during COVID-19 infection. In this patient group, careful evaluation of age, sex, inflammatory parameters, and biomarker levels is warranted. These findings underscore the importance of early identification of high-risk groups and the implementation of personalized treatment strategies in the management of COVID-19. Graphic Abstract.
Single-cell RNA sequencing has emerged as a powerful approach to reveal cellular heterogeneity within biological systems. With the continuous advancement of high-throughput sequencing technologies, studies are generating vast amounts of complex data, posing a significant challenge for researchers in effective data processing and analysis. To address this issue, we developed SCSEQ, an interactive web-based bioinformatics analysis platform. This platform enables even users without programming expertise to conveniently process and analyze sequencing data. SCSEQ provides a comprehensive workflow encompassing data preprocessing, normalization, clustering, dimension reduction, differential expression analysis, cell type identification, and downstream analyses. The downstream analysis tasks include gene enrichment analysis, transcription factor analysis, cell-cell communication analysis, copy number variation detection, trajectory inference, and pan-cancer analysis. SCSEQ facilitates information transfer between different workflows, accepts various input formats, and generates graphical and tabular outputs. As a user-friendly platform, we enhance user experience through detailed parameter settings and dynamic interactions. This enables users to precisely regulate research processes and customize result figures. Additionally, we provide comprehensive user manuals to assist with parameter configuration and workflow execution. SCSEQ provides an intuitive and convenient solution for single-cell transcriptome sequencing data analysis. Our platform has successfully completed full-process analyses on real-world data with reliable results, demonstrating its applicability in practical scenarios. The platform is available at https://scseq.com.cn/.
On September 22, 2025, the United States government announced that the Food and Drug Administration (FDA) would modify paracetamol (acetaminophen) labelling to warn of possible associations with autism, advising pregnant individuals to avoid the medication. This contradicts professional medical consensus and high-quality evidence, replicating communication failures of the 1998 MMR-autism controversy that caused vaccine hesitancy, disease outbreaks, and trust erosion. This narrative review synthesized epidemiological evidence on paracetamol safety in pregnancy, analyzed the September 2025 announcement through the measles, mumps, and rubella (MMR)-autism crisis lens, and proposed an evidence-based communication framework. We searched PubMed, Embase, Web of Science, and Google Scholar, supplemented with governmental statements, professional responses, and media analysis. The two highest-quality sibling-control studies (Swedish: 2.5 million; Japanese: 200,000 children) reported no causal associations between prenatal paracetamol exposure and neurodevelopmental outcomes after controlling genetic and familial confounding. Conversely, untreated maternal fever and pain carry established risks including neural tube defects, preterm birth, and maternal morbidity. The governmental announcement employed inflammatory categorical warnings contradicting FDA's nuanced advisory and scientific consensus. Professional organizations immediately issued strong rebuttals. This replicates MMR failures: governmental statements contradicting evidence, false media balance, and public confusion. The September 2025 announcement represents failure to apply MMR lessons. Healthcare providers must employ evidence-based shared decision-making emphasizing sibling-controlled studies show no causal relationship while untreated conditions carry established harms. The Precautionary Communication Principle provides framework for transparent uncertainty discussion without disproportionate alarm or undermining evidence-based medicine trust. See also the graphical abstract(Fig. 1).
The adoption of Continuous Manufacturing (CM) for Oral Solid Dosages (OSD) is often challenged by the limited sensitivity of traditional Process Analytical Technology (PAT), such as Near-infrared (NIR) and Raman spectroscopy, to provide sufficient accuracy for process monitoring and control of low-dose or fixed-dose formulations. This manuscript explores solutions by highlighting advanced control strategies and alternative manufacturing technologies. These strategies include enhanced spectroscopic methods (e.g., Spatially resolved-NIRS, Light-induced fluorescence) to provide improved accuracy/precision, the use of process data and process models (Residence Time Distribution, Multivariate Statistical Process Control) as soft sensors, hybrid PAT and process models and more traditional at-line/off-line monitoring using NIR, Raman or high-sensitivity liquid chromatography with stratified sampling and bracketing. Alternatively, several technologies inherently ensure high content uniformity, such as semi-Continuous Manufacturing (sCM) with accurate mini-batch dispensing and Dry Coating Technology. For Twin-Screw Hot Melt Extrusion (HME) molecular-level mixing delivers more uniform blends, but current low-dose applications still require pre-blending of the drug substance with suitable excipients. When fed with a uniform powder blend, twin screw wet granulation also ensures compliant content uniformity without the need for PAT monitoring. In conclusion, a successful CM of low dose products may be possible when strategically combining advanced spectral and data approaches, modelling, and innovative platforms to build robust and validated process controls. This has been demonstrated across multiple peer reviewed studies and is now gradually being incorporated into control strategies for the commercial manufacture of pharmaceutical products.
Neuromyelitis optica spectrum disorders (NMOSD) are associated with a high burden of depression, pain, and physical disability, all of which significantly impair quality of life. At the same time, discussions on the cost-effectiveness of treatment strategies are gaining importance. However, it is not yet known whether specific symptom burdens are particularly cost-driving. This study aims to provide a comprehensive cost analysis considering depression and pain to optimise future healthcare strategies. This prospective cross-sectional multicentre study was conducted at twelve centres of the Neuromyelitis Optica Study Group (NEMOS). Over a three-year period, 115 NMOSD patients were recruited. Disease-related costs, pain, and depression were assessed using standardised questionnaires. A generalised linear model analysis and graphical sub-cost analysis were performed to identify key cost drivers. The robustness of our findings was confirmed using two independent depression rating scales. In our sample of 115 patients, 77% suffered from chronic pain with a median pain intensity of 4.0 on the numeric rating scale (NRS). Moreover, 56% of patients reported depressive symptoms. In multivariate regression analysis, depression emerged as a significant predictor of total costs (p < 0.001) alongside the EDSS score (p < 0.001) and age (p = 0.004). In contrast, pain was not significantly influencing total costs (p = 0.057), despite being reported by the majority of patients. Graphical analyses highlighted informal costs as the main cost driver in patients with increasing depressive symptoms. Depressive symptoms are not only common in NMOSD patients but also represent a major cost driver alongside neurological disability. Addressing these symptoms is essential for optimal patient care and may help reduce the socioeconomic burden.
Brucellosis has significant public health and human health consequences. It causes significant economic hardship, especially in areas where food safety measures, hygiene standards, and veterinary care are inadequate. This study aimed to analyze the demographic and clinical characteristics, laboratory results, complications, and treatment modalities of patients with brucellosis. This retrospective study included patients aged ≥18 years who were followed for brucellosis between December 2018 and December 2023 in the Infectious Diseases and Clinical Microbiology clinics of two hospitals located in endemic provinces of Türkiye. Demographic characteristics, risk factors, clinical manifestations, laboratory findings, focal complications, diagnostic methods, and treatment regimens were analyzed. Brucellosis was diagnosed based on compatible clinical findings together with culture positivity or serologic test results. The study included 748 patients diagnosed with brucellosis. Of these, 484 (64.7%) were female, and the mean age was 39.2 ± 15.0 years. A significant proportion of patients (91%) lived in rural areas. Regarding transmission routes, 79.4% of patients reported consuming unpasteurized fresh cheese. Patients were categorized as follows: 72.7% had acute brucellosis, 20.6% had subacute brucellosis, and 6.7% had chronic brucellosis. The most frequently reported symptoms were joint pain (89%) and malaise (65.3%). A comparative analysis showed that patients with acute brucellosis had significantly higher erythrocyte sedimentation rate (ESR), alanine aminotransferase (ALT) levels, and C-reactive protein (CRP) levels than those in other subgroups. The most prevalent laboratory findings were elevated CRP (50.3%), elevated ESR (37.6%), and anemia (33.3%). Hepatobiliary and osteoarticular complications developed in 22.6% and 6.5% of patients, respectively. Brucellosis in this endemic region predominantly affected women and individuals living in rural areas and was strongly associated with the consumption of unpasteurized dairy products. Our findings emphasize the need for improved public awareness and preventive measures, particularly regarding safe dairy consumption, in endemic settings. Graphic Abstract.
Knowledge of the behavior of high-strength concrete reinforced with bamboo fibers remains limited with respect to compressive deformability and flexural fracture energy. The effect of alkali-treated bamboo fibers (1.0% and 1.5% by weight of cement; 2% NaOH) on the fresh-mix properties, mechanical performance, compressive deformability, and flexural fracture energy of high-strength concrete was evaluated. The addition of fibers increased the air content and reduced the consistency of the mix. The compressive strength changed by + 4% and - 6%, while the strain corresponding to peak stress increased by 11% and 7%. The splitting tensile strength decreased by 14%, whereas the flexural tensile strength increased significantly by 12%. A more pronounced effect was observed for fracture energy, which increased significantly: Gf,δ by 20% and 143%, and Gf, CMOD by 20% and 33%. The increase in fracture energy may be associated with delayed microcrack initiation in the notch-tip zone, limited microcrack coalescence, and short-term load retention near the peak value, as confirmed by the flexural response curves and by the analysis of the evolution of the principal tensile strain ε₁ concentration zone. Scanning electron microscopy revealed only minor qualitative changes in fiber surface morphology after alkali treatment.
Canine anxiety is a complex welfare issue involving neuro-endocrine dysregulation. While acute stress biomarkers are widely used, the chronic metabolic consequences of anxiety remain under-investigated. Hair mineral analysis offers a retrospective window into long-term systemic homeostasis. This study aimed to identify chronic trace element signatures in the hair of anxious dogs compared to healthy controls and evaluate whether behavioral recovery induced by Hericium erinaceus supplementation is reflected in the hair mineral matrix. Hair samples from 21 healthy controls and 8 clinically anxious dogs were analyzed for a 16-element panel (Zn, Cu, Fe, Mg, Mn, Se, Ni, Si, Ca, P, Al, Cd, Cr, As, B, and Pb) using ICP-OES. The anxious cohort received H. erinaceus extract (1000 mg/10 kg) daily for 28 days. Anxious dogs exhibited a distinct dyshomeostatic profile characterized by significantly higher hair zinc and aluminum, alongside a profound depletion of nickel (p < 0.001) and significantly elevated phosphorus (P) levels (p = 0.003). Supplementation led to a substantial 62% reduction in anxiety scores (p = 0.002), and post-treatment scores were no longer statistically different from those of the control group. However, most hair mineral concentrations remained stable post-treatment, with the notable exception of a significant increase in calcium (Ca) levels (p = 0.012). Furthermore, Spearman's correlation analysis revealed that higher anxiety severity was strongly and negatively associated with hair iron (Fe) and chromium (Cr) levels (rs = -0.77, p = 0.041). These findings suggest that hair Zn, Al, and Ni status may represent potential candidate biomarkers, warranting validation in larger cohorts of the chronic anxious phenotype. The persistence of these signatures despite rapid clinical improvement supports a temporal dissociation between behavioral recovery and metabolic normalization as reflected in the hair matrix.
The expansion of digitalization in the pre-, intra- and post-operative surgical phases allow the development and integration of advanced technologies such as virtual surgical planning (VSP), additive manufacturing (AM), augmented reality (AR) and virtual reality (VR) in surgical workflows, all aiming to improve the surgical precision and efficiency. However, their implementation in clinical practice leads to novel organizational challenges, such as excessive costs, inefficiencies in the use of hard- and software and availability, and the coordination of the required highly specific expertise and skill of different employees with backgrounds in different domains. We aim to provide a practical step-by-step overview of how a centralized platform within academic hospitals can solve practical and organizational problems concerning surgical digitalization. A bottom-up approach was used to ensure engagement from all stakeholders within the hospital. Hereto, a small core group identified all potential stakeholders deemed essential for a successful implementation of a centralized platform. These stakeholders were then initially approached separately by the core group and then brought together in multiple focus groups to discuss action points, identify essential components and find solutions for emerging barriers. The implementation focused on 5 essential points: (1) medical imaging & segmentation process; (2) centralized Central Processing Unit (CPU) and Graphical Processing Unit (GPU) capacity with guaranteed continued collaboration with Department of Information and Communication Technologies (ICT); (3) medical device and in-house AM dealing with the production and sterilization of three dimensional (3D)-printed models; (4) Implementation of CPSD within the operating room; (5) External connection and collaboration with industry and other academic centres aimed to support interventional medical digitalization and the implementation of other innovative medical technologies. The protocol focuses on key aspects, including identifying existing innovations, naming prevailing challenges and formulating effective solutions. Centralizing digitalization in the hospital streamlines workflows, enabling faster processing and improved multidisciplinary collaboration. Success depends on coordinated input from medical, technical, and legal experts. The resulting platform fosters ongoing innovation while staying compliant and adaptable. Not applicable.
This study assessed the predictive value of TURPAID and PREDICT-crFMF scores at diagnosis for FMF50 response at the sixth month in children with Familial Mediterranean Fever (FMF) and examined their associations with disease-activity indices and acute-phase reactants. Children newly diagnosed with FMF according to the Eurofever/PRINTO criteria and who received colchicine treatment for at least 6 months were included. Clinical and laboratory data were retrospectively obtained from electronic medical records. Treatment response was evaluated at the 6-month follow-up visit. Overall, 168 children with FMF (50.6% female) were included. FMF50 response at 6 months was achieved in 64.8% of patients. PREDICT-crFMF and TURPAID scores were significantly higher in non-responders than in responders (p < 0.001 for both). Higher PREDICT-crFMF (aOR 1.240, 95% CI 1.113-1.382, p < 0.001) and TURPAID (aOR 2.009, 95% CI 1.384-2.916, p < 0.001) scores, and M694V homozygosity (aOR 3.390, 95% CI 1.700-6.760, p < 0.001) independently predicted FMF50 nonresponse. Both scores showed significant discrimination (AUC = 0.685 for PREDICT-crFMF; 0.670 for TURPAID; p < 0.001). Optimal cut-offs were ≥ 3 for PREDICT-crFMF (sensitivity 67.8%, specificity 69.7%) and > 1.5 for TURPAID (sensitivity 76.3%, specificity 54.1%). PREDICT-crFMF scores correlated with erythrocyte sedimentation rate (ESR), serum amyloid A, and disease activity indices, but not Mor score; TURPAID scores correlated with ESR and all evaluated disease activity indices. Early assessment of PREDICT-crFMF and TURPAID scores at diagnosis may help identify FMF patients at risk for colchicine resistance. Key Points • This is the first study to evaluate PREDICT-crFMF and TURPAID scores together using FMF50 as the treatment-response outcome in children with FMF. • Higher PREDICT-crFMF and higher TURPAID scores at diagnosis independently predicted failure to achieve FMF50 response at 6 months. • The significant associations of these scores with disease activity indices and inflammatory markers support their value in reflecting early inflammatory burden.
This study focuses on the fabrication and analysis of hybrid epoxy based composites using jute fiber (JF) and Linz-Donawitz (LD) sludge as reinforcement materials. The composites were fabricated through a hand-lay-up technique, with LD sludge concentrations varying at 0, 5, 10, 15, 20, and 25 weight percentages and a fixed concentration of JF (20 wt%). The density and microhardness of the composites were evaluated in accordance with ASTM standards. An experimental design approach and analysis of variance (ANOVA) were employed to examine the effects of control parameters on the sliding wear rate of the composites. Further, this study evaluated the performance of machine learning models for predicting composite properties that are enhanced by the addition of reinforcements. The findings showed that the S4 specimen (20 wt% of LD sludge and 20 wt% JF) exhibited enhanced microhardness and improved wear resistance relative to the other specimens. The density of the S4 specimen improved by approximately 18.77% compared to neat epoxy. The specific wear rate decreased, as it dropped to 0.526 mm3/N-m at 20 wt% LD sludge, which is a 60% improvement in wear resistance. The machine learning models were highly predictive, and Gradient Boosting and XGBoost yielded R2 values of 0.9999 and low error rates. The study concluded that the practical outcomes were in close alignment with the optimal predicted results.
Deep Mutational Scanning (DMS) experiments generate large volumes of sequencing data that must be processed through multi-step computational pipelines to yield interpretable variant scores. At least twelve dedicated tools have been published for this purpose, yet the diversity of experimental designs, scoring strategies, and software implementations has produced a fragmented landscape in which no single tool accommodates the full range of workflows encountered in practice. Here we present CountESS (Count-based Experiment Scoring and Statistics), an open-source pipeline tool that provides a modular, graphical interface for constructing flexible DMS analysis workflows. CountESS supports a wide range of input formats, barcode translation, HGVS variant calling, and user-defined scoring functions, enabling it to accommodate diverse experimental designs including selection assays, time-series experiments, and bin-based assays such as VAMP-seq. Implemented in Python with DuckDB as a computational backend, the software provides high-performance, memory-efficient processing suitable for large datasets. CountESS is freely available at https://github.com/CountESS-Project/CountESS under the 3-Clause BSD Licence. Supplementary data, including demonstration pipelines and example datasets, are available at https://github.com/CountESS-Project/countess-demo .
Oxidative stress is identified as a potential factor in vitiligo pathogenesis. We aimed here to evaluate whether USP11 regulates the oxidative stress of melanocytes in vitiligo. Human melanocyte PIG1 cells were induced with 1 mM H2O2 and pre-infected with lentiviruses for genetic intervention. The dorsal skin of C57BL/6J mice was applied with 5% H2O2, and genetic intervention was elicited through adenoviruses. USP11, SIRT3, and TRIM28 were reduced in melanocytes (Melan-A positive) from vitiligo mouse skin tissues and in the H2O2-induced PIG1 cells. TRIM28 transcriptionally activated USP11 to promote deubiquitination of SIRT3. H2O2 decreased viability and melanin and tyrosinase contents and increased apoptosis and oxidative stress in PIG1 cells. H2O2 induced severe depigmentation of the dorsal skin in mice, reduced melanin deposition in hair follicles, loss of melanocytes, and increased oxidative stress. Overexpression of either USP11 or TRIM28 inhibited H2O2-induced melanocyte damage and vitiligo, while combined knockdown of SIRT3 or USP11 reversed the effects of USP11 or TRIM28 overexpression. These findings suggest that TRIM28 exerts its effect by reducing oxidative stress in melanocytes through USP11-mediated SIRT3 deubiquitination. This observation provides a mechanistic insight that could inform future therapeutic exploration in vitiligo. Graphical abstract text. The diagram. TRIM28 inhibits oxidative stress damage in melanocytes and alleviates vitiligo by transcriptionally upregulating USP11 and promoting deubiquitination modification of SIRT3.
Loss-of-function variants in the human phenylalanine hydroxylase (PAH) gene are the most common genetic causal factors for Phenylketonuria (PKU). Currently, a broad spectrum of variations is recognized in the human PAH gene. However, the molecular function and clinical significance of some novel PAH variants remain unclear. Here, we report on five PKU-affected families carrying three novel PAH variants, including one missense variant (PAH: c.271C>A (p.Leu91Met)) and two deletions (PAH: c.206_208delCTT (p.Ser70del) and PAH: c.541_544delGAGG (p.Glu181Lysfs*13)). These variations constitute different compound heterozygous genotypes with other known pathogenic variants such as PAH: c.721C>T (p.Arg241Cys), PAH: c.168+5G>C, and PAH: c.1238G>C (p.Arg413Pro), which probably led to the patients' PKU etiopathology. qRT-PCR and immunoblotting showed that the protein levels of PAH (S70del) and PAH (E181Kfs*13) were significantly reduced compared with the wild-type control, although their transcript levels were not. Also, the enzyme activity of PAH (S70del) and PAH (E181Kfs*13) mutants was significantly decreased relative to the wild type (P < 0.001). PAH: c.271C>A (p.Leu91Met) had no significant effect on PAH mRNA and protein levels or enzyme activity. Collectively, our data demonstrate that the two deletions PAH: c.206_208delCTT and PAH: c.541_544delGAGG are clinically significant for pathogenicity. Our findings are anticipated to contribute to the advancement of prenatal diagnosis, population-based carrier screening, and genetic counseling for individuals affected by PKU, and is expected to help reduce the incidence of PKU and ameliorate the associated disease burden. See also the graphical abstract(Fig. 1).
Understanding how early mother-child interactions are linked to children's social-cognitive processes requires methods capable of capturing the temporal structure of naturalistic behavior. This study introduces a computational framework based on Bayesian Network modeling to identify sequential dependencies among nonverbal behaviors (smiles, gaze, and social touch) exchanged during free play in mother-child dyads (n = 38; age 3 years). From each network, we derived the Order of Sequential Interaction (OSI), a compact index of interaction complexity. We then examined its associations with behavioral, physiological, and neural measures relevant to cognitive development. Although OSI was not associated with language or executive-function scores, analyses revealed links between OSI and prosocial behavior, facial EMG, and neural responses (rTPJ, lIFG) during prosocial-scene viewing. These findings suggest that OSI may capture aspects of interaction structure specifically connected to children's social and affective responsiveness. Building on this, the present framework demonstrates how probabilistic graphical models can structure complex interaction data and support future investigations into multimodal processes in early social cognition. SUMMARY: A Bayesian-network framework is proposed to model multivariate sequential dependencies in naturalistic mother-child interaction. The order of sequential interaction (OSI) quantifies interaction complexity from behavioral time-series data. Higher OSI is associated with greater prosocial behavior and with neural (rTPJ, lIFG) and physiological (facial EMG) responses during social processing. Interaction complexity is not associated with general cognitive or language measures, suggesting that it reflects a distinct dimension of social behavior. The proposed framework provides a basis for studying social-cognitive development from naturalistic interaction data.