This article presents a reproducible field-to-simulation workflow that translates real football plays into solver-ready boundary conditions for a full-body finite-element human model. Single-view video reconstructs six-degree-of-freedom kinematics at the skull CG; these signals are applied to a helmet-head-body assembly that preserves event-specific hardware. The protocol codifies quality-control gates before interpretation: (I) helmet readiness (mesh integrity, contact stability), (ii) mass and center-of-gravity agreement between the physical configuration and its FE surrogate, (iii) energy balance with bounded spurious energies, (iv) driver-fidelity metrics comparing target versus solver-applied motion (RMSE, peak magnitudes, time-to-peak), and (v) versioned inputs for auditability. A demonstration replay verifies numerical stability and driver fidelity, and reports tissue-level response and diagnostic damage metrics as process outputs without making injury claims. For studies requiring internal response, the framework supports physics-based constitutive models (Internal State Variable formulations) and treats them as process diagnostics unless separately validated. By separating readiness checks from injury interpretation, the method provides a practical foundation to standardize helmet integration, enable cross-laboratory reproducibility for event scenarios, and inform safer design and policy. Although developed for sport, the workflow generalizes to transportation and defense settings where ethical constraints prevent human experimentation.
Evolutionary multitask optimization (EMTO) aims to optimize multiple tasks simultaneously by transferring the related knowledge between tasks. Therefore, knowledge transfer (KT) between different tasks is crucial for facilitating the optimization of tasks. The traditional KT methods in EMTO achieve superficial KT through individual transfers, limiting their ability to transfer high-quality knowledge from other tasks. Therefore, in this article, a diffusion model-based KT (DMKT) method built specifically on cold diffusion (CD) is proposed to deeply mine the mapping relationships between different tasks and obtain transfer models for mapping individuals across tasks. Since CD supports arbitrary degradation operators, the interpolation between fitness-paired source and target task individuals can be defined as a task-oriented gradual degradation process, enabling the corresponding restoration process to learn directed cross-task mappings. In particular, we first construct two cyclic training diffusion models (DMs) for each source-target task pair. Subsequently, the trained DM can be used to generate new promising solutions for achieving efficient KT. The experimental results on the CEC2022 multitask optimization problem (MTOP) benchmark demonstrate that the proposed diffusion-based KT multitask optimization (DKTMTO) algorithm outperforms other state-of-the-art EMTO algorithms. Moreover, DMKT can be integrated into other EMTO algorithms to further improve their performance. Finally, DKTMTO is applied to real-world multitask planar kinematic arm control problems (PKACPs) and the WCCI2020 many-task optimization problem (MaTOP) benchmark, demonstrating its applicability and scalability.
Metal‑nitrogen-carbon (M-N-C) materials originating from prussian blue analogs (PBAs) are regarded as attractive catalysts toward oxygen evolution reaction (OER) and hydrogen evolution reaction (HER). Nevertheless, they suffer from drawbacks of poor conductivity and catalyst deactivation owing to metal aggregation. In this work, through a dual optimization strategy depending on structure regulation and interface engineering, we prepared a bead-like iron diselenide/nickel diselenide‑nitrogen-doped carbon@carbon nanofibers (FeSe2/NiSe2-NC@CNF) electrocatalyst with FeSe2/NiSe2-NC evenly anchored in the carbon nanofibers (CNF) one by one. FeSe2/NiSe2-NC@CNF demonstrates outstanding dual-functional electrocatalytic activity toward OER (Ej=10 = 254 mV, Ej=300 = 403 mV) and HER (Ej=450 = 601 mV) at high current densities. In addition, when applied to overall water splitting, the potential of FeSe2/NiSe2-NC@CNF at 10 mA·cm-2 is only 1.63 V, indicating its outstanding water electrolysis capability. X-ray absorption spectroscopy (XAS) and density functional theory (DFT) calculations indicate that the additional unsaturated coordinated NiSe bonds in FeSe2/NiSe2-NC@CNF are beneficial to optimizing the adsorption/desorption behaviors of intermediates and accelerating the rate-determining step (*O transforming into *OOH). The present study proposes a rational design strategy to optimize the performance of M-N-C catalysts, and lays the foundation for the advancement of superior bifunctional non-noble metal catalysts toward overall water electrolysis.
The role of the tissue microenvironment in the transition from acute kidney injury to chronic kidney disease remains poorly understood. While persistence of failed-repair proximal tubule cells is postulated to hamper kidney regeneration, the spatial metabolic architecture of injured tissue and its effect on regenerative capacity have not been fully characterized. We analyzed mouse kidneys 14 days post bilateral ischemia-reperfusion injury using a multimodal spatial omics approach. Internal standard normalized mass spectrometry imaging (MSI) quantified metabolite abundances, followed by unsupervised spatial domain analysis using the BANKSY algorithm to identify tissue niches based on lipidome profiles. Consecutive sections underwent high-resolution spatial transcriptomics (Stereo-seq), and we applied niche projection to integrate metabolomic and transcriptomic data, enabling comparison of proximal tubule cells in healthy versus injured niches. Unsupervised spatial domain analysis revealed distinct healthy and injured niches, with injured niches exhibiting diffusely spread metabolic abnormalities extending beyond failed-repair proximal tubule cells. Quantitative metabolomics demonstrated that seemingly healthy proximal tubule cells residing in injured niches exhibited elevated succinic acid and depleted linoleic acid compared with cells in healthy niches. Spatial transcriptomics confirmed these metabolic defects at the transcriptional level, revealing downregulation of oxidative phosphorylation and fatty acid β-oxidation pathways in proximal tubule cells within injured microenvironments. Combined spatially resolved analysis of internal standard-normalized MSI and spatial transcriptomics revealed distinct healthy and injured tissue niches following ischemia-reperfusion injury. Metabolic abnormalities, including defects in oxidative phosphorylation and fatty acid β-oxidation, were not restricted to failed-repair proximal tubule cells but extended into seemingly healthy epithelial cells embedded within injured microenvironments.
Patellofemoral complications after total knee arthroplasty (TKA) are linked to femoral component design and trochlear alignment. This study compared the effects of a kinematically aligned (KA)-optimized femoral component versus a standard design on patellofemoral kinematics, contact mechanics and quadriceps function, using native knee biomechanics as reference. Seventeen fresh-frozen cadaveric knees (10 valgus, 7 varus) were tested in a dynamic knee rig (30°-130° flexion) under controlled loading, divided into a valgus and a varus group. Patellofemoral kinematics, contact area and peak pressure patterns were recorded in the native state and after KA TKA using either a standard component (prosthetic trochlear angle [PTA] 6°) or a KA-optimized design (PTA 20°). One-dimensional statistical parametric mapping (SPM1D) independent two-sample t tests were applied (α = 0.05). The KA-optimized component reduced medialization between 30° and 70° in both groups (varus: 2.5 mm; valgus 2.0 mm). Patellar tilt remained unchanged. Contact area decreased after TKA without consistent differences between components (valgus: p < 0.001 until 80°; varus: p < 0.001 at 60°-80°). Peak pressure was not reduced with the KA-optimized design; slightly higher values occurred up to mid-flexion (+1.1 MPa at 90°), normalizing at higher flexion angles in valgus knees. In varus knees, both designs increased peak pressure at high flexion (+1.5 MPa at 120°). Quadriceps force showed no significant differences. The KA-optimized femoral component reduces patellar medialization but does not improve patellofemoral loads or quadriceps efficiency, suggesting that isolated geometric optimization may be insufficient to restore physiological biomechanics after KA TKA. N/A.
Reliable endotracheal intubation is essential for experimental procedures in rats that require controlled ventilation, particularly during thoracic and cardiovascular surgeries. However, airway management in rats remains technically challenging due to anatomical constraints and limited accessibility of available intubation techniques. The goal of this protocol is to describe a simple, low-cost method for orotracheal intubation in rats using video-assisted direct laryngoscopy successfully applied in a cohort of 50 rats. This protocol uses a commercially available video otoscope with an integrated light source and wireless smartphone connectivity to provide real-time visualization of the upper airway. The method does not require device modification or specialized imaging systems. Adult rats are anesthetized with xylazine and isoflurane, positioned on an inclined intubation platform, and intubated under continuous visual guidance using a flexible guide and an endotracheal tube. Correct tube placement is defined by bilateral thoracic expansion, stable oxygen saturation (SpO₂), and a consistent end-tidal CO₂ (capnography) waveform. Following intubation, mechanical ventilation is initiated without positive end-expiratory pressure, and the tidal volume was set at 7.5 mL/kg. In this cohort, the technique enabled rapid intubation with a high first-attempt success rate and was associated with a low incidence of peri-procedural complications. Continuous visualization may reduce the risk of esophageal intubation and airway trauma. These findings support the feasibility of this approach under the conditions tested, without extrapolation beyond this experimental setting.
To evaluate whether principal component analysis (PCA) of rectal and bladder dose-volume histograms (DVHs) identifies dose regions associated with late toxicity after prostate stereotactic body radiotherapy (SBRT). This retrospective single-institution study included 106 patients with linked clinical and dosimetric data after prostate SBRT to 36.25 Gy in five fractions. PCA was applied separately to whole-organ and wall-based rectal and bladder DVHs. Associations with late grade ≥ 2 gastrointestinal (GI) and genitourinary (GU) toxicity at 12 months were explored using Spearman correlation, ROC analysis and deliberately limited logistic regression. Toxicity was extracted retrospectively from institutional follow-up records scored according to CTCAE v5.0. Among 84 patients with 12-month follow-up in the DVH-linked dosimetric cohort, grade ≥ 2 toxicity was uncommon, with 3 GI events (3.6%) and 2 GU events (2.4%). Intermediate-dose rectal metrics showed the strongest exploratory signal for late GI toxicity, particularly rectal V18.1 Gy (AUC 0.868, 95% CI 0.740-0.997) and V29 Gy (AUC 0.827). These estimates are based on very few events and should not be interpreted as validated predictive performance or as evidence of a new planning threshold. No bladder or bladder-wall metric showed clinically meaningful discrimination for GU toxicity. Intermediate whole-rectum dose was associated with late GI toxicity in this low-event SBRT cohort, but the findings are exploratory and require validation. No isolated dosimetric predictor of late GU toxicity was identified.
As elite women's football evolves tactically, understanding goalkeeper distribution behaviour is increasingly important for performance analysis and coaching. Traditional approaches rely on isolated passing metrics, providing limited insight into how goalkeeper actions are organised within possession structures. This study applied a graph-based clustering framework to classify goalkeeper passing behaviours in the English Women's Super League across three seasons. The dataset comprised 29,911 goalkeeper passes from 27,895 possessions. Pass embeddings were represented within a relational graph and clustered using K-means (k = 14), identifying recurrent distribution patterns shaped by spatial context, defensive pressure, and possession phase. Results may indicate that behaviours were organised along continuous gradients rather than discrete categories. Central clusters reflected adaptable distributions, whereas peripheral clusters captured more specialised actions, including long passes under defensive pressure. Lateral tendencies suggested interactions between dominant-side preferences and spatial availability. Phase analysis showed goalkeeper involvement was most prominent during transitional moments, particularly at possession initiation and termination, with comparatively limited involvement in sustained attacking sequences. Collectively, these findings provide a structured account of goalkeeper distribution in elite women's football and demonstrate how graph-based modelling can support behavioural profiling, tactical interpretation, and evidence-based coaching practice.
The 2021 expansion of the Child Tax Credit provided advance monthly cash transfers to most US households with children. Although prior evaluations documented overall improvements in household well-being, less is known about variation by household structure. This study examined policy-period changes in parental mental health and material hardship among female-headed households. Using data from 1.3 million respondents to the Census Bureau's Household Pulse Survey (2020-25) and an intersectional framework, we applied difference-in-differences and multilevel models to estimate changes in parental mental health and material hardship before, during, and after expanded Child Tax Credit implementation. Non-female-headed households showed improvements in depression, anxiety, and housing insecurity, whereas female-headed households, particularly those with low incomes and Black, Hispanic, and Asian households, showed smaller or no comparable gains. Food insecurity did not improve across groups. Intersectional analyses showed that the highest burdens and smallest improvements were concentrated among low-income, female-headed households across racial and ethnic groups. Findings suggest that the expanded Child Tax Credit did not fully close baseline disparities, underscoring the importance of household structure and intersecting social positions in policy design and evaluation.
To investigate the feasibility of 2D convolutional neural networks (CNNs) in the automatic classification of anterior talofibular ligaments (ATFLs) on MR images. A total of 560 transverse T2-weighted MR images of the ATFL were collected from Center A, and 96 from Center B; manual segmentation of the ATFL was performed. The ATFL segmentation model was trained on YOLO11 and was validated on images from Center B. The dice similarity coefficient (DSC) between manual and automatic segmentation was calculated. A total of 1,103 T2-weighted MR images of the ATFL were further collected from Center C and divided into three groups: normal, partial, and total tear, and ATFL was automatically segmented for all the images. The 2D ResNet model was then trained for ATFL classification. Finally, the segmentation model and classification model were applied to 420 images from Center D. The median DSC for the YOLO11 segmentation model was 0.95. For Center D data, the automatic workflow achieved an accuracy of 92.6% (389/420). It showed 95.0% (190/200) sensitivity and 93.6% (206/220) specificity for abnormal ATFL detection, slightly below the junior radiologist's 97.0% (194/200) sensitivity and 95.5% (210/220) specificity, but the difference did not reach statistical significance (P = 0.22). Automatic classification of the Center D dataset took 3 minutes, compared with manual 14 minutes for the junior radiologist. Automatic segmentation and classification of ATFL on MR images based on CNNs are feasible for evaluating ATFL.
The development of new analytical tools remains a powerful approach in analytical chemistry, and three-dimensional (3D) printing has gained increasing popularity as a manufacturing technique in recent years. The fabrication of experimental tools using 3D printers has attracted attention in many fields. However, objects fabricated by fused filament fabrication (FFF) may contain gaps between printed paths, and which can cause leakage when liquid is introduced. In this study, solvent-based post-processing was performed in order to fill layer gaps in FFF-printed polypropylene fluidic chips. As methods, a compression process and solvent-based post-processing were applied. In the compression process, a polypropylene fluidic chip was compressed using a compression tool while being heated. After this compression process, solvent-based post-processing was conducted. The post-processed chip was used to measure peak profile during flow-injection measurement. As a result, peak profiles were obtained, and no leakage from the chip was observed during the measurements.
Maintaining or improving the health-related quality of life (QoL) of lung cancer patients is essential throughout treatment. To support shared decision-making and improve patient-physician communication, patients' preferences should be understood and considered in clinical decision-making; however, existing research indicates notable discrepancies between patient and physician priorities. This study therefore aims to elicit and compare their preferences regarding QoL dimensions in lung cancer care in Germany. Based on systematic literature reviews as well as qualitative analysis, two discrete choice experiments (DCEs) were applied to elicit the preferences of patients and treating physicians. In the DCE scenarios, both groups chose between alternative QoL profiles reflecting different health states during lung cancer treatment. Data were analyzed using multinomial logit models. The final DCEs comprised five common attributes - activities of daily living, shortness of breath, anxiety, social life, and emotional impairment - and one group-specific attribute: financial difficulties in the patient DCE and severity of pain in the physician DCE. Overall, 162 patients (mean age: 60.39 years, SD 10.05) and 154 referring physicians (mean age: 51.26 years, SD 11.28) participated. All model coefficients were statistically significant (p < 0.001). Patients prioritized "Shortness of breath" (28.0%; level range of 1.417) and "Activities of daily living" (23.8%; level range of 1.207), while "Emotional impairment" (6.4%; level range of 0.323) ranked lowest. In contrast, treating physicians emphasized ''Shortness of breath" (26.0%; level range of 1.114) and "Severity of pain" (22.5%; level range of 0.962); again, "Emotional impairment" ranked lowest (4.8%; level range of 0.207). Overall, patients and treating physicians showed broad agreement regarding the importance of several central QoL dimensions, particularly shortness of breath and activities of daily living. At the same time, differences in the weighting of selected attributes suggest that individual QoL priorities should be addressed explicitly in patient-physician communication.
Electrophoretic deposition is a method of choice for generating coatings thanks to its ease of implementation and its ability to produce coatings of relatively large thicknesses in a single-step process. While this process also benefits from a large number of tunable parameters to adapt the coating to each application (such as applied electric field, particle concentration, and viscosity of the suspension), such freedom can make selecting parameters an overwhelming task. A better fundamental understanding of the microscopic phenomena and mechanisms at play during deposition can provide clues for a more efficient design of optimized coatings. Particle-based models, which allow for the systematic simulation of deposit microstructures across various process parameters, are particularly interesting for gaining insights into such systems. Nevertheless, such studies are rare and usually do not include the possibility of self-cohesion between particles, which is crucial for the final structure of the deposit. Here, we use particle-based simulations to study how barrier-limited aggregation influences the deposits formed under different applied electric fields. We show that self-cohesion indeed leads to different microstructures, both in the close vicinity of the substrate and in the bulk of the deposit, and we relate this to the mechanical signature of the deposits. Our results reveal that at high electric fields, the influence of self-cohesion on the resulting microstructures essentially vanishes beyond a critical field strength. This marks the transition from a deposition regime affected by aggregation to a regime largely dominated by volume-exclusion effects.
Advances in Earth observation (EO) remote sensing technologies have delivered a range of aerosol and trace gas pollution data with ever-improving spatial and temporal resolution, significantly benefitting assessments of global air quality (AQ). Furthermore, the application of data synthesis techniques incorporating satellite EO with other information sources has improved the availability of satellite-derived estimates of pollutant exposure at local to global scales. These data have been applied to address a diversity of use cases in AQ monitoring and public health, from long-term trend tracking, exposure assessment, and epidemiological analysis to short-term emissions identification and early warning. Successful application of satellite EO to address AQ and AQ-related health problems requires an alignment between (1) the technical capabilities of satellite data to provide relevant information, (2) a defined case for using this information to address a particular need, and (3) the human capacity, computational resources, operational plans, and policy and governance frameworks to implement a solution and take action, and to sustain the solution for as long as the need remains. Only when there is substantial alignment across all these factors can satellite EO information be effectively translated into public health benefits. This paper surveys applications of satellite EO to AQ assessment and AQ-related health management globally, synthesizing key commonalities into recommendations for how satellite EO can effectively support health needs. We also identify gaps in current satellite EO capabilities, use-case applications, and feasibility factors where future research and investment could reduce barriers to increased application of satellite EO to address pressing public health concerns related to AQ worldwide.Implications: This paper summarizes insights collected through the Group on Earth Observations (GEO) Health Community of Practice Air Quality and Respiratory Health Work Group on the current state and gaps in the use of satellite EO to support air quality and related health decision-making globally. We synthesize these insights into general recommendations for how satellite EO capabilities, use cases, and feasibility considerations can be aligned towards effective use of satellite EO data for air quality and related health effects. We also identify barriers and gaps in current capabilities, uses, and capacities, making recommendations for how these might be addressed.
Statistical misreasoning is a key mechanism through which anti-vaccine narratives distort scientific information and undermine public confidence in immunisation. Although prior research has examined thematic and ideological features of vaccine misinformation, little is known about the specific errors in numerical reasoning that shape users' interpretations of vaccine-related data. A total of 597 Polish-language Facebook posts expressing anti-vaccine views and containing references to statistical information were analysed. Based on previous research on statistical cognition and an inductive review of the material, a coding scheme comprising ten categories of statistical misreasoning was developed and applied to all posts. Quantitative analyses were then conducted to examine how frequently these categories occurred and which combinations of errors appeared together. The most prevalent forms of misreasoning were the correlation-causation fallacy (70%, p < 0.001) and base rate neglect (58%, p < 0.001). Denominator neglect and cherry picking appeared in half of the posts, while the remaining categories were less frequent. Most posts contained multiple errors (median = 4), and the most common configuration involved the correlation-causation fallacy, base rate neglect and denominator neglect. The distribution of error counts further showed that posts most often exhibited four distinct categories of misreasoning (23%), followed by three (19%) and five (17%), and overall a majority of posts (62%, p < 0.001) contained between one and four different types of errors. Co-occurrence analysis revealed stable structural patterns, with the strongest association observed between denominator neglect and intuitive reasoning error (ϕ = 0.23; p < 0.001). Anti-vaccine discourse exhibits systematic patterns of statistical misreasoning that shape erroneous interpretations of vaccine-related data, highlighting the need to address cognitive and statistical misunderstandings through targeted public health communication.
Linking clinically derived risk signals to reproducible molecular states across independent cohorts remains a major challenge in translational bioinformatics. Existing approaches often rely on cohort-specific model fitting, limiting cross-dataset comparability and downstream biological interpretation. We developed a cross-cohort projection framework that maps baseline clinical variables to a clinically anchored latent risk coordinate, $\mu$, enabling application across external datasets without refitting. The fixed projector was trained in a local imaging cohort and applied unchanged to independent cohorts. Projected $\mu$ was evaluated across multiple molecular layers, including bulk transcriptomics, single-cell-guided deconvolution, spatial transcriptomics, and circulating cell-free DNA (cfDNA). In an independent external cohort, projected $\mu$ preserved separation of time to castration resistance across predefined strata (P = .002), with 30-month risk increasing from 0.13 to 0.86 across ordered $\mu$ bins. In bulk transcriptomics, higher projected $\mu$ was associated with increased proliferation-related signaling and reduced androgen receptor/lineage programs ($\rho$ = 0.40 and -0.26; both P < .001). Deconvolution analyses linked higher projected $\mu$ to reduced AR-high epithelial cell fractions ($\rho$ = -0.17, P = .001). Spatial transcriptomics demonstrated organized tissue-level structure of prespecified molecular programs. In cfDNA, higher projected $\mu$ was associated with a more negative RB1 copy-number signal in the detectable subset ($\rho$ = -0.49, P = .0278). This study presents a projection-based framework for cross-cohort translation of clinically anchored latent risk into interpretable multi-omics context. By enabling reuse of a fixed coordinate without refitting, the approach provides a practical strategy for linking clinical risk to molecular programs and blood-based readouts across datasets.
Mood can be understood as an affective state resulting from the integration, over time, of positive and negative outcomes. To capture intra- and inter-individual variability in mood fluctuations, computational models have been increasingly applied to self-reported mood ratings obtained during behavioural tasks. Such computational models of mood may be useful tools for understanding mood disorders. However, to be used in a clinical setting, their validity and reliability should be assessed. Recent versions of these models incorporate reciprocal interactions between mood and event perception, governed by specific parameters. Hence, it is critical to determine the extent to which estimated parameters depend on potentially arbitrary aspects of experimental design (e.g., feedback sequences) and to assess their psychometric test-retest stability. We used two widely established mood-induction tasks-a lottery task and a general-knowledge quiz-alongside a newly developed task, designed to allow precise experimental control over outcome sequences, while preserving participants' perception that outcomes depended on their actions. Extensive numerical simulations were conducted to test the robustness of the computational models. To evaluate test-retest reliability, 163 healthy volunteers completed the tasks twice, separated by a two-week interval. Simulations demonstrated robust parameter recovery overall, though estimating the effect of mood on feedback perception proved more challenging. All tasks successfully induced mood fluctuations, accurately described by models employing leaky integration of feedback. Test-retest reliability was satisfactory for two important parameters, baseline mood and accumulated feedback weight, with significant correlations observed across most parameters. Furthermore, our newly developed task confirmed that mood-related parameter estimates remained largely unaffected by specific feedback sequences. Computational models of mood dynamics show robust validity and satisfactory test-retest reliability. Stable parameters, such as baseline mood and feedback weighting, endorse the application of these models in longitudinal studies, offering a reliable methodological basis for clinical research on mood disorders.
This study compared the efficacy of three combined treatment regimens (biofeedback electrical stimulation [BES] combined with pelvic floor muscle training [PFMT], acupuncture combined with PFMT, and a triple therapy regimen of acupuncture, BES, and PFMT) in addressing urinary incontinence (UI) and sexual dysfunction in patients with postpartum pelvic floor dysfunction (PFD), with the aim of exploring the clinical advantages of the triple therapy regimen. This study was designed as a randomized controlled trial, with blinding applied to both the evaluators and the data analysts. A total of 203 postpartum women with PFD were enrolled and randomly assigned to Group A (BES+ PFMT, n = 65), Group B (acupuncture + PFMT, n = 68), and Group C (acupuncture + BES + PFMT, n = 64). Pelvic floor EMG, muscle strength, MUCP, and MFR were measured at baseline and 6 months post-treatment. Leakage volume was assessed by 1-hour pad test. UI was evaluated using ICI-Q-SF and IIQ-7, and sexual function using FSFI and PISQ-31. Adverse events were recorded throughout treatment. All groups showed significant improvement in EMG, muscle strength, MUCP, MFR, FSFI, and PISQ-31 scores, alongside reductions in leakage volume, ICI-Q-SF, and IIQ-7 scores. Improvements were most significant in Group C. No significant difference was found between Groups A and B after treatment. Adverse event rates did not differ significantly among groups. Compared to dual-therapy approaches, triple-therapy (acupuncture + BES + PFMT) enhances postpartum pelvic floor rehabilitation, effectively improving UI and sexual function in patients with PFD.
Early identification of Parkinson's disease is critical for timely intervention. Disruptions in sleep architecture and changes in physical activity patterns have been reported years before motor symptom onset, yet prodromal behavioral changes spanning sleep and daytime activity patterns are subtle and difficult to detect with conventional clinical tools. Wearable sensors provide a scalable means of monitoring these behaviors in natural settings, but extracting meaningful, interpretable features from high-frequency, unlabeled time series remains a major challenge. We present an end-to-end framework for assessing Parkinson's disease risk from wrist-worn accelerometry via automated feature extraction and survival modeling of time to diagnosis. Behavioral states are derived from unlabeled data using pretrained models including random forests with hidden Markov models for physical activity classification and a self-supervised learning based sleep staging model. We jointly model both sleep stage and physical activity sequences using hierarchical nonstationary Markov chains stratified by time of day and temporal resolution, characterizing individual-level behavioral rhythms and transition dynamics. Gradient-boosted Cox proportional hazards models are then used to estimate Parkinson's disease risk from these transition-based features. Applied to accelerometer data from the UK Biobank, our approach outperforms baselines that exclude dynamic modeling or rely on traditional functional data analysis, while providing interpretable predictors from long sequences of wearable sensor data. This demonstrates the potential of integrating pretrained AI models, temporal sequence modeling, and survival analysis to detect early behavioral signatures of neurodegeneration.
RAD51 is a central protein in the homologous recombination (HR) pathway and is essential for the accurate repair of DNA double-strand breaks (DSBs). Following DSB formation, DNA end resection generates single-stranded DNA substrates that facilitate the recruitment and assembly of RAD51 nucleoprotein filaments at sites of damage. This process results in the formation of discrete nuclear RAD51 foci, which serve as a widely accepted functional readout of HR activity and a surrogate marker of HR proficiency. Because defects in HR are common in several malignancies, particularly ovarian and breast cancers, assessment of RAD51 foci formation has emerged as an important approach for evaluating DNA repair capacity and predicting response to DNA-damaging therapies, including platinum compounds and poly(ADP-ribose) polymerase (PARP) inhibitors, whose efficacy is strongly influenced by HR repair status. This manuscript describes a simple, reliable, and reproducible immunofluorescence-based protocol for the detection and quantification of RAD51 nuclear foci in cultured ovarian cancer cells. The method involves induction of DNA damage by ionizing radiation (IR), followed by fixation, immunostaining with antibodies against RAD51 and γH2AX, confocal microscopy, and manual quantitative analysis of RAD51/γH2AX co-localized foci. The protocol can be applied under basal conditions or after genetic and pharmacological perturbations to determine their effects on HR function. Representative results demonstrate robust induction of RAD51 foci in HR-proficient ovarian cancer cells following DNA damage, whereas RAD51 depletion markedly reduces foci formation despite comparable levels of DSBs, confirming assay specificity. Overall, this protocol provides a robust and reproducible functional assay for assessing HR competency, with broad applications in preclinical and potentially translational cancer research.