Sleep bruxism (SB), which is frequently observed in patients with obstructive sleep apnea (OSA), has received increasing attention in recent years. Oral appliance (OA) therapy may indirectly influence SB by reducing respiratory events and respiratory-related arousals. This exploratory within-subject study aimed to investigate how OA therapy influences SB activity and arousal characteristics by examining changes in SB, respiratory events, and arousal indices before and after OA therapy in patients with OSA. Patients diagnosed with OSA based on polysomnography (PSG) were referred for OA therapy using a custom-made mandibular advancement OA (monoblock or twin-block design). Twenty-three participants (10 males, 13 females; mean age 58.5 ± 15.8 years) were enrolled. Masseter muscle activity was simultaneously recorded using an identical single-channel wearable electromyography device during PSG. Participants underwent a second PSG and electromyography (EMG) evaluation after OA therapy. Treatment effectiveness was defined as a ≥ 50% reduction in the apnea-hypopnea index (AHI) or a post-treatment AHI < 5. At baseline, the mean AHI was 17.4 [12.4-18.4] events/h, with a mean SB frequency of 13.25 [11.26-23.81] episodes/h. Baseline and post-treatment PSG and EMG evaluations demonstrated a significant reduction in the AHI (p = 0.002) and total SB episodes (p < 0.001), along with a significant increase in spontaneous arousal index (SAI) during the rapid eye movement (REM) stage (p = 0.02). Reductions in total SB episodes were observed in both treatment-response groups; however, SB changes were not consistently aligned with improvements in respiratory indices. The effective group exhibited a significant increase in arousal index (ArI) during REM sleep (p = 0.018) and a significant decrease in respiratory arousal index (RAI) during REM sleep (p = 0.045). These findings suggest that changes in SB under OA therapy are not solely dependent on respiratory improvement and may reflect multifactorial mechanisms. OA therapy may therefore influence SB through both indirect respiratory effects and direct mechanical or neurophysiological pathways.
Coal and gas outburst represents a highly destructive dynamic phenomenon inherent in deep coal mining operations. Currently, outburst prediction frameworks rely heavily on a uniform critical threshold system recommended by national regulations. However, within heterogeneous coal seams characterized by complex geological conditions, this universal approach frequently leads to "low-index outburst" incidents or excessive engineering redundancy, significantly undermining the intrinsic safety of mine operations. To address this core scientific bottleneck, the present study establishes a theoretical methodology for the quantitative determination of sensitive prediction indicators and proposes a hierarchical optimization framework for both regional and local critical thresholds. By integrating long-term historical statistics, laboratory kinetic tests of gas desorption, and in-situ multi-point tracking and verification, the critical thresholds undergo scientific calibration and site-specific alignment. Empirical research conducted on the No. 1 coal seam of the Miluo Coal Mine in Guizhou demonstrates that, at the regional prediction level, gas content and gas pressure exhibit equivalent sensitivity, with established critical values of 8.0 m3/t and 0.74 MPa, respectively. Furthermore, the sensitivity hierarchy for local prediction indicators was determined as [Formula: see text]. Significantly, the finalized local thresholds ([Formula: see text]= 0.47 mL/(g·min0.5), [Formula: see text]= 184 Pa, and S= 6.0 kg/m) are more stringent than the recommendations set forth in the Detailed Rules for Prevention and Control of Coal and Gas Outburst. The proposed prediction system effectively standardizes disaster characterization in complex coal seams and provides strategic guidance for coal mining enterprises to establish precision-based, site-specific outburst prevention standards. Coal and gas outbursts constitute a highly destructive dynamic phenomenon inherent in deep coal mining operations. Current outburst prediction frameworks largely depend on a uniform critical threshold system mandated by national regulations. However, in heterogeneous coal seams characterized by complex geological conditions, this universal approach frequently leads to "low-index outburst" incidents or excessive engineering redundancy, significantly undermining the intrinsic safety of mining operations. To resolve this fundamental scientific bottleneck, the present study establishes a theoretical methodology for the quantitative determination of sensitive prediction indicators and proposes a hierarchical optimization framework for both regional and local critical thresholds. By integrating long-term historical statistics, laboratory kinetic tests of gas desorption, and in-situ multi-point tracking and verification, the critical thresholds undergo rigorous scientific calibration and site-specific alignment. Empirical research conducted on the No. 1 coal seam of the Miluo Coal Mine in Guizhou demonstrates that, at the regional prediction level, gas content (w) and gas pressure (p) exhibit equivalent sensitivity, with established critical values of 8.0 m3/t and 0.74 MPa, respectively. Furthermore, the sensitivity hierarchy for local prediction indicators was established as [Formula: see text]. Significantly, the finalized local thresholds ([Formula: see text]= 0.47 mL/(g·min0.5), [Formula: see text]= 184 Pa, and S= 6.0 kg/m) are more stringent than the standards set forth in the Detailed Rules for Prevention and Control of Coal and Gas Outburst. The proposed prediction system effectively standardizes hazard characterization in complex coal seams and provides strategic guidance for coal mining enterprises to establish precision-based, site-specific outburst prevention standards.
This study investigates the bearing behavior of a pile-bucket composite foundation in marine soft clay under combined vertical, horizontal, and moment (V-H-M) loading, with direct application to offshore photovoltaic systems deployed in shallow-water regions. Centrifuge tests and validated 3D finite element analyses employing the Nanshui constitutive model were conducted. The results indicate that the pile-bucket foundation exhibits a hybrid deformation mode, effectively integrating the deep rotational restraint of the pile with the shallow translational constraint of the bucket. Under combined V-H-M loading, the composite foundation demonstrates a significantly expanded failure envelope. Notably, the vertical load enhances the lateral capacity to a greater extent in the composite system compared to monopile or suction caisson foundations. Plastic strain analysis reveals a synergistic interaction, where the pile extends the plastic zone deeper while the bucket mobilizes a broader near-surface soil mass, leading to a more distributed and efficient load-transfer mechanism. The findings provide critical insights for the optimized design of innovative pile-bucket hybrid foundations in soft clay for offshore photovoltaic arrays.
The development of spintronic memory and logic devices depends on an understanding of current-driven domain wall dynamics in realistic nanostructures. Using micromagnetic simulations, we investigate transverse head-to-head domain wall motion in CoFeB nanostrips under deterministic (0 K) and thermally activated (300 K) circumstances, considering both smooth and structurally disordered geometries. The domain wall velocity in smooth nanostrips increases almost linearly with current density at 0 K, indicating effective spin-transfer-torque-driven propagation. However, thermal fluctuations cause domain-wall deformation and a tendency toward velocity saturation at 300 K, especially in thicker nanostrips. To simulate realistic polycrystalline microstructures, structural disorder is introduced using Voronoi tessellation with 10% variations in saturation magnetization and exchange stiffness. Domain wall motion in rough nanostrips exhibits creep-like dynamics, characterized by intermittent propagation and thermally aided depinning from pinning sites induced by disorder. Additionally, as the nanostrip thickness increases, domain wall mobility decreases, accompanied by increased wall deformation and roughness. These findings show that current-driven domain wall dynamics are collectively governed by thermal fluctuations, structural disorder, and geometrical confinement, offering guidance for the design of thermally robust CoFeB-based spintronic devices.
The purity of commercial cucumber varieties, predominantly F1 hybrids, is a critical factor in seed quality assessment. While SSR or SNP markers are commonly used, insertion-deletion (InDel) markers offer greater stability and practical advantages, including lower cost, compatibility with agarose gel electrophoresis, and suitability for multiplex PCR. In this study, we developed long InDel (> 30 bp) markers for variety purity detection in cucumber using whole-genome resequencing data from 182 accessions. A total of 326,479 InDel loci were identified, of which 2655 long InDels satisfy the following requirements which have conserved 200 bp flanking sequences, high polymorphism and suitability for primer design. From these, 74 markers with minor allele frequency (MAF) > 0.2 across four cucumber populations were evenly distributed across seven chromosomes and synthesized. All markers successfully amplified target products, and 10 exhibited high heterozygosity and clear bands on agarose gels. These 10 markers were further adapted for multiplex capillary electrophoresis detection by adding different fluorescent groups. DNA fingerprints of 66 commercial cucumber varieties were constructed using these markers. Among them, four markers InD_Cu35, InD_Cu46, InD_Cu61 and InD_Cu66, showed at least one heterozygous genotype across all varieties, making them particularly suitable for purity testing of cucumber hybrid varieties. The practical utility of these markers was confirmed by assessing the purity of a new hybrid variety, Lvlinglong (98.9%). These long InDel markers provide a robust, flexible, and cost-effective tool for routine cucumber variety purity detection and genetic analysis.
Circadian rhythms shape antitumor immunity by regulating endocrine signaling, vascular permissiveness, leukocyte trafficking, metabolism, and suppressive features of the tumor microenvironment across the 24-h cycle. Here, we propose that biological time may represent an important design variable for CAR T-cell therapy. This concept may be particularly relevant to in vivo CAR T platforms, which could extend temporal control beyond infusion timing through repeatable induction, tunable amplitude, and reversible shutdown. We discuss evidence that CD8⁺ T-cell clocks, neuroendocrine oscillations, endothelial gatekeeping, and rhythmic tumor-microenvironment remodeling influence immune access, effector competence, exhaustion risk, and inflammatory toxicity. We further examine how viral vectors, lipid nanoparticles, and programmable control circuits might enable circadian-aware CAR installation and duty-cycling. Together, these observations support chrono-synthetic CAR T as a testable translational framework for precision immuno-oncology.
This study proposes a fuzzy machine learning framework for optimizing antiepileptic drug selection using Quantitative Structure-Property Relationship (QSPR) modeling under pharmacological uncertainty. Feature relevance was assessed using Random Forest-based importance and SelectKBest with mutual information, and a feedforward neural network was trained with 5-fold cross-validation. Fuzzy membership functions were incorporated to model variability in clinical and experimental data. Compared with Multiple Linear Regression and conventional QSPR models, the proposed approach achieved a 24 percent reduction in RMSE. Predicted pharmacological attributes were further integrated into a Multi-Criteria Decision-Making framework using TOPSIS to rank drug candidates based on efficacy, safety, and cost. The resulting rankings showed 95 percent Spearman correlation with clinician evaluations, demonstrating the framework reliability for uncertainty-aware antiepileptic drug prioritization and custom MCDM libraries for TOPSIS based prioritization.
Very preterm (VP) infants undergo rapid brain development while hospitalized in the neonatal intensive care unit (NICU). Meaningful auditory experiences enhance brain development, yet understanding VP infants' auditory environment remains a challenge. We examined the trajectories of auditory exposures of VP infants before term-equivalent age. This was a prospective, observational study of 25 VP infants born ≤32 weeks and 6 days gestation in a hybrid-design NICU. We collected 128 auditory recordings using language environment analysis (LENA) devices, with up to six consecutive weekly recordings per infant. We performed repeated-measure correlations between auditory measurements and postmenstrual age (PMA) and assessed relationships with room type and parental presence using stratification. Between 31 and 39 weeks PMA, VP infants experienced primarily silence (63.2% of recorded time) and electronic sounds (14.5%), with overall limited meaningful language exposure (3.9%). With advancing PMA and transitioning from single-family rooms to semi-private bays, meaningful language increased (r = 0.54, p < 0.001) and noise exposure decreased (r = -0.59, p < 0.001). Higher parental presence appeared to positively correlate with language exposure. VP infants experience reduced meaningful auditory exposures during NICU hospitalization. Further work should examine how modifiable NICU environment factors could be leveraged to optimize auditory experiences during a sensitive period. NICU auditory environments remain suboptimal for preterm infants, with a predominance of silence and limited meaningful experiences. In this hybrid-design NICU, meaningful language exposures monitored longitudinally increased with advancing postmenstrual age and as infants transitioned from single-family rooms to semi-private bays. Modifiable factors, including NICU designs, models of care, and parental presence, may play a role in optimizing auditory exposures of preterm infants. A better understanding of factors influencing the auditory experience can facilitate the design of effective interventions in the NICU.
暂无摘要（点击查看详情）
Although Angelica sinensis (Oliv.) Diels possesses anti-glioma potential, its active compounds and molecular targets and molecular mechanisms are still not fully understood. This study aims to systematically identify the active components of Angelica sinensis involved in glioma treatment, elucidate its molecular mechanisms, and validate these findings through an integrated approach combining network pharmacology and experimental verification. An integrated methodological approach-incorporating network pharmacology, molecular docking techniques, and in vitro experiments-was adopted in the present research. Using network pharmacology, common targets of Angelica sinensis and stigmasterol in glioma were identified and subjected to GO and KEGG enrichment analysis. To evaluate the molecular interaction, docking simulations were performed between active constituents of Angelica sinensis and the core target proteins. Finally, we performed a battery of cellular assays to validate the effects of Angelica sinensis and stigmasterol on proliferation, clonogenicity, migration, invasion, and ferroptosis in U251-MG cells. Network pharmacology predictions indicated that the p53 signaling pathway is a key pathway mediated by stigmasterol. In vitro experiments confirmed that Angelica sinensis and stigmasterol significantly suppressed malignant phenotypes in U251-MG cells. Mechanistically, stigmasterol activated the p53 pathway, upregulating ACSL4 while downregulating GPX4 and SLC7A11 expression, thereby inducing ferroptosis. This study reveals that stigmasterol, an active component of Angelica sinensis, can inhibit the proliferation of glioma cells by activating the p53 signaling pathway to induce ferroptosis. These findings provide a theoretical basis for the development of novel stigmasterol-based therapeutic strategies against glioma.
Soil salinization and alkalization severely constrain crop production worldwide, yet the combined effects of mixed saline-alkaline stress on minor cereal germination have received limited investigation. This study sought to elucidate the effects of mixed saline-alkaline stress on seed germination and seedling growth of proso millet (Panicum miliaceum L.) under controlled laboratory conditions, and to comparatively assess the relative contributions of osmotic stress, ionic toxicity, and high-pH damage through analysis of different neutral-to-alkaline salt ratios at equivalent Na⁺ concentrations. Seeds were subjected to nine treatment combinations comprising three salinity levels (80, 160, and 240 mM Na⁺) and three neutral-to-alkaline salt ratios (3:1, 1:1, and 1:3), employing NaCl, Na₂SO₄, NaHCO₃, and Na₂CO₃. Germination characteristics, seedling morphology, and key physiological parameters were assessed over seven days. Mixed saline-alkaline stress markedly suppressed germination in a concentration-dependent manner, with germination percentage decreasing from 94.5% (control) to 23.8% at 240 mM Na⁺ under high-alkali conditions (P < 0.05). At equivalent Na⁺ concentrations, high-alkali treatments (pH > 9.5) diminished germination index by 35.6-52.3% relative to low-alkali treatments. The IC₅₀ values were 187.3 mM Na⁺ (95% CI: 171.2-205.8 mM; R² = 0.982), 142.6 mM Na⁺ (95% CI: 131.4-155.1 mM; R² = 0.976), and 98.4 mM Na⁺ (95% CI: 89.7-108.3 mM; R² = 0.991) for low-, medium-, and high-alkali ratios, respectively. High-alkali stress was associated with 67.4% greater electrolyte leakage and 2.3-fold higher MDA accumulation than neutral salt treatments at equivalent Na⁺ concentrations, indicating greater membrane disruption under high-pH conditions. Two-way ANOVA revealed significant main effects of both Na⁺ concentration and alkaline proportion, as well as a significant salinity × alkalinity interaction (P < 0.001; Supplementary Table S1). These results demonstrate that alkaline stress exerts stronger inhibitory effects on proso millet germination compared to neutral salt stress, with the magnitude of salinity effects being dependent on the pH level. While complete mechanistic separation of osmotic, ionic, and pH components was not achieved due to experimental design limitations (absence of iso-osmotic non-ionic controls and direct ion measurements), comparative analysis across salt ratio treatments suggests that high pH is associated with additional inhibitory effects beyond those observed in neutral salt treatments. These controlled-condition findings establish preliminary germination -stage thresholds that may inform screening protocols for saline-alkaline tolerance breeding in proso millet, pending validation under field conditions.
We are developing a decision support system for treatment response assessment of bladder cancer by analyzing patients' CT urography (CTU) examinations. Accurate segmentation of bladder lesions is a critical and challenging task. We previously developed a bladder cancer segmentation method using a deep learning convolutional neural network and level sets (DL-CNN + LS). In this study, we designed several deep learning models based on U-Net for bladder cancer segmentation and compared them with DL-CNN + LS and two transformer-based models developed for medical imaging - DATTNet and the Med-Segment Anything Model (Med-SAM). Our new U-Net models did not use the second-stage level set refinement, greatly simplifying the overall segmentation pipeline. We trained and evaluated the models by using radiologist's hand-drawn 3D contours as the reference standard. The proposed Crop U-Net model, utilizing a user-defined box to direct the U-Net attention to the lesion region by masking out the structured background, was superior to other models being investigated. On the independent test set, the Crop U-Net achieved average Jaccard index (AJI) of 48.1 ± 18.0% and average minimum distance (AMD) of 4.3 ± 3.0 mm, while the DL-CNN + LS achieved AJI of 33.2 ± 20.0% and AMD of 5.3 ± 2.2 mm. The results demonstrated that the Crop U-Net could achieve a higher accuracy than the previous DL-CNN + LS while reducing the complexity of the segmentation pipeline.
Electrical signalling across distinct populations of brain cells underpins cognitive and emotional function. However, approaches that selectively regulate electrical signalling between two cellular components of a mammalian neural circuit remain sparse. Here we engineered an electrical synapse composed of two connexin proteins1 found in Morone americana (white perch fish)-connexin 34.7 and connexin 35-to accomplish mammalian circuit modulation. By exploiting protein mutagenesis, devising a new in vitro system for assaying connexin hemichannel docking, and performing computational modelling of hemichannel interactions, we uncovered a structural motif that contributes to electrical synapse formation. Targeting this motif, we designed connexin 34.7 and connexin 35 hemichannels that dock with each other to form an electrical synapse but not with other major connexins expressed in the mammalian central nervous system. We validated this electrical synapse in vivo using worms (Caenorhabditis elegans) and mice (Mus musculus). We demonstrate that it can strengthen communication across neural circuits composed of pairs of distinct cell types and modify behaviour accordingly. Thus, we establish 'long-term integration of circuits using connexins' (LinCx) for precision circuit editing in mammals.
We propose and numerically demonstrate subtractive color filters (SCFs) based on a plasmonic metasurface including coaxial aperture geometry working in the visible range. The Proposed designs involve arranging coaxial apertures (circular, elliptical, square, rectangular) in a silver film for reflection mode color filtering. By controlling the geometric parameters of the SCFs design, we achieve tunability of high-absorption resonance peaks (> 99.9%) across the visible spectrum. The proposed SCFs are supposed to exhibit a polarization-insensitive operation for symmetric apertures and a polarization-sensitive response for asymmetric apertures. Furthermore, the devices provide a relaxed angular tolerance. Finally, our results are then modeled using nonlinear regression analysis to a cubic regression, and a quadratic exponential model. Accordingly, it is possible to create an SCF for any color of choice in a straightforward way. This technique constitutes a promising process that provides a methodology to design color filters for practical applications such as color printing, high-resolution chromatic displays, and multispectral imaging.
Renal involvement in sarcoidosis may manifest as hypercalcemia, hypercalciuria, nephrolithiasis, nephrocalcinosis, and other abnormalities, but is often underrecognized. This study investigated chest radiographic findings suggestive of possible sarcoidosis in patients with nephrolithiasis and compared available laboratory parameters between patients with and without such findings. This retrospective analytical cross-sectional study included patients undergoing PCNL or TUL for urinary stones at Razi Hospital (Guilan, Iran) from 2016 to 2021. Routine preoperative chest X-rays were reviewed; 1297 encounters had both a retrievable diagnostic-quality CXR and available core laboratory data. Two pulmonologists independently interpreted CXRs for findings suggestive of possible sarcoidosis and assigned Scadding stage (0-IV), each blinded to the other's assessment; discrepancies were adjudicated by a third pulmonologist. Preoperative laboratory variables (BUN, creatinine, uric acid, alkaline phosphatase, calcium, phosphorus, AST, ALT, and CBC) were extracted and compared between patients with radiographic findings suggestive of possible sarcoidosis (RFS group) and those without such findings (non-RFS group) using Mann-Whitney U tests and chi-square/Cochran-Armitage trend tests. Logistic regression was used to examine the association between elevated creatinine and RFS. Radiographic findings suggestive of possible sarcoidosis were identified in 2.5% of patients, most commonly in middle-aged men and predominantly as Scadding stage I patterns. Creatinine levels were higher in the RFS group than in the non-RFS group. Elevated creatinine was associated with RFS in unadjusted analysis but not after adjustment for age and sex. Radiographic findings suggestive of possible sarcoidosis were identified in a small subset of nephrolithiasis patients. Although higher creatinine levels were observed in this group, this finding should be interpreted cautiously. Chest radiography may help identify patients who warrant further evaluation, but prospective studies incorporating confirmatory diagnostic methods are needed to clarify the clinical significance of these findings.
To identify early indicators of short‑term drought in tea plantations driven by climatic and environmental changes, this study develops a LASSO-Cox-nomogram predictive model to achieve accurate prediction of short term and localized drought variation in tea plantations. Corresponding variability quantification indices were designed for multisource climatic data collected by Internet of Things devices. Limma differential analysis was used to examine climatic variables under different drought severities. Combined with univariable Cox regression, this approach systematically screened key climatic factors significantly associated with drought severity and showing clear variation patterns across drought stages. A nomogram was then constructed using LASSO for variable selection and Cox regression for multivariate analysis to assess the impact of climatic changes on drought conditions. LASSO regression was used to screen modeling factors, and fivefold cross‑validation together with multivariate Cox analysis was applied to establish the model. A nomogram was then constructed, and a visual prediction system was developed using Shiny and DynNOM. The prediction model achieved AUC values of 0.776, 0.762, and 0.777 for soil moisture content changes exceeding - 5%, 0%, and 5%, respectively, in the training set. In the validation set, corresponding AUC values were 0.742, 0.799, and 0.710. The model demonstrates strong discriminative ability and effectively captures differences in soil moisture across distinct variation intervals. The calibration curves closely matched the ideal reference lines, and the temporal hold-out testing demonstrated an accuracy of 78.57%. The developed drought prediction system enables accurate forecasting of short-term, localized drought variations in tea plantations. It offers high precision with low computational demand, thereby providing a foundation for improving the yield and quality of Yunnan tea.
Forest fires in Turkey have received comparatively limited scholarly attention despite the country's high seasonal susceptibility, particularly during summer due to adverse climatic conditions. For real-time detection and risk assessment, this research suggests an integrated intelligent wildfire monitoring and prediction framework that integrates a unique weighted-voting RF-XGB hybrid model with Internet of Things (IoT)-based wireless sensor networks (WSNs). The adaptive weighting approach, which goes beyond traditional majority-voting ensembles, combines Random Forest and Extreme Gradient Boosting to take use of complementary variance-reduction and boosting processes. This is the methodological innovation. A multi-season Turkish forest fire dataset that included environmental sensor data, including temperature, relative humidity, and carbon monoxide concentration, was used to train the model. Distribution-preserving sampling and stratified k-fold cross-validation were used to alleviate class imbalance. With an accuracy of 0.9631, F1-score of 0.9627, and ROC-AUC of 0.994, the suggested hybrid model outperforms the others when compared to RF, XGBoost, KNN, Decision Tree, MLR, SVM, and ANN. Larger improvements were shown over KNN (10.4%) and Decision Tree (18.3%), while Relative Improvement (RI), as determined by the AUC measure, reveals a 4.6% increase over XGBoost and 5.7% over Random Forest-the strongest baselines. When compared to MLR, SVM, and ANN, improvements of over 50% were seen, demonstrating the hybrid model's greater robustness and discriminating capabilities. At the system level, a lightweight Multiple Logistic Regression (MLR) model was deployed on Arduino Nano-based sensor nodes to enable edge-level probability estimation and reduce communication overhead. Nodes operate using hourly duty cycling and transmit only when fire probability exceeds a predefined threshold, achieving an analytically estimated lifetime of up to 11 months. The framework was implemented in Zeytinpark using 80 sensor nodes deployed via hybrid grid and K-means clustering, achieving 95.58% coverage. Real-time detections are verified at the sink node using the RF-XGB model before triggering multi-level alerts, including local alarms, cloud updates, Telegram notifications, and mobile-based fire localization. The results demonstrate that the proposed contribution lies in the adaptive hybrid ensemble design, hierarchical edge-cloud intelligence distribution, and validated real-world deployment. The framework provides a robust, energy-efficient, and scalable solution for rapid wildfire detection and forecasting.
This study focuses on the vessel normalization window of anlotinib to preliminarily explore the optimal intervention timing for combining anlotinib with whole brain radiotherapy (WBRT) in treating brain metastases from non-small cell lung cancer (NSCLC). We aimed to explore the feasibility of combining anlotinib with WBRT based on the hypothesized vascular normalization window, and to investigate potential associations with intracranial tumor control, iPFS, and quality of life in patients with NSCLC brain metastases. This study was designed as a prospective, non-randomized, single-center cohort study. From Feb 8, 2024, to Sep 30, 2025, a total of 38 patients with NSCLC brain metastases diagnosed by the Department of Oncology, the Fifth Affiliated Hospital of Chengdu University of Traditional Chinese Medicine, were prospectively recruited. Anlotinib was used as the intervention measure in this study. According to whether the patients received anlotinib or not, they were divided into the experimental group (anlotinib combined with WBRT) and the control group (sole WBRT), with 19 patients in each group. In the experimental group, the vascular normalization time window of anlotinib, which is 5 to 7 days, was precisely utilized. The specific medication regimen was to start taking 8 mg of anlotinib 5 days before the initiation of WBRT and continue the medication until the end of WBRT. In contrast, the control group received only WBRT. The primary and secondary endpoint indicators of the patients in both groups were followed up regularly. The primary endpoint indicators included the intracranial objective response rate (iORR) and iPFS, while the secondary endpoint indicators included the intracranial disease control rate (iDCR), quality of life, and adverse reactions. The Kaplan-Meier method was used to draw the survival curve.Meanwhile, the clinical characteristics of the patients in both groups, such as gender, age, primary tumor site, T stage, N stage, and the number of brain metastases, were collected. Univariate analysis was used to screen out the prognostic factors that might affect iPFS. Then, the factors with statistical differences (P < 0.10) in the univariate analysis were taken as independent variables, and further Cox multivariate regression analysis was carried out to explore the independent prognostic factors affecting iPFS. The test standard P value was < 0.05. From Feb 8, 2024, to Sep 30, 2025, a total of 38 patients diagnosed with brain metastases from NSCLC by the Oncology Department of the Fifth Affiliated Hospital of Chengdu University of Traditional Chinese Medicine were prospectively recruited and included in the statistical analysis. The median follow-up time was 15.2 months (95% CI: 9.02-21.37). The results showed that the experimental group had better iORR (57.90% vs. 15.79%, P = 0.017) and iDCR (100% vs. 73.68%, P = 0.046) compared to the control group, with statistically differences. Compared with the control group, the experimental group showed a advantage in iPFS (6.7 months vs. 4.27 months, P = 0.038), and the median iPFS was extended by an additional 2.43 months. The results of subgroup analysis showed that the iPFS of patients with ≥ 3 brain metastases and patients with < 3 brain metastases were 6.3 months and 6.7 months, respectively, and there was no significant difference between the two groups (P = 0.723). The iPFS was longer in patients with less than 3 metastases than those with more than 3 metastases (11.73 months vs. 3.17 months, P = 0.035). After WBRT, the iPFS of NSCLC patients with brain metastases who received anti-tumor therapy was improved compared with those who did not receive anti-tumor therapy (8.67 months vs. 3.80 months, P = 0.040). In terms of quality of life, the experimental group showed better outcomes in functional status, symptom domains, and overall health compared to the control group over time. Regarding adverse reactions, the main ones included decreased appetite, fatigue, nausea and vomiting, hypertension, Myelosuppression, dizziness, headache, and abnormal liver function indicators. Grade ≥ 3 adverse reactions primarily included anemia, agranulocytosis, leukopenia, thrombocytopenia, cognitive impairment and abnormal liver function indicators, most of which were tolerable after symptomatic treatment. Univariate regression analysis of the overall population indicated that antitumor therapy after WBRT (P = 0.078) and the number of organ metastases (P = 0.038) were clinically relevant factors affecting iPFS. Further multivariate Cox regression analysis revealed that antitumor therapy after WBRT (P = 0.047) and the number of organ metastases (P = 0.028) were independent prognostic factors influencing iPFS. In this exploratory cohort, low-dose (8 mg) anlotinib administered 5-7 days prior to WBRT was associated with higher iORR, iDCR, and longer iPFS relative to WBRT alone in patients with NSCLC brain metastases. This combination regimen showed a manageable safety profile and trends toward improved quality of life. Subgroup analyses suggested that patients with < 3 organ metastases or those receiving post-WBRT antitumor therapy tended to have prolonged iPFS. Multivariate Cox regression identified post-WBRT antitumor therapy and number of organ metastases as potential independent prognostic factors for iPFS in this cohort. These findings are hypothesis-generating and require validation in larger randomized controlled trials.
Cross-sectional study. Evaluate sexual activity and satisfaction in women with spinal cord disease (SCD) and their associations with urinary incontinence. Outpatient neuro-urology clinics at a tertiary rehabilitation center in Brazil. Ninety-eight women aged ≥18 years with traumatic or non-traumatic SCD were included. Clinical and demographic data were collected through structured interviews. Bladder symptoms were assessed with the Neurogenic Bladder Symptom Score-Short Form (NBSS-SF). Sexual function was evaluated with the Female Sexual Function Index (FSFI), and sexual satisfaction with the WHOQOL-BREF. Sexual activity was defined as partnered sexual activity, with or without intercourse, within the prior six months. Logistic regression identified predictors of sexual activity and satisfaction. Mean age was 42.9 (±12.1) years; most cases were non-traumatic (85.7%), mainly multiple sclerosis (55.9%). Urinary incontinence was reported by 52%, severe in 28.6%. Sexual activity was reported by 48 women (49.0%), of whom 58.3% had FSFI-defined dysfunction and 60.4% reported satisfaction. Incontinence was strongly associated with inactivity (72.5% vs. 27.5%, p < 0.001) and lower FSFI scores (16.2 (10.3) vs. 24.4 (9.1), p < 0.001). Urinary continence (OR 5.0, 95% CI, 1.7 to 14.4), younger age (OR 0.93/year, 95% CI, 0.89 to 0.98), and being married (OR 10.9, 95% CI, 3.6 to 33.3) predicted sexual activity and satisfaction. Sexual dysfunction and UI were prevalent and interrelated in women with SCD. UI independently predicted inactivity and dissatisfaction.
The analysis of human movement data in sports science is often challenged by the inherent variability in movement speed and rhythm, which results in gait time-series data of inconsistent lengths (dynamic dimensionality). This poses a significant obstacle for traditional optimization algorithms in constructing accurate motion templates for performance analysis and rehabilitation. To address this, we propose a novel Dynamic Dimension Warping (DDW) algorithm specifically designed for efficient search in dynamic multidimensional spaces. DDW integrates a Cross-Dimensional Mapping (CDM) mechanism, fusing Dynamic Time Warping and Euclidean distance to enable comparison between variable-length sequences, and an Optimal Dimension Collection (ODC) method to break fixed-dimension constraints. When applied to the task of optimizing human gait templates from experimental data, DDW demonstrated superior performance against 31 benchmark algorithms, reducing average fitness to 9.16 (41% below mean) and achieving rapid convergence within 10 generations. The algorithm also attained global optima in 52.17% of classical function tests, confirming its robustness. This work establishes DDW as an effective optimization framework for complex, dynamic-dimensional problems, with direct methodological value for gait analysis and biomechanical motion assessment.