Effectiveness of fentanyl buccal soluble film in cancer patients with inadequate breakthrough pain control.

BACKGROUND: Clinical evidence for the rapidity and effectiveness of fentanyl buccal soluble film (FBSF) in reducing pain intensity of breakthrough cancer pain (BTcP) remains inadequate. This study aimed to evaluate the efficacy of FBSF proportional to the around-the-clock (ATC) opioid regimens in rapidly relieving the intensity of BTcP episodes by determining the percentage of patients requiring further dose titration. METHODS: The study procedure included a dose-finding period followed by a 14-day observation period. Pain intensity was recorded with a Numeric Rating Scale (NRS) at onset and 5, 10, 15, and 30 min after FBSF self-administration. Meaningful pain relief was defined as the final NRS score ≤ 3. Satisfaction survey was conducted for each patient after treatment using the Global Satisfaction Scale. RESULTS: A total of 63 BTcP episodes occurred in 30 cancer patients. Only one patient required rescue medication at first BTcP episode and then achieved meaningful pain relief after titrating FBSF by 200 µg. Most BTcP episodes relieved within 10 min. Of 63 BTcP episodes, 30 (47.6%), 46 (73.0%), and 53 (84.1%) relieved within 5, 10, and 15 min after FBSF administration. Only grade 1/2 adverse events were reported, including somnolence, malaise, and dizziness. Of the 63 BTcP episodes, 82.6% were rated as excellent/good satisfaction with FBSF. CONCLUSION: FBSF can be administrated "on demand" by cancer patients at the onset of BTcP, providing rapid analgesia by achieving meaningful pain relief within 10 min. TRIAL REGISTRATION: This study was retrospectively registered 24 December, 2021 at Clinicaltrial.gov (NCT05209906): https://clinicaltrials.gov/study/NCT05209906 .

Automatic Localization of Soybean Seedlings Based on Crop Signaling and Multi-View Imaging.

Soybean is grown worldwide for its high protein and oil content. Weeds compete fiercely for resources, which affects soybean yields. Because of the progressive enhancement of weed resistance to herbicides and the quickly increasing cost of manual weeding, mechanical weed control is becoming the preferred method of weed control. Mechanical weed control finds it difficult to remove intra-row weeds due to the lack of rapid and precise weed/soybean detection and location technology. Rhodamine B (Rh-B) is a systemic crop compound that can be absorbed by soybeans which fluoresces under a specific excitation light. The purpose of this study is to combine systemic crop compounds and computer vision technology for the identification and localization of soybeans in the field. The fluorescence distribution properties of systemic crop compounds in soybeans and their effects on plant growth were explored. The fluorescence was mainly concentrated in soybean cotyledons treated with Rh-B. After a comparison of soybean seedlings treated with nine groups of rhodamine B solutions at different concentrations ranging from 0 to 1440 ppm, the soybeans treated with 180 ppm Rh-B for 24 h received the recommended dosage, resulting in significant fluorescence that did not affect crop growth. Increasing the Rh-B solutions reduced crop biomass, while prolonged treatment times reduced seed germination. The fluorescence produced lasted for 20 days, ensuring a stable signal in the early stages of growth. Additionally, a precise inter-row soybean plant location system based on a fluorescence imaging system with a 96.7% identification accuracy, determined on 300 datasets, was proposed. This article further confirms the potential of crop signaling technology to assist machines in achieving crop identification and localization in the field.

APHS-YOLO: A Lightweight Model for Real-Time Detection and Classification of Stropharia Rugoso-Annulata.

The classification of Stropharia rugoso-annulata is currently reliant on manual sorting, which may be subject to bias. To improve the sorting efficiency, automated sorting equipment could be used instead. However, sorting naked mushrooms in real time remains a challenging task due to the difficulty of accurately identifying, locating and sorting large quantities of them simultaneously. Models must be deployable on resource-limited devices, making it challenging to achieve both a high accuracy and speed. This paper proposes the APHS-YOLO (YOLOv8n integrated with AKConv, CSPPC and HSFPN modules) model, which is lightweight and efficient, for identifying Stropharia rugoso-annulata of different grades and seasons. This study includes a complete dataset of runners of different grades in spring and autumn. To enhance feature extraction and maintain the recognition accuracy, the new multi-module APHS-YOLO uses HSFPNs (High-Level Screening Feature Pyramid Networks) as a thin-neck structure. It combines an improved lightweight PConv (Partial Convolution)-based convolutional module, CSPPC (Integration of Cross-Stage Partial Networks and Partial Convolution), with the Arbitrary Kernel Convolution (AKConv) module. Additionally, to compensate for the accuracy loss due to lightweighting, APHS-YOLO employs a knowledge refinement technique during training. Compared to the original model, the optimized APHS-YOLO model uses 57.8% less memory and 62.5% fewer computational resources. It has an FPS (frames per second) of over 100 and even achieves 0.1% better accuracy metrics than the original model. These research results provide a valuable reference for the development of automatic sorting equipment for forest farmers.

Restoring Treg/Th17 cell balance in ulcerative colitis through HRas silencing and MAPK pathway inhibition.

This study investigates HRas-dependent mechanisms in the disruption of regulatory T (Treg) cells and T helper 17 (Th17) cells balance in ulcerative colitis (UC). Comprehensive RNA sequencing and bioinformatics analyses revealed elevated HRas and MAPK pathway-related protein expression in UC samples. Using a murine UC model induced by dextran sulfate sodium (DSS), HRas silencing was found to promote Treg cell differentiation and suppress Th17 cell production, effectively restoring balance. Inactivation of the MAPK pathway played a pivotal role in this rebalancing effect. In vivo experiments further confirmed that HRas silencing mitigated colon tissue damage in DSS-induced mice, emphasizing its potential as a therapeutic strategy for UC.

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To accurately estimate the age of individual tree and to achieve full-cycle sustainable management of natural Larix gmelinii forest in Great Xing'an Mountains of northeastern China, we constructed individual tree age prediction model using stepwise regression and random forest algorithms based on 44 fixed plots data and 280 stan-dard tree cores obtained from the Pangu Forest Farm. We analyzed the influence of stand structure, site conditions, and competition index on the accuracy of model prediction. The model was evaluated by the coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE). The results showed that the random forest model had the highest prediction accuracy when number of decision trees was 1500 and number of node con-tention variables was 8. The random forest model had better accuracy and prediction ability than the stepwise regression model, with R2, RMSE and MAE of 0.5882, 9.9259 a, 8.1155 a. Diameter at breast height was the most important factor affecting age prediction (83.8%), followed by tree height (34.4%), elevation (17.9%), and basal area per hectare (17.5%). The random forest algorithm exhibited better adaptability and modeling effect on constructing a predictive model for individual tree age. This research contributed to improving the accuracy of growth and harvest estimation for L. gmelinii, and could provide a reference for other scientific studies related to tree age estimation in forests.

The predictive value of serum tumor markers for EGFR mutation in non-small cell lung cancer patients with non-stage IA.

Objective: The predictive value of serum tumor markers (STMs) in assessing epidermal growth factor receptor (EGFR) mutations among patients with non-small cell lung cancer (NSCLC), particularly those with non-stage IA, remains poorly understood. The objective of this study is to construct a predictive model comprising STMs and additional clinical characteristics, aiming to achieve precise prediction of EGFR mutations through noninvasive means. Materials and methods: We retrospectively collected 6711 NSCLC patients who underwent EGFR gene testing. Ultimately, 3221 stage IA patients and 1442 non-stage IA patients were analyzed to evaluate the potential predictive value of several clinical characteristics and STMs for EGFR mutations. Results: EGFR mutations were detected in 3866 patients (57.9 %) of all NSCLC patients. None of the STMs emerged as significant predictor for predicting EGFR mutations in stage IA patients. Patients with non-stage IA were divided into the study group (n = 1043) and validation group (n = 399). In the study group, univariate analysis revealed significant associations between EGFR mutations and the STMs (carcinoembryonic antigen (CEA), squamous cell carcinoma antigen (SCC), and cytokeratin-19 fragment (CYFRA21-1)). The nomogram incorporating CEA, CYFRA 21-1, pathology, gender, and smoking history for predicting EGFR mutations with non-stage IA was constructed using the results of multivariate analysis. The area under the curve (AUC = 0.780) and decision curve analysis demonstrated favorable predictive performance and clinical utility of nomogram. Additionally, the Random Forest model also demonstrated the highest average C-index of 0.793 among the eight machine learning algorithms, showcasing superior predictive efficiency. Conclusion: CYFRA21-1 and CEA have been identified as crucial factors for predicting EGFR mutations in non-stage IA NSCLC patients. The nomogram and 8 machine learning models that combined STMs with other clinical factors could effectively predict the probability of EGFR mutations.

Dietary long-chain fatty acids promote colitis by regulating palmitoylation of STAT3 through CD36-mediated endocytosis.

The Western diet, characterized by its high content of long-chain fatty acids (LCFAs), is widely recognized as a significant triggering factor for inflammatory bowel disease (IBD). While the link between a high-fat diet and colitis has been observed, the specific effects and mechanisms remain incompletely understood. Our study provides evidence that the diet rich in LCFAs can disrupt the integrity of the intestinal barrier and exacerbate experimental colitis in mice. Mechanistically, LCFAs upregulate the signal transducer and activator of transcription-3 (STAT3) pathway in the inflammatory model, and STAT3 knockout effectively counters the pro-inflammatory effects of LCFAs on colitis. Specifically, palmitic acid (PA), a representative LCFA, enters intestinal epithelial cells via the cluster of differentiation 36 (CD36) pathway and participates in the palmitoylation cycle of STAT3. Inhibiting this cycle using pharmacological inhibitors like 2-Bromopalmitate (2-BP) and ML349, as well as DHHC7 knockdown, has the ability to alleviate inflammation induced by PA. These findings highlight the significant role of dietary LCFAs, especially PA, in the development and progression of IBD. Diet adjustments and targeted modulation offer potential therapeutic strategies for managing this condition. Model of LCFAs involvement in the palmitoylation cycle of STAT3 upon internalization into cells. Following cellular uptake through CD36, LCFAs are converted to palmitoyl-CoA. In the presence of DHHC7, palmitoyl-CoA binds to STAT3 at the C108 site, forming palmitoylated STAT3. Palmitoylation further promotes phosphorylation at the Y705 site of STAT3. Subsequently, palmitoylated STAT3 undergoes depalmitoylation by APT2 and translocates to the nucleus to exert its biological functions.

YOLOV5-CBAM-C3TR: an optimized model based on transformer module and attention mechanism for apple leaf disease detection.

Apple trees face various challenges during cultivation. Apple leaves, as the key part of the apple tree for photosynthesis, occupy most of the area of the tree. Diseases of the leaves can hinder the healthy growth of trees and cause huge economic losses to fruit growers. The prerequisite for precise control of apple leaf diseases is the timely and accurate detection of different diseases on apple leaves. Traditional methods relying on manual detection have problems such as limited accuracy and slow speed. In this study, both the attention mechanism and the module containing the transformer encoder were innovatively introduced into YOLOV5, resulting in YOLOV5-CBAM-C3TR for apple leaf disease detection. The datasets used in this experiment were uniformly RGB images. To better evaluate the effectiveness of YOLOV5-CBAM-C3TR, the model was compared with different target detection models such as SSD, YOLOV3, YOLOV4, and YOLOV5. The results showed that YOLOV5-CBAM-C3TR achieved mAP@0.5, precision, and recall of 73.4%, 70.9%, and 69.5% for three apple leaf diseases including Alternaria blotch, Grey spot, and Rust. Compared with the original model YOLOV5, the mAP 0.5increased by 8.25% with a small change in the number of parameters. In addition, YOLOV5-CBAM-C3TR can achieve an average accuracy of 92.4% in detecting 208 randomly selected apple leaf disease samples. Notably, YOLOV5-CBAM-C3TR achieved 93.1% and 89.6% accuracy in detecting two very similar diseases including Alternaria Blotch and Grey Spot, respectively. The YOLOV5-CBAM-C3TR model proposed in this paper has been applied to the detection of apple leaf diseases for the first time, and also showed strong recognition ability in identifying similar diseases, which is expected to promote the further development of disease detection technology.

Immunogenicity and Therapeutic Efficacy of a Sendai-Virus-Vectored HSV-2 Vaccine in Mouse and Guinea Pig Models.

BACKGROUND: To date, there is no licensed vaccine for preventing herpes simplex virus type 2 (HSV-2). The current treatment to address the infection and prevent its transmission is not always satisfactory. METHODS: We constructed two recombinant vectors, one encoding HSV-2 glycoprotein D (gD, SeV-dF/HSV-2-gD) and one encoding HSV-2-infected cell protein 27 (ICP27, SeV-dF/HSV-2-ICP27), based on a replication-defective Sendai virus through reverse genetics, collectively comprising a combinatorial HSV-2 therapeutic vaccine candidate. The immunogenicity and proper immunization procedure for this vaccine were explored in a murine model. The therapeutic effect that helps prevent recurrent HSV-2 disease was evaluated in HSV-2-infected guinea pigs. RESULTS: Both a robust humoral immune response and a cellular immune response, characterized by the neutralizing antibody titer and the IFN-γ level, respectively, were elicited in BALB/c mice. A further study of cellular immunogenicity in mice revealed that T lymphocytes were successfully enhanced with the desirable secretion of several cytokines. In HSV-2-seropositive guinea pigs, vaccination could reduce the severity of HSV-2 in terms of recurrent lesions, duration of recurrent outbreak, and frequency of recurrence by 58.66%, 45.34%, and 45.09%, respectively, while viral shedding was also significantly inhibited in the vaccine-treated group compared to the group treated with phosphate-buffered saline. CONCLUSIONS: The replication-defective recombinant Sendai viruses conveying HSV-2-gD and ICP27 proteins showed great immunogenicity and potential for preventing recurrent HSV-2 disease.