Search Results (160)

Artificial intelligence (AI) is becoming a central driver of change across the drug development lifecycle. However, its integration is evolving so rapidly that it remains essential to understand how these technologies are currently positioned within the field. Because reliable access to high-quality (effective and safe) drugs is essential to public health, the pharmaceutical product lifecycle (PPL) offers a coherent framework for evaluating how AI can enhance evidence and data creation across all stages. To understand where AI genuinely adds value, this review examines its contribution across the major stages of the PPL. Rather than treating drug discovery, nonclinical evaluation, clinical research, and post-marketing assessment as separate domains, we view them as a continuous chain of data, where digital technologies enhance different decision points in distinct ways. In early discovery, AI narrows the search space by integrating diverse datasets to prioritize candidates most likely to succeed. Nonclinical models increasingly rely on machine-learning systems designed to improve the human relevance of safety predictions. Within clinical trials, AI supports cohort formation, real-time monitoring, and new analytic strategies that supplement empirical evidence. Case studies from leading pharmaceutical companies illustrate that the most meaningful advances emerge when AI is embedded not as a standalone tool but as part of a broader data strategy that links information across stages. Taken together, current evidence suggests that AI is beginning to transform data generation and integration throughout the PPL. Given the accelerating pace of digital innovation, it is essential for the field to maintain continuous awareness of emerging methodologies and evolving regulatory frameworks to ensure that these technologies are implemented in a reliable, transparent, and scientifically grounded manner. Full article

Florpyrauxifen-benzyl (FPB) is an auxin-mimic herbicide that controls selected grasses, sedges, and broadleaves in rice cropping systems. Field experiments were conducted in 2023 and 2024 to characterize the effects of FPB on crop safety and weed control when applied alone or in combination with other herbicides, and to assess whether FPB can provide season-long, effective weed management. Base treatments of benzobicyclon (BBC)/halosulfuron-methyl (HSM), clomazone (CLM), or thiobencarb (TBC) were applied on the day of seeding (DOS) or within the 2-leaf stage (LS) rice and followed by foliar treatments of FPB alone or in a mixture with bispyribac-sodium (BPS), penoxsulam (PNX)/cyhalofop-butyl (CHB), or propanil (PPL). Additionally, FPB was applied alone with no prior base treatment, in combination with a mixture partner, and as a sequential treatment, 14 days apart, with the first application made to 4- to 5-LS rice; in contrast, the second application was made to mid-tillering rice. The FPB applied alone or in sequential application showed results for more than 98% of watergrasses and 100% of ricefield bulrush, smallflower umbrella sedge, ducksalad, redstems, and all other broadleaves control at 56 days after treatment (DAT). When applied after the base treatments, the weed control increased to 100% for all weed species at 14 DAT. The sequential application of FPB achieved the highest yields of 7683 kg ha⁻¹ in 2023 and 11,249 kg ha⁻¹ in 2024, resulting in 3.6- and 6.4-fold increases in rice yield over the nontreated control. Owing to its excellent sedge and broadleaf weed control and good activity on troublesome grasses, such as barnyardgrass, FPB could be an essential part of the weed management programs in rice production systems. Full article

Iterative self-training of language models presents a promising avenue for realizing self-improving Artificial Intelligence systems; however, this process is often hindered by the fundamental challenge of “Model Collapse.” Existing research indicates that models undergo catastrophic performance degradation and diversity collapse when recursively trained on their own increasingly homogenized synthetic data. Although some data selection-based approaches attempt to mitigate this issue by enhancing diversity, they predominantly rely on static strategies, lacking a feedback mechanism capable of adapting in real-time to the dynamic evolution of the model state and data distribution. To address this limitation, we propose a dynamic data selection framework titled “DCES” (dynamic center-edge sampling). We conducted extensive experiments on iterative self-training tasks across multiple model architectures. The results demonstrate that our system significantly outperforms baselines in terms of Perplexity (PPL) and loss across various models and test sets. Simultaneously, the framework effectively mitigates the degradation of Expected Calibration Error (ECE) and entropy metrics, successfully preventing mode collapse. Our findings highlight that an adaptive system capable of intelligent data curation based on training feedback is pivotal for maintaining the dynamic balance of data distributions and achieving sustainable AI self-evolution. This work provides a systematic methodology for realizing this goal. Full article

Background: Glioblastoma (GBM) is a lethal, highly invasive, and recurrent brain tumor. Standard treatment combines maximal surgical resection, radiation, and chemotherapy; however, such approaches are often infeasible for tumors in eloquent brain regions. Objective: Histotripsy is a noninvasive, nonthermal ultrasound-based mechanical ablation modality that employs focused acoustic energy for targeted tissue destruction. This study aimed to investigate the feasibility, safety, and therapeutic effect of a one-time transcranial histotripsy treatment in a pre-clinical murine GBM model. Methods: GL261 GBM cells were orthotopically implanted into C56BL/6 mouse brains. Transcranial histotripsy was performed using a stereotactically guided 2 MHz transducer targeting either lower (25%) or higher (75%) tumor volume, with 5 or 10 pulses per location (PPL) administered. Tumor growth and cerebral injury were monitored with weekly magnetic resonance imaging (MRI) following treatment. At the study endpoint, hematoxylin and eosin (H&E) histology assessed residual tumor burden and histotripsy-induced tissue changes. Results: Mice receiving 5 PPL high-percent treatment (>30 sites) showed a statistically significant median survival extension of 5 days (18.5%) compared to untreated controls. MRI demonstrated marked tumor volume reduction in the high-percent treatment group at week 4, while H&E confirmed increased tumor necrosis and cellular damage in the treated cohort. Conclusions: Single-session, incisionless transcranial histotripsy was well tolerated and conferred mild yet meaningful survival advantages in this GBM model. These results support ongoing exploration of histotripsy, alone or in combination with existing therapies, for safe and effective treatment of challenging brain tumors. Full article

In recent years, the expansion of large-model parameters has substantially increased storage and inference overhead. Consequently, post-training quantization has become a key technique for reducing model size and inference-time energy consumption. However, we observe that, under extremely low bit-width settings, mainstream error-compensation-based algorithms tend to overfit the calibration data. To mitigate this issue, we propose Plug-in Balanced Binary Quantization for LLMs (PBBQ), which reduces the excessive emphasis on subsequent channels via block-wise dropout and layer-wise reordering. PBBQ can be integrated into GPTQ-style frameworks and ultra-low-bit methods such as BiLLM and ARB-LLM. Experimental results show that PBBQ significantly improves the performance of multiple error-compensation quantization algorithms. When combined with the state-of-the-art methods BiLLM and ARB-LLM, the perplexity (ppl) on WikiText-2 is reduced by 21.46% (from 32.48 to 25.51) and 22.02% (from 16.44 to 12.82), respectively. Full article

Introduction: Lung cancer is a leading cause of cancer-related deaths globally, with approximately 1.5 million new peripheral pulmonary lesions (PPLs) detected annually in the United States. Robotic-assisted bronchoscopy (RAB) has emerged as a promising technology, with two platforms initially approved, the Monarch platform (Auris Health Inc, Redwood City, CA, USA) and the Ion Endoluminal System (Intuitive Surgical, Sunnyvale, CA, USA), offering improved stability and distal airway visualization. As RAB adoption increases, there is a critical need for comparative effectiveness data of these systems to guide clinical decision-making and institutional investments. This study aims to compare the diagnostic yield and safety profiles of the Ion and Monarch RAB platforms after introduction at a single institution. Methods: We conducted a retrospective chart review of patients undergoing RAB in the first six months following the introduction of each platform. Demographic and radiographic data were collected. Diagnostic yield was defined as obtaining a malignant or specific benign diagnosis from bronchoscopy. Results: The study included 56 Ion and 36 Monarch procedures. Diagnostic yield was similar between Ion (75%) and Monarch (72%) groups (p = 0.8), with an adjusted odds ratio 0.89 (95% CI 0.30–2.72). Complications were low, with one pneumothorax occurring in each group. Conclusions: Early adoption and use of both RAB platforms suggests comparable diagnostic yields and safety profiles in our limited sample size. Larger studies including standardized anesthesia protocol and systematic use of real-time imaging are needed for further evaluation and comparative analysis. Full article

Background: This study examined the acute effects of post-run plantar fascia recovery training (PFRT) on dorsal kinetic chain performance (DKCP) in adolescent long-distance runners. Methods: Thirty-four adolescent runners were randomly assigned to a PFRT group (n = 17) or a control group (n = 17). Following a standardized running session, the PFRT group received bilateral PFRT. Assessments were performed on the dominant side at three time points: pre-training, post-training, and post-PFRT. DKCP was evaluated using the Bunkie Test for the posterior stabilization line (PSL) and posterior power line (PPL), Myoton measurements of the latissimus dorsi, erector spinae, hamstrings, and gastrocnemius, the Sit-and-Reach Test for hamstring/lumbar flexibility, and the Modified Schober Test for lumbar mobility. Results: No significant group × time interactions were observed for any outcome except lumbar mobility. PSL performance increased significantly following PFRT compared with post-training (p = 0.016), whereas PPL performance did not change. Lumbar mobility improved significantly over time (p < 0.05). Although latissimus dorsi stiffness and hamstring and gastrocnemius stiffness were lower in the PFRT group at baseline, no significant within-group changes were observed following PFRT. Conclusions: PFRT may acutely improve lumbar mobility as a recovery intervention in adolescent runners. Further research is needed to clarify its short- and long-term effects within structured recovery programs during adolescence. Full article

Background: Physical literacy is a multidimensional construct that may be relevant for promoting active lifestyles and healthy development during adolescence. However, the association between perceived physical literacy (PPL) and body composition assessed by dual-energy X-ray absorptiometry (DXA) remains underexplored. Objective: To examine the association between PPL and DXA-derived body composition parameters in Spanish adolescents. Methods: This cross-sectional study included 56 adolescents (13.2 ± 1.27 years, 28.6% girls). PPL was assessed using the validated Spanish version of the Perceived Physical Literacy Instrument (S-PPLI). Body composition was measured by DXA. Associations between PPL and body composition outcomes were examined using general linear models, adjusting for sex, age, and device-measured moderate-to-vigorous physical activity (MVPA) and sedentary time. Results: Higher PPL was significantly associated with greater lean body mass (β = 0.81; p = 0.02), lean mass index (β = 0.22; p = 0.01), and fat-free mass (β = 0.85; p = 0.01), as well as with higher body mass index (BMI) (β = 0.24; p = 0.03). Conclusions: Higher PPL is associated with more favorable lean-related body composition outcomes in Spanish adolescents, whereas no associations were found with adiposity or bone parameters. These findings highlight PPL as a relevant correlation of lean body composition during adolescence. Given the cross-sectional design, causal inferences cannot be drawn, and future longitudinal and interventional studies are warranted. Full article

Exogenous fires in underground coal mines are characterized by low illumination, smoke occlusion, heavy dust loading and pseudo fire sources, which jointly degrade image quality and cause missed and false alarms in visual detection. To achieve accurate and real-time early warning under such conditions, this paper proposes a mine exogenous fire detection algorithm based on an improved YOLOv9m, termed PPL-YOLO-F-C. First, a lightweight PP-LCNet backbone is embedded into YOLOv9m to reduce the number of parameters and GFLOPs while maintaining multi-scale feature representation suitable for deployment on resource-constrained edge devices. Second, a Fully Connected Attention (FCAttention) module is introduced to perform fine-grained frequency–channel calibration, enhancing discriminative flame and smoke features and suppressing low-frequency background clutter and non-flame textures. Third, the original upsampling operators in the neck are replaced by the CARAFE content-aware dynamic upsampler to recover blurred flame contours and tenuous smoke edges and to strengthen small-object perception. In addition, an MPDIoU-based bounding-box regression loss is adopted to improve geometric sensitivity and localization accuracy for small fire spots. Experiments on a self-constructed mine fire image dataset comprising 3000 samples show that the proposed PPL-YOLO-F-C model achieves a precision of 97.36%, a recall of 84.91%, mAP@50 of 96.49% and mAP@50:95 of 76.6%, outperforming Faster R-CNN, YOLOv5m, YOLOv7 and YOLOv8m while using fewer parameters and lower computational cost. The results demonstrate that the proposed algorithm provides a robust and efficient solution for real-time exogenous fire detection and edge deployment in complex underground mine environments. Full article

Purpose: Fetal bovine serum (FBS) is widely used in cell culture due to its rich nutrient and growth factor content, but it poses ethical concerns, biosafety risks, and cost limitations. This study investigates plasma-depleted lyophilized (freeze-dried) porcine platelet lysate (pPL) as a potential alternative to FBS for use in cell-based research and biomanufacturing. Materials and Methods: Fresh porcine blood was processed to obtain plasma-depleted pPL using double centrifugation and repeated freeze–thaw cycles. The lysate was analyzed for growth factor content via ELISA, then freeze-dried and sterilized with gamma irradiation. Endotoxin levels and cytotoxicity were evaluated. The ability of plasma-depleted lyophilized pPL to promote cell proliferation was assessed using L929 fibroblast cultures and compared with FBS. Results: Plasma-depleted lyophilized pPL contained significantly higher levels of TGF-β1 than FBS. The freeze-dried product remained stable for at least three months at room temperature. Gamma irradiation effectively sterilized the lysate without degrading key growth factors. Plasma-depleted lyophilized pPL showed no cytotoxicity and promoted greater proliferation of L929 cells compared to FBS, indicating enhanced mitogenic activity. Conclusions: Plasma-depleted lyophilized pPL is a stable, safe, and growth factor-rich alternative to FBS. It supports fibroblast proliferation, retains bioactivity after sterilization and storage, and may provide a scalable, ethical option for cell culture in biomedical research, regenerative medicine, and therapeutic product development. Full article

Background: Endobronchial ultrasound-guided transbronchial biopsy (EBUS-TBB) is a valuable technique for diagnosing peripheral pulmonary lesions (PPLs). Although computer-aided diagnostic (CAD) systems have been explored for EBUS interpretation, most rely on manually selected 2D static frames and overlook temporal dynamics that may provide important cues for differentiating benign from malignant lesions. This study aimed to develop an artificial intelligence model that incorporates temporal modeling to analyze EBUS videos and improve lesion classification. Methods: We retrospectively collected EBUS videos from patients undergoing EBUS-TBB between November 2019 and January 2022. A dual-path 3D convolutional network (SlowFast) was employed for spatiotemporal feature extraction, and contrastive learning (SwAV) was integrated to enhance model generalizability on clinical data. Results: A total of 465 patients with corresponding EBUS videos were included. On the validation set, the SlowFast + SwAV_Frame model achieved an AUC of 0.857, accuracy of 82.26%, sensitivity of 93.18%, specificity of 55.56%, and F1-score of 88.17%, outperforming pulmonologists (accuracy 70.97%, sensitivity 77.27%, specificity 55.56%, F1-score 79.07%). On the test set, the model achieved an AUC of 0.823, accuracy of 76.92%, sensitivity of 84.85%, specificity of 63.16%, and F1-score of 82.35%. The proposed model also demonstrated superior performance compared with conventional 2D architectures. Conclusions: This study introduces the first CAD framework for real-time malignancy classification from full-length EBUS videos, which reduces reliance on manual image selection and improves diagnostic efficiency. In addition, given its higher accuracy compared with pulmonologists’ assessments, the framework shows strong potential for clinical applicability. Full article

Background/Objectives: Preclinical animal studies and clinical case reports have shown evidence of histotripsy being capable of inducing anti-tumor immune responses strong enough to inhibit tumor growth of off-target tumors. Previous studies exploring histotripsy immune stimulation have used a single therapy dose. This study aims to explore how histotripsy dose affects local tumor immune stimulation in a murine melanoma model. Methods: C57BL/6J mice bearing subcutaneous B16F10 tumors were treated with histotripsy using an ultrasound-guided 8-element 1 MHz transducer operating at a 100 Hz pulse repetition frequency (PRF) and >30 MPa peak-negative pressure. The histotripsy dose was defined by the number of pulses (8, 20, 40, or 100) per focal location (ppl). Tissue damage and residual tissue structure were measured histologically and scored by a trained pathologist. The longitudinal effect of histotripsy dosing was assessed using tumor growth and survival. Acute immune stimulation was measured at days 2 and 7 post-treatment via immunofluorescence staining of the treated tumor. Results: Histotripsy doses at 20, 40, and 100 ppl achieved significant tumor necrosis within the target region (>75%), with residual structure decreasing as the dose increased. Overall, the greatest tumor control was observed in mice that received the 40 ppl dose compared to untreated mice. This correlates with the 40 ppl dose also having the largest increase in CD45+ immune cells and CD8⁺ T cells 7 days post-treatment compared to untreated mice. Conclusions: The effect of histotripsy dosing on immune infiltration and tumor growth highlights the significant impact of histotripsy dose on clinical effects. Full article

A Bose-Fermi mixture, consisting of both bosons and fermions, exhibits distinctive quantum coherence and phase transitions, offering valuable insights into many-body quantum systems. The ground state, as the system’s lowest energy configuration, is essential for understanding its overall behavior. In this study, we introduce the Bose-Fermi Energy-based Deep Neural Network (BF-EnDNN), a novel deep learning approach designed to solve the ground-state problem of Bose-Fermi mixtures at zero temperature through energy minimization. This method incorporates three key innovations: point sampling pre-training, a Dynamic Symmetry Layer (DSL), and a Positivity Preserving Layer (PPL). These features significantly improve the network’s accuracy and stability in quantum calculations. Our numerical results show that BF-EnDNN achieves accuracy comparable to traditional finite difference methods, with effective extension to two-dimensional systems. The method demonstrates high precision across various parameters, making it a promising tool for investigating complex quantum systems. Full article

Background: General aviation pilots are, anecdotally, referred to as “weekend warriors” due to their flying infrequency. Considering that flight skills erode with irregular practice/reinforcement, we determined whether private pilots (PPLs) fly/train sufficiently to operate safely in the context of slow flight, a skill critical for safe operations and which rapidly atrophies with <~51 h flight time/8 months per prior research. Method: Slow-flight-related aviation accidents (2008–2019) were per the NTSB Access^R database, and fatal mishap rates were calculated using general aviation fleet times. Eight-month flight histories of airplanes in single PPL ownership were captured retrospectively using FlightAware^R. PPL survey responses were collected between January and March 2025. Statistical tests employed proportion/Independent-Samples Median Tests and a Poisson Distribution. Results: The slow-flight-related fatal accident rate (2017–2019) trended downwards (p = 0.077). In-flight tracking of 90 airplanes revealed an 8-month median flight time of 6 h, which is well below the aforementioned 51 h requisite for safe operations. Of the aircraft flown < 51 h, only 9% engaged in slow-flight practice. In the online survey, only the upper quartile of 126 PPLs achieved the aforementioned time requisite for preserving slow-flight skills, but nevertheless, 89% of respondents attested to being flight-proficient. Conclusions: Persistence in slow-flight-related fatal accidents likely partly reflects PPLs’ deficiency in in-flight time/slow-flight practice. Full article

Background: Peripheral pulmonary lesions (PPLs) are increasingly detected due to widespread use of cross-sectional imaging and lung cancer screening. While cone-beam CT and robotic bronchoscopy have improved diagnostic accuracy, they remain resource-intensive and inaccessible in many settings. A novel technique employing simultaneous use of two bronchoscopes referred to as the “Kissing Probe Technique” was developed to provide real-time lesion localization and precise sampling using standard equipment. Methods: This retrospective, single-centre study included 43 patients with radiologically confirmed PPLs suspicious for malignancy. Under general anaesthesia with rigid bronchoscopy or continuous sedation with endotracheal intubation, two single-use bronchoscopes were introduced in parallel. The first (standard diameter) housed a radial EBUS probe for lesion localization, while the second (ultra-thin) guided a 1.1 mm cryoprobe to the lesion based on direct ultrasound and fluoroscopic confirmation. Cryobiopsies were performed once “kissing contact” between the radial probe and cryoprobe was established. Results: A total of 43 procedures were completed without major complications. The mean lesion size was 24.6 mm. Radial probe localization was successful in 86% of cases, and tool-contact confirmation was achieved in 35/43 patients (81%). The overall diagnostic yield was 83.7% (36/43). Bleeding occurred in 23% of cases and was managed conservatively without the need for escalation of care. No pneumothorax or equipment-related damage occurred. Conclusions: The “Kissing Probe Technique” is a safe and feasible approach for bronchoscopic sampling of PPLs. It offers a cost-effective alternative for real-time tool-in-lesion confirmation using widely available equipment. Further multicentre validation is warranted to confirm generalizability and cost-effectiveness. Full article