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Pictilisib (GDC-0941) is a well-known dual inhibitor of class I PI3K and mTOR and is presently undergoing phase 2 clinical trials for cancer treatment. The present work investigated the dynamic behaviors and interaction mechanism between GDC-0941 and human serum albumin (HSA). Molecular docking and MD trajectory analyses revealed that GDC-0941 bound to HSA and that the binding site was positioned in subdomain IIA at Sudlow’s site I of HSA. The fluorescence intensity of HSA was strongly quenched by GDC-0941, and results showed that the HSA–GDC-0941 interaction was a static process caused by ground-state complex formation. The association constant of the HSA–GDC-0941 complex was approximately 105 M−1, reflecting moderate affinity. Thermodynamic analysis conclusions were identical with MD simulation results, which revealed that van der Waals interactions were the vital forces involved in the binding process. CD, synchronous, and 3D fluorescence spectroscopic results revealed that GDC-0941 induced the structural change in HSA. Moreover, the conformational change of HSA affected its molecular sizes, as evidenced by AFM. This work provides a useful research strategy for exploring the interaction of GDC-0941 with HSA, thus helping in the understanding of the transport and delivery of dual inhibitors in the blood circulation system.

Aim. Atherosclerosis is the major cause of acute coronary syndrome (ACS) which is a significant contributor to both morbidity and mortality in the world. The microbiome-derived metabolite trimethylamine-N-oxide (TMAO) has aroused great interest and controversy as a risk factor of atherosclerosis. Therefore, in this study, we aimed at investigating whether plasma TMAO can be a risk factor of atherosclerosis in coronary artery of patients with ACS and how this relates to lipids and proinflammatory cytokines in plasma. Methods. We enrolled consecutive patients with ACS who underwent percutaneous coronary intervention (PCI). Gensini scoring was used to evaluate angiographic atherosclerosis in the coronary artery of the patients. 13 patients were divided into low (Gensini score<25), 33 into intermediate (Gensini score 25-50), and 81 into severe atherosclerosis (Gensini score ≥50). Plasma TMAO, vasculitis factors, and cardiovascular biomarkers were measured by clinical biochemistry, intima-media thickness (IMT) of carotid artery was determined by the Color Doppler ultrasound, and the atherosclerotic lesion in coronary artery was assessed in PCI. Results. Plasma TMAO concentrations were positively associated with Gensini score (OR=0.629, p<0.001) and Gensini subgroup (R=0.604, p<0.001). Plasma TMAO concentrations in patients with severe coronary atherosclerosis were higher than those of patients with moderate coronary atherosclerosis, and the plasma TMAO concentrations of patients with moderate coronary atherosclerosis were higher than those of patients with mild coronary atherosclerosis, the difference was statistically significant [4.73 (3.13, 4.62) versus 1.13 (0.63, 3.34) versus 0.79 (0.20, 1.29), p<0.001], respectively. Furthermore, ROC analysis showed that plasma TMAO could identify the severity of atherosclerosis (p<0.001). The AUC of TMAO for severe atherosclerosis was 0.852 (95%CI=0.779−0.925). The sensitivity and specificity of TMAO for identifying severe atherosclerosis are 96.3% and 63.0% when the cut-off value of TMAO was set at 1.2715 pg/ml. Furthermore, logistic regression analysis showed plasma TMAO concentrations were positively associated with severity of atherosclerosis in coronary artery (OR=1.934, 95%CI=1.522−2.459, p<0.001). For all that, negatively association was observed between TMAO and age (OR=−0.224, p<0.05), B-type natriuretic peptide (BNP) (OR=−0.175, p<0.05), and interleukin-8 (IL-8) (OR=−0.324, p<0.001), while positive association was observed between TMAO and nitric oxide (NO) (OR=0.234, p<0.01). However, there is no obvious association was observed between Gensini score and cardiovascular biomarkers, vasculitis factors, and carotid IMT, respectively. Conclusion. Our cross-sectional observation suggested that plasma TMAO concentrations positively associated with coronary atherosclerosis in ACS patients and serve as a risk factor for severe atherosclerosis. Plasma TMAO also correlated with age, BNP, IL-8, and NO. However, no obvious association was found between atherosclerosis with vasculitis factors and cardiovascular biomarkers in this study, and there was no conclusive evidence showing TMAO enhance atherosclerosis via regulation of inflammation or lipid.

A UAV infrared target detection model ITD-YOLOv8 based on YOLOv8 is proposed to address the issues of model missed and false detections caused by complex ground background and uneven target scale in UAV aerial infrared image target detection, as well as high computational complexity. Firstly, an improved YOLOv8 backbone feature extraction network is designed based on the lightweight network GhostHGNetV2. It can effectively capture target feature information at different scales, improving target detection accuracy in complex environments while remaining lightweight. Secondly, the VoVGSCSP improves model perceptual abilities by referencing global contextual information and multiscale features to enhance neck structure. At the same time, a lightweight convolutional operation called AXConv is introduced to replace the regular convolutional module. Replacing traditional fixed-size convolution kernels with convolution kernels of different sizes effectively reduces the complexity of the model. Then, to further optimize the model and reduce missed and false detections during object detection, the CoordAtt attention mechanism is introduced in the neck of the model to weight the channel dimensions of the feature map, allowing the network to pay more attention to the important feature information, thereby improving the accuracy and robustness of object detection. Finally, the implementation of XIoU as a loss function for boundary boxes enhances the precision of target localization. The experimental findings demonstrate that ITD-YOLOv8, in comparison to YOLOv8n, effectively reduces the rate of missed and false detections for detecting multi-scale small targets in complex backgrounds. Additionally, it achieves a 41.9% reduction in model parameters and a 25.9% decrease in floating-point operations. Moreover, the mean accuracy (mAP) attains an impressive 93.5%, thereby confirming the model’s applicability for infrared target detection on unmanned aerial vehicles (UAVs).

Background: The role of dietary patterns in the development of chronic obstructive pulmonary disease (COPD), particularly under varying levels of ambient air pollution, remains insufficiently understood. Aims: We aimed to investigate the association between adherence to multiple established dietary patterns and the risk of incident COPD, and to assess potential effect modification by exposure to ambient air pollutants. Methods: We conducted a prospective study including 206,463 participants from the UK Biobank free of COPD at baseline. Individual-level residential air pollution exposure was estimated for the year 2010. Nine dietary indices were derived from 24 h dietary recalls. Associations with incident COPD were assessed using Cox proportional hazards models. Effect modification was examined using smoking-specific tertiles of nitrogen oxides (NO, NO2, and NOx) and particulate matter (PM2.5, PM2.5–10, and PM10). Results: Greater adherence to healthy dietary patterns was associated with a 14% to 34% reduced risk of COPD (highest vs. lowest quintile). In contrast, high adherence to the Unhealthful plant-based diet index (PDI) was associated with a 34% increased risk (HR = 1.34, 95% CI: 1.16–1.54). Notably, the protective associations of the AHA, EAT-Lancet, and MIND dietary patterns were most pronounced in settings with relatively high air pollution, as evidenced by elevated levels in at least four air quality indicators (p for interaction < 0.05). Conclusions: Adherence to AHA, EAT-Lancet, and MIND dietary patterns is associated with a reduced risk of incident COPD, with potentially amplified benefits observed in areas with higher ambient air pollution.

Objective The effects of three short-segment vertebral fixation methods—short-segment fixation (4s group), short-segment fixation across the injured vertebra (6s group), and long-segment fixation (8s group)—on the surgical efficacy of patients with type A thoracolumbar fractures were compared to identify the optimal fixation method. Methods Data from 277 patients who underwent posterior pedicle screw fixation for thoracolumbar fractures between September 2018 and January 2023 were retrospectively analyzed. Surgery-related indicators, laboratory parameters, clinical functional measures (VAS and ODI), and postoperative imaging findings were compared among the three groups. Results Baseline data showed no significant differences among the three groups. The operation time in the 4s group (75.352 ± 15.458 min) and intraoperative blood loss (188.65 ± 42.728 ml) were significantly lower compared to the 8s group (operation time: 108.243 ± 19.529 min; intraoperative blood loss: 209.93 ± 50.542 ml), with statistically significant differences ( p  < 0.05). Postoperative hematocrit (33.277 ± 4.639) and albumin levels (34.971 ± 4.116) in the 6s group were significantly higher than those in the 8s group (hematocrit: 31.820 ± 4.323; albumin: 33.170 ± 3.553), with p  < 0.05. Other outcome indicators did not show statistically significant differences ( p  > 0.05). Conclusion Short-segment fixation across the injured vertebra (6s) provides results comparable to short-segment fixation (4s) while causing less trauma. Furthermore, the 6s method demonstrates similar efficacy to long-segment fixation (8s) in maintaining long-term deformity correction. These findings offer valuable insights for clinicians in selecting surgical fixation methods, optimizing treatment strategies, and improving patient outcomes.

The primary objective of this study was to investigate the behavior of Listeria monocytogenes (L. monocytogenes) on fresh-cut cucumbers. Fresh-cut cucumber samples were inoculated with a mixture of six strains of L. monocytogenes. The inoculated samples were stored at 5, 10, 15, 20, 25, 30, and 35 °C. The results demonstrated that L. monocytogenes was able to grow on fresh-cut cucumbers at all the evaluated temperatures, although its growth decreased but was not inhibited at 5 °C. An extreme storage temperature of 35 °C considerably reduced the lag time. L. monocytogenes growth on fresh-cut cucumbers was controlled for several days by storage at a low temperature, mainly at 5 °C. Thus, this product should only be stored at low temperatures. The growth process was fitted by the Baranyi model, with the specific growth rates equally well-fitted to the Ratkowsky square-root model. The R-square and mean square error values for the corresponding Ratkowsky square-root models were 0.97 (R2 > 0.95) and 0.02, respectively. The Baranyi and Ratkowsky square-root models exhibited good relevancy. The predictive models developed in this study can be used to estimate the risk assessment of L. monocytogenes on fresh-cut cucumber.

Due to the objects in UAV aerial images often presenting characteristics of multiple scales, small objects, complex backgrounds, etc., the performance of object detection using current models is not satisfactory. To address the above issues, this paper designs a multiscale small object detection model for UAV aerial images, namely MSUD-YOLO, based on YOLOv10s. First, the model uses an attention scale sequence fusion mode to achieve more efficient multiscale feature fusion. Meanwhile, a tiny prediction head is incorporated to make the model focus on the low-level features, thus improving its ability to detect small objects. Secondly, a novel feature extraction module named CFormerCGLU has been designed, which improves feature extraction capability in a lighter way. In addition, the model uses lightweight convolution instead of standard convolution to reduce the model’s computation. Finally, the WIoU v3 loss function is used to make the model more focused on low-quality examples, thereby improving the model’s object localization ability. Experimental results on the VisDrone2019 dataset show that MSUD-YOLO improves mAP50 by 8.5% compared with YOLOv10s. Concurrently, the overall model reduces parameters by 6.3%, verifying the model’s effectiveness for object detection in UAV aerial images in complex environments. Furthermore, compared with multiple latest UAV object detection algorithms, our designed MSUD-YOLO offers higher detection accuracy and lower computational cost; e.g., mAP50 reaches 43.4%, but parameters are only 6.766 M.

A lightweight infrared target detection model, G-YOLO, based on an unmanned aerial vehicle (UAV) is proposed to address the issues of low accuracy in target detection of UAV aerial images in complex ground scenarios and large network models that are difficult to apply to mobile or embedded platforms. Firstly, the YOLOv8 backbone feature extraction network is improved and designed based on the lightweight network, GhostBottleneckV2, and the remaining part of the backbone network adopts the depth-separable convolution, DWConv, to replace part of the standard convolution, which effectively retains the detection effect of the model while greatly reducing the number of model parameters and calculations. Secondly, the neck structure is improved by the ODConv module, which adopts an adaptive convolutional structure to adaptively adjust the convolutional kernel size and step size, which allows for more effective feature extraction and detection based on targets at different scales. At the same time, the neck structure is further optimized using the attention mechanism, SEAttention, to improve the model’s ability to learn global information of input feature maps, which is then applied to each channel of each feature map to enhance the useful information in a specific channel and improve the model’s detection performance. Finally, the introduction of the SlideLoss loss function enables the model to calculate the differences between predicted and actual truth bounding boxes during the training process, and adjust the model parameters based on these differences to improve the accuracy and efficiency of object detection. The experimental results show that compared with YOLOv8n, the G-YOLO reduces the missed and false detection rates of infrared small target detection in complex backgrounds. The number of model parameters is reduced by 74.2%, the number of computational floats is reduced by 54.3%, the FPS is improved by 71, which improves the detection efficiency of the model, and the average accuracy (mAP) reaches 91.4%, which verifies the validity of the model for UAV-based infrared small target detection. Furthermore, the FPS of the model reaches 556, and it will be suitable for wider and more complex detection task such as small targets, long-distance targets, and other complex scenes.

In the past decade, as more and more passengers choose to fly on trips, China’s airport infrastructure construction has achieved world-renowned achievements. Despite the growing opportunities and demands for using brand research to assist airport industry services in improving, few studies have investigated the impact of service quality in terminal facilities on brand due to the diversity of service. This study uses structural equation models based on empirical research to explore the impact of facility service quality, including processing facility and non-processing facility, on airport brand experience and passenger satisfaction. This study also aims to assess the mediating effect of brand engagement on the relationship between facility service quality, brand experience, and passenger satisfaction. At the same time, this study also uses importance–performance map analysis (IPMA) to find specific items influencing brand engagement. The sampling method used a random sampling approach, with a total of 186 questionnaires distributed at Shanghai Pudong International Airport for data analysis. The results show that airport facility service quality is significant for brand engagement and experience, as well as for satisfaction, especially for processing facilities. In addition, the IPMA results show that facility services involved in the check-in process is of high importance, which requires more attention from managers. Overall, the findings of this study extend the understanding of service quality, brand engagement, brand experience, and passenger satisfaction in the context of an international airport, and they offer implications for Shanghai Pudong International Airport regarding the improvement of its facilities and brand.

Dexmedetomidine nasal sprays is effective for perioperative sedation, analgesia, and anxiolysis. Nevertheless, its impact on postoperative sleep quality along with the optimal dosage and overall efficacy remains unclear in patients undergoing laparoscopic gynecological surgery. A total of 150 adult patients undergoing laparoscopic gynecological surgery were enrolled, with 144 included in the final analysis. Patients in the dexmedetomidine (Dex) group received 50 µg of intranasal dexmedetomidine 30 minutes before sleep on the first postoperative night, while the control group received an equivalent volume of saline. Primary outcomes included objective sleep parameters (sleep duration, deep sleep duration, REM sleep duration and light sleep duration) measured via a portable sleep monitor, as well as subjective sleep parameters assessed by Athens Insomnia Scale (AIS) and Numerical Rating Scale (NRS) scores one night before and on the first night after surgery. Secondary outcomes comprised postoperative pain measured by VAS, nausea and vomiting, and average heart rate. The sleep duration, deep sleep duration and REM sleep duration of patients in the control group decreased after laparoscopic gynaecological surgery (vs preoperative control group, =0.021, <0.001, =0.005, respectively), whereas the fragmented sleep duration and NRS score increased (vs preoperative control group, =0.017, =0.032, respectively) . In contrast, those treated with dexmedetomidine (Dex group) exhibited significantly improved sleep quality postoperatively, with greater sleep duration, deep sleep duration, and REM sleep durations (vs control group, <0.001, <0.001, <0.001, respectively). There were no significant differences in postoperative pain or nausea and vomiting between the two groups. Dexmedetomidine nasal sprays improved postoperative sleep quality in patients who underwent laparoscopic gynaecological surgery. Chinese Clinical Trial Registry (clinical trial number: ChiCTR2400080181).