Search Results (67)

Cruciferous vegetables are popular in Asian dishes. However, striped flea beetles prefer to feed on leaves, which can damage the appearance of crops and reduce their economic value. Due to the lack of pest monitoring, the occurrence of pests is often irregular and unpredictable. Regular and quantitative spraying of pesticides for pest control is an alternative method. Nevertheless, this requires manual execution and is inefficient. This paper presents a system powered by solar energy, utilizing batteries and supercapacitors for energy storage to support the implementation of edge AI devices in outdoor environments. Raspberry Pi is utilized for artificial intelligence image recognition and the Internet of Things (IoT). YOLOv5 is implemented on the edge device, Raspberry Pi, for detecting striped flea beetles, and StyleGAN3 is also utilized for data augmentation in the proposed system. The recognition accuracy reaches 85.4%, and the results are transmitted to the server through a 4G network. The experimental results indicate that the system can operate effectively for an extended period. This system enhances sustainability and reliability and greatly improves the practicality of deploying smart pest detection technology in remote or resource-limited agricultural areas. In subsequent applications, drones can plan routes for pesticide spraying based on the distribution of pests. Full article

Patients with sepsis often receive mechanical ventilation (MV). Continued use of MV may increase overdistention in the lungs, inflammatory mediator production, and inflammatory cell recruitment, eventually causing ventilator-induced lung injury (VILI). Endoplasmic reticulum (ER) stress caused by MV, oxidative stress, and sepsis results in dissociation of GRP78 from transmembrane proteins (PERK, IRE1α, and ATF6) and generates abundant incorrect protein structures. Phosphoinositide 3-kinase-γ (PI3K-γ) has been demonstrated to modulate ER stress associated with sepsis and acute lung injury (ALI). However, the regulatory mechanisms by which ER stress is involved in VILI remain unclear. In this study, MV was hypothesized to augment lung injury and induce ER stress through the PI3K-γ pathway, regardless of endotoxemia. Wild-type or PI3K-γ-deficient C57BL/6 mice were exposed to 30 mL/kg tidal volume of MV with or without endotoxemia for 5 h. The control group comprised nonventilated mice. MV with endotoxemia increased microvascular permeability, lung edema, interleukin-6 and metalloproteinase-9 production, oxidative loads, ER stress biomarkers (GRP78, IRE-1α, PERK), morphological rearrangement, PI3K-γ expression, and bronchial epithelial apoptosis in rodent lungs. The increase in lung injury was substantially reduced in PI3K-γ-deficient mice and in mice administered 4-phenylbutyric acid. In conclusion, MV-augmented ALI after endotoxemia partially depends on the PI3K-γ pathway. Full article

Background/Objectives: This research presents a novel analytical method for breast tumor characterization and tissue classification by leveraging intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) combined with hyperspectral imaging techniques and deep learning. Traditionally, dynamic contrast-enhanced MRI (DCE-MRI) is employed for breast tumor diagnosis, but it involves gadolinium-based contrast agents, which carry potential health risks. IVIM imaging extends conventional diffusion-weighted imaging (DWI) by explicitly separating the signal decay into components representing true molecular diffusion ($D$) and microcirculation of capillary blood (pseudo-diffusion or $D*$). This separation allows for a more comprehensive, non-invasive assessment of tissue characteristics without the need for contrast agents, thereby offering a safer alternative for breast cancer diagnosis. The primary purpose of this study was to evaluate different methods for breast tumor characterization using IVIM-DWI data treated as hyperspectral image stacks. Dice similarity coefficients and Jaccard indices were specifically used to evaluate the spatial segmentation accuracy of tumor boundaries, confirmed by experienced physicians on dynamic contrast-enhanced MRI (DCE-MRI), emphasizing detailed tumor characterization rather than binary diagnosis of cancer. Methods: The data source for this study consisted of breast MRI scans obtained from 22 patients diagnosed with mass-type breast cancer, resulting in 22 distinct mass tumor cases analyzed. MR images were acquired using a 3T MRI system (Discovery MR750 3.0 Tesla, GE Healthcare, Chicago, IL, USA) with axial IVIM sequences and a bipolar pulsed gradient spin echo sequence. Multiple b-values ranging from 0 to 2500 s/mm² were utilized, specifically thirteen original b-values (0, 15, 30, 45, 60, 100, 200, 400, 600, 1000, 1500, 2000, and 2500 s/mm²), with the last four b-value images replicated once for a total of 17 bands used in the analysis. The methodology involved several steps: acquisition of multi-b-value IVIM-DWI images, image pre-processing, including correction for motion and intensity inhomogeneity, treating the multi-b-value data as hyperspectral image stacks, applying hyperspectral techniques like band expansion, and evaluating three tumor detection methods: kernel-based constrained energy minimization (KCEM), iterative KCEM (I-KCEM), and deep neural networks (DNNs). The comparisons were assessed by evaluating the similarity of the detection results from each method to ground truth tumor areas, which were manually drawn on DCE-MRI images and confirmed by experienced physicians. Similarity was quantitatively measured using the Dice similarity coefficient and the Jaccard index. Additionally, the performance of the detectors was evaluated using 3D-ROC analysis and its derived criteria (AUC_OD, AUC_TD, AUC_BS, AUC_TD−BS, AUC_ODP, AUC_SNPR). Results: The findings objectively demonstrated that the DNN method achieved superior performance in breast tumor detection compared to KCEM and I-KCEM. Specifically, the DNN yielded a Dice similarity coefficient of 86.56% and a Jaccard index of 76.30%, whereas KCEM achieved 78.49% (Dice) and 64.60% (Jaccard), and I-KCEM achieved 78.55% (Dice) and 61.37% (Jaccard). Evaluation using 3D-ROC analysis also indicated that the DNN was the best detector based on metrics like target detection rate and overall effectiveness. The DNN model further exhibited the capability to identify tumor heterogeneity, differentiating high- and low-cellularity regions. Quantitative parameters, including apparent diffusion coefficient ($ADC$), pure diffusion coefficient ($D$), pseudo-diffusion coefficient ($D*$), and perfusion fraction ($PF$), were calculated and analyzed, providing insights into the diffusion characteristics of different breast tissues. Analysis of signal intensity decay curves generated from these parameters further illustrated distinct diffusion patterns and confirmed that high cellularity tumor regions showed greater water molecule confinement compared to low cellularity regions. Conclusions: This study highlights the potential of combining IVIM-DWI, hyperspectral imaging techniques, and deep learning as a robust, safe, and effective non-invasive diagnostic tool for breast cancer, offering a valuable alternative to contrast-enhanced methods by providing detailed information about tissue microstructure and heterogeneity without the need for contrast agents. Full article

Governments are encouraging public transportation and bicycle-sharing systems to promote sustainable development and reduce greenhouse gas emissions. Despite the expansion of Taipei’s YouBike program, many stations frequently run out of bikes or docking spaces, and current redistribution strategies are suboptimal. This study proposes a novel approach to optimize YouBike allocation under resource constraints. We first used K-means clustering to group stations with similar rental profiles, reducing the number of models needed. A random forest model selected key crowd grid factors as input variables for a long short-term memory (LSTM) prediction model to accurately predict demand patterns, including during special events or weather changes. A genetic algorithm then determined optimal station configurations and provided return station recommendations, considering user destinations and station dock ratios, while minimizing manual redistribution. Simulations demonstrated that the proposed system meets user needs, enhances operational efficiency, and significantly reduces manual redistribution costs. Our methods have practical applicability for YouBike managers, indicating that user compliance with recommendations can offset the need for manual redistribution and support the current policy of recommending stations within 600 m of the user’s destination. Full article

Background: Traumatic brain injury (TBI) research often focuses on mortality rates or functional recovery, yet the critical need for long-term care among patients dependent on institutional or Respiratory Care Ward (RCW) support remains underexplored. This study aims to address this gap by employing machine learning techniques to develop and validate predictive models that analyze the prognosis of this patient population. Method: Retrospective data from electronic medical records at Chi Mei Medical Center, encompassing 2020 TBI patients admitted to the ICU between January 2016 and December 2021, were collected. A total of 44 features were included, utilizing four machine learning models and various feature combinations based on clinical significance and Spearman correlation coefficients. Predictive performance was evaluated using the area under the curve (AUC) of the receiver operating characteristic (ROC) curve and validated with the DeLong test and SHAP (SHapley Additive exPlanations) analysis. Result: Notably, 236 patients (11.68%) were transferred to long-term care centers. XGBoost with 27 features achieved the highest AUC (0.823), followed by Random Forest with 11 features (0.817), and LightGBM with 44 features (0.813). The DeLong test revealed no significant differences among the best predictive models under various feature combinations. SHAP analysis illustrated a similar distribution of feature importance for the top 11 features in XGBoost, with 27 features, and Random Forest with 11 features. Conclusions: Random Forest, with an 11-feature combination, provided clinically meaningful predictive capability, offering early insights into long-term care trends for TBI patients. This model supports proactive planning for institutional or RCW resources, addressing a critical yet often overlooked aspect of TBI care. Full article

Listeria monocytogenes causes listeriosis, a serious foodborne illness with a high mortality rate, especially in vulnerable populations. It accounts for 19% of foodborne deaths, with invasive cases having a mortality rate of up to 44%, leading to conditions like meningitis, bacteremia, and meningoencephalitis. However, the prognostic factors remain unclear. This study examines the hospital outcomes of invasive listeriosis and identifies risk factors for in-hospital and one-year mortality. We analyzed the electronic medical records of 118 hospitalized patients with non-perinatal, culture-proven invasive listeriosis collected over a 21-year period. The in-hospital mortality rate was 36.4%, with only 33.1% surviving one year and 22.0% surviving two years. The key findings indicate that a quick Sequential Organ Failure Assessment (qSOFA) score of ≥2 (OR 106.59, p < 0.001), respiratory failure (OR 7.58, p = 0.031), and shorter ampicillin duration (OR 0.53, p = 0.012) independently predicted poorer in-hospital outcomes. Additionally, a qSOFA score of ≥2 (OR 8.46, p < 0.001) and shorter ampicillin duration (OR 0.65, p < 0.001) were linked to higher one-year mortality. This study is the first to identify a qSOFA score of ≥2 as a significant marker for high-risk invasive listeriosis patients, with poorer outcomes linked to a qSOFA score of ≥2, respiratory failure, and shorter ampicillin use. Full article

Backgroud: This prospective randomized trial evaluated the clinical efficacy of inhaled colistin administered through two distinct nebulizer types, a vibrating mesh nebulizer (VMN) and a jet nebulizer (JN), in the treatment of ventilator-associated pneumonia caused by multidrug-resistant bacteria. In addition, an in vitro model was used to determine the optimal delivery of colistin. Method: Thirty-two patients prescribed intravenous (IV) colistin inhalation were randomized to receive either a VMN (n = 17) or a JN (n = 15), then compared to the control group (IV alone) over a 7-to 10-day period. The primary endpoint was the clinical pulmonary infection score (CPIS), and the secondary endpoints were the Sequential Organ Failure Assessment (SOFA) score, Acute Physiology and Chronic Health Evaluation (APACE) score, and duration of ventilator use. Results: Results from in vitro testing demonstrated that VMN delivered a significantly higher colistin dose than JN (35.68 ± 3.55% vs. 23.56 ± 3.31%; p < 0.001) when positioned at the humidifier inlet. Compared to the IV alone group, the IV with inhalation group yielded significant improvements in CPIS, SOFA score, and APACHE score on day 7; nevertheless, clinical outcomes between the two nebulizers were statistically indistinguishable. Conclusions: In conclusion, although VMN delivers a higher dose in vitro, both nebulizers yielded comparable clinical outcomes. This study was registered at US Clinical Trial Registration (NCT04633317). Full article

Background: The Ministry of Health and Welfare has reported oral cancer to be one of the most prevalent malignant cancers; it has the third highest incidence rate of all cancers and is the fifth leading cause of death among men in Taiwan. Lymph node metastasis in oral cancer usually has a low survival rate, with no significant improvement in the past 30 years. Therefore, a more effective survival predictor is warranted. Many cancer studies have revealed that monitoring tumor thickness and lymph node density, in addition to tumor, node, and metastasis (TNM) stages, can provide more accurate predictions. Methods: This retrospective study analyzed data from 612 patients with oral cancer who had the habit of chewing betel nuts. The study focused on tumor thickness, lymph node density, and the regional distribution of lymph node metastasis to determine their effectiveness as predictors. Results: The results revealed that a tumor thickness of 6 mm indicated cervical lymph node metastasis and was the optimal cutoff point for overall survival. The optimal cutoff value for lymph node density was 0.04. Patients with a tumor thickness of >6 mm and a lymph node density of >0.04 had significantly lower overall survival rates. Additionally, patients with >1 lymph node metastasis level and lower cervical metastasis exhibited a relatively worse prognosis. Conclusions: Therefore, in addition to TNM staging, tumor thickness, lymph node density, and metastasis level are suitable as parameters for predictors that can be used as references for adjuvant therapies for better therapeutic effects. Full article

BACKGROUND: Chronic hepatitis B and C infections are major causes of morbidity and mortality in end-stage kidney disease (ESKD) patients on hemodialysis (HD). The Fibrosis-4 (FIB-4) score is a non-invasive method to evaluate chronic liver disease. However, it is unclear whether there is a connection between the FIB-4 score and major adverse cardiovascular events (MACEs) and mortality in patients on HD. This study investigates the relationship between FIB-4 scores, MACEs, and mortality in HD patients. METHODS: A 5-year retrospective study included 198 HD patients with chronic hepatitis B and C from Chang Gung Memorial Hospital. FIB-4 scores were categorized into high (>2.071), middle (1.030~2.071), and low (<1.030) tertiles for cross-sectional analyses. MACEs and mortality were tracked longitudinally. RESULTS: Patients with high FIB-4 scores had lower hemoglobin and albumin levels. Cox multivariate analysis showed that high FIB-4 scores (aHR: 1.589) and diabetes mellitus (aHR: 5.688) were significant factors for all-cause mortality. The optimal FIB-4 score for 5-year mortality was 2.942. FIB-4 scores were not significant for predicting 5-year MACEs. CONCLUSIONS: High FIB-4 scores are associated with increased 5-year all-cause mortality risk in HD patients with chronic hepatitis virus infection. Full article

For patients on ventilation without acute respiratory distress syndrome (ARDS), there are, as yet, limited data on ventilation strategies. We hypothesized that driving pressure (DP) and mechanical power (MP) may play key roles for the late development of ARDS in patients without initial ARDS. A post hoc analysis of a database from our previous cohort was performed. The mean DP/MP was computed from the data before ARDS development or until ventilator support was discontinued within 28 days. The association between DP/MP and late development of ARDS within 28 days was determined. One hundred and twelve patients were enrolled, among whom seven developed ARDS. Univariate Cox regression showed that congestive heart failure (CHF) history and higher levels of mean MP and DP were associated with ARDS development. Multivariate models revealed that the mean MP and mean DP were still factors independently associated with ARDS development at hazard ratios of 1.177 and 1.226 after adjusting for the CHF effect. Areas under the receiver operating characteristic curves for mean DP/MP in predicting ARDS development were 0.813 and 0.759, respectively. In conclusion, high mean DP and MP values may be key factors associated with late ARDS development. The mean DP had a better predicted value for the development of ARDS than the mean MP. Full article

Predicting and improving the response of rectal cancer to second primary cancers (SPCs) remains an active and challenging field of clinical research. Identifying predictive risk factors for SPCs will help guide more personalized treatment strategies. In this study, we propose that experience data be used as evidence to support patient-oriented decision-making. The proposed model consists of two main components: a pipeline for extraction and classification and a clinical risk assessment. The study includes 4402 patient datasets, including 395 SPC patients, collected from three cancer registry databases at three medical centers; based on literature reviews and discussion with clinical experts, 10 predictive variables were considered risk factors for SPCs. The proposed extraction and classification pipelines that classified patients according to importance were age at diagnosis, chemotherapy, smoking behavior, combined stage group, and sex, as has been proven in previous studies. The C5 method had the highest predicted AUC (84.88%). In addition, the proposed model was associated with a classification pipeline that showed an acceptable testing accuracy of 80.85%, a recall of 79.97%, a specificity of 88.12%, a precision of 85.79%, and an F1 score of 79.88%. Our results indicate that chemotherapy is the most important prognostic risk factor for SPCs in rectal cancer survivors. Furthermore, our decision tree for clinical risk assessment illuminates the possibility of assessing the effectiveness of a combination of these risk factors. This proposed model may provide an essential evaluation and longitudinal change for personalized treatment of rectal cancer survivors in the future. Full article

Mechanical ventilation (MV), used in patients with acute lung injury (ALI), induces diaphragmatic myofiber atrophy and contractile inactivity, termed ventilator-induced diaphragm dysfunction. Phosphoinositide 3-kinase-γ (PI3K-γ) is crucial in modulating fibrogenesis during the reparative phase of ALI; however, the mechanisms regulating the interactions among MV, myofiber fibrosis, and PI3K-γ remain unclear. We hypothesized that MV with or without bleomycin treatment would increase diaphragm muscle fibrosis through the PI3K-γ pathway. Five days after receiving a single bolus of 0.075 units of bleomycin intratracheally, C57BL/6 mice were exposed to 6 or 10 mL/kg of MV for 8 h after receiving 5 mg/kg of AS605240 intraperitoneally. In wild-type mice, bleomycin exposure followed by MV 10 mL/kg prompted significant increases in disruptions of diaphragmatic myofibrillar organization, transforming growth factor-β1, oxidative loads, Masson’s trichrome staining, extracellular collagen levels, positive staining of α-smooth muscle actin, PI3K-γ expression, and myonuclear apoptosis (p < 0.05). Decreased diaphragm contractility and peroxisome proliferator-activated receptor-γ coactivator-1α levels were also observed (p < 0.05). MV-augmented bleomycin-induced diaphragm fibrosis and myonuclear apoptosis were attenuated in PI3K-γ-deficient mice and through AS605240-induced inhibition of PI3K-γ activity (p < 0.05). MV-augmented diaphragm fibrosis after bleomycin-induced ALI is partially mediated by PI3K-γ. Therapy targeting PI3K-γ may ameliorate MV-associated diaphragm fibrosis. Full article

In this study, we demonstrate breakdown voltage at 1500 V of GaN on a QST power device. The high breakdown voltage and low current collapse performance can be attributed to the higher quality of GaN buffer layers grown on QST substrates. This is primarily due to the matched coefficient of thermal expansion (CTE) with GaN and the enhanced mechanical strength. Based on computer-aided design (TCAD) simulations, the strong electric-field-induced trap-assisted thermionic field emissions (TA-TFEs) in the GaN on QST could be eliminated in the GaN buffer. This demonstration showed the potential of GaN on QST, and promises well-controlled performance and reliability under high-power operation conditions. Full article

The present study investigated the in vivo bone-forming efficacy of an innovative titanium (Ti) dental implant combined with a collagen sponge containing recombinant human bone morphogenetic protein-2 (BMP-2) in a pig model. Two different concentrations of BMP-2 (20 and 40 µg/mL) were incorporated into collagen sponges and placed at the bottom of Ti dental implants. The investigated implants were inserted into the edentulous ridge at the canine–premolar regions of Lanyu small-ear pigs, which were then euthanized at weeks 1, 2, 4, 8, and 12 post-implantation. Specimens containing the implants and surrounding bone tissue were collected for histological evaluation of their bone-to-implant contact (BIC) ratios and calculation of maximum torques using removal torque measurement. Analytical results showed that the control and BMP-2-loaded implants presented good implant stability and bone healing for all testing durations. After 1 week of healing, the BMP-2-loaded implants with a concentration of 20 µg/mL exhibited the highest BIC ratios, ranging from 58% to 76%, among all groups (p = 0.034). Additionally, they also possessed the highest removal torque values (50.1 ± 1.3 N-cm) throughout the 8-week healing period. The BMP-2-loaded implants not only displayed excellent in vivo biocompatibility but also presented superior osteoinductive performance. Therefore, these findings demonstrate that BMP-2 delivered through a collagen sponge can potentially enhance the early-stage osseointegration of Ti dental implants. Full article

Gynecologic tract melanoma is a malignant tumor with poor prognosis. Because of the low survival rate and the lack of a standard treatment protocol related to this condition, the investigation of the mechanisms underlying melanoma progression is crucial to achieve advancements in the relevant gynecological surgery and treatment. Mitochondrial transfer between adjacent cells in the tumor microenvironment regulates tumor progression. This study investigated the effects of endothelial mitochondria on the growth of melanoma cells and the activation of specific signal transduction pathways following mitochondrial transplantation. Mitochondria were isolated from endothelial cells (ECs) and transplanted into B16F10 melanoma cells, resulting in the upregulation of proteins associated with tumor growth. Furthermore, enhanced antioxidation and mitochondrial homeostasis mediated by the Sirt1-PGC-1α-Nrf2-HO-1 pathway were observed, along with the inhibition of apoptotic protein caspase-3. Finally, the transplantation of endothelial mitochondria into B16F10 cells promoted tumor growth and increased M2-type macrophages through Nrf2/HO-1-mediated pathways in a xenograft animal model. In summary, the introduction of exogenous mitochondria from ECs into melanoma cells promoted tumor growth, indicating the role of mitochondrial transfer by stromal cells in modulating a tumor’s phenotype. These results provide valuable insights into the role of mitochondrial transfer and provide potential targets for gynecological melanoma treatment. Full article