Impression Scores Research Articles

Educational effectiveness of remote training with smart glasses for impression-taking.

To compare the educational outcomes of remote instruction (RI) in impression-taking using smart glasses with those of face-to-face instruction (FI) and paper-based self-learning (SL) and analyze the educational effects, aiming to develop a remote clinical training strategy. Participants were recruited from among the dental residents who were trained in the first-year clinical program at the university hospital in 2023. The participants were divided into three groups as the original skill level was equal, and the groups were assigned RI, FI, printed guidance, and SL. All the participants took impressions of the jaw models attached to the mannequin using alginate impression material. Next, assigned instructions were provided. Then again, the trainees took impressions of the jaw models. The pre- and postinstruction impressions of each participant were evaluated, and the change in the impression score was statistically analyzed. The pre- and postinstruction scores of the trainees in the RI and FI groups showed a significant increase (p<0.05), whereas no significant difference was observed in the score changes in the SL group. In the intergroup comparisons, the score changes of the RI and FI groups were greater than those of the SL group, although no significant difference was found between the score changes of the RI and FI groups (p<0.05). RI in impression-taking using smart glasses has a greater educational effectiveness than paper-based SL. It has also been suggested that RI can have educational efficacy similar to FI.

Molecular docking, ADME properties and synthesis of thiophene sulfonamide derivatives

This study investigates the drug-like properties of target molecules containing thiophene sulfonamide groups (7a–7s) using computational molecular docking techniques. The binding interactions of these derivatives were assessed using protein 2NSD (Enoyl acyl carrier protein reductase InhA, complexed with N-(4-methylbenzoyl)-4-benzylpiperidine, PDB DOI: 10.2210/pdb2NSD/pdb) as the receptor. Molecular docking results revealed notable docking scores for all compounds, ranging from −6 to −12 kcal/mol. Compounds 7e, 7i, and 7f, in particular, demonstrated impressive glide scores (>11 kcal/mol) and were selected for further analysis through molecular dynamics simulations, which provided deeper insights into their dynamic behavior and stability. The drug-like properties of these molecules were evaluated based on Lipinski’s Rule of Five and ADME (Absorption, Distribution, Metabolism, and Excretion) criteria and compared with known drugs. Additionally, we synthesized these target molecules (7a–7s) using Suzuki-Miyaura coupling with a nickel catalyst replacing palladium. The chemical structures of the synthesized compounds were confirmed through elemental analysis, LC-MS,1H-NMR, and 13C-NMR spectroscopy.

Assessing the ability of GPT-4o to visually recognize medications and provide patient education.

Various studies have investigated the ability of ChatGPT (OpenAI) to provide medication information; however, a new promising feature has now been added, which allows visual input and is yet to be evaluated. Here, we aimed to qualitatively assess its ability to visually recognize medications, through medication picture input, and provide patient education via written and visual output. The responses were evaluated by accuracy, precision and clarity using a 4-point Likert-like scale. In regards to handling visual input and providing written responses, GPT-4o was able to recognize all 20 tested medications from packaging pictures, even with blurring, retrieve their active ingredients, identify formulations and dosage forms and provide detailed, yet concise enough, patient education in an almost completely accurate, precise and clear manner with a score of 3.55 ± 0.605 (85%). In contrast, the visual output through GPT-4o generated images illustrating usage instructions contained many errors that would either hinder the effectiveness of the medication or cause direct harm to the patient with a poor score of 1.5 ± 0.577 (16.7%). In conclusion, GPT-4o is capable of identifying medications from pictures and exhibits contrasting patient education performance between written and visual output with very impressive and poor scores, respectively.

The effectiveness and safety of botulinum toxin injections for managing motor disorders of patients with Parkinson's disease: A meta-analysis of randomized controlled trials.

This study aimed to assess the effectiveness and safety of botulinum toxin (BTX) injections for managing motor disorders in patients with Parkinson's disease (PD). An electronic search was conducted based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Data from available randomized controlled trials (RCTs) assessing BTX injections for motor disorders in PD patients were extracted for meta-analysis. Ultimately, 215 patients from eight RCTs were enrolled. Pooled analyses indicated that BTX was more effective than placebo in improving tremor (standardized mean difference [SMD] = 0.96, 95% CI [0.34, 1.58], p < 0.01), whereas no notable differences were observed between BTX and placebo regarding freezing of gait (SMD = 0.66, 95% CI [-0.26, 1.58], p = 0.162), United Parkinson's Disease Rate Scale (UPDRS) III score (SMD = -0.20, 95% CI [-1.17, 0.76], p = 0.68) and clinical global impression (CGI) score (SMD = 0.84, 95% CI [-0.74, 2.42], p = 0.298). Adverse events related to BTX injections were comparable to placebo (OR = 1.74, 95% CI [0.59, 5.14], p = 0.32). The current evidence suggests that BTX is effective and safe in treating PD tremor but fails to provide therapeutic benefits for freezing of gait and motor functional scores in PD patients. Furthermore, the limited number of included studies and heterogeneity in BTX intervention protocols suggest more research is needed, with additional standardized RCTs, to better understand and optimize BTX injections for motor disorders in PD.

Efficacy and safety of estetrol (E4) 15 mg/drospirenone (DRSP) 3 mg combination in a cyclic regimen for the treatment of primary and secondary dysmenorrhea: A multicenter, placebo-controlled, double-blind, randomized study.

To evaluate the efficacy and safety of the estetrol (E4) 15 mg/drospirenone (DRSP) 3 mg combination in a cyclic regimen in Japanese women with primary and secondary dysmenorrhea. A 16-week, multi-center, randomized, double-blind, placebo-controlled, parallel-group study, followed by a 36-week, open-label, extension study. 162 Japanese women with primary and secondary dysmenorrhea. Participants were randomly allocated to either the E4/DRSP group or the placebo group. In the E4/DRSP group, participants orally received one tablet containing E4 15 mg and DRSP 3 mg daily for 24 days, followed by a placebo tablet for 4 days, constituting one cycle. The placebo group was given one placebo tablet daily for 28 days. After 16 weeks, participants in the placebo group were switched to receive E4/DRSP for 36 weeks. Absolute change in the most severe total dysmenorrhea score from baseline to the end of the 16-week double-blinded period. E4/DRSP reduced the most severe total dysmenorrhea score by 2.3 points from baseline at week 16. The between-group difference was significant (-1.4, two-sided 95% confidence interval: -1.8 to -1.0), showing superiority to placebo (p<0.001). The responder rate, the proportion of participants who achieved a ≥2.0-point reduction in the most severe total dysmenorrhea score from baseline, was 64.3% in the E4/DRSP group, significantly higher than in the placebo group, 28.4%, (p<0.05). In the E4/DRSP group, visual analog scale scores for pelvic pain and dysmenorrhea symptoms during the menstrual bleeding periods were decreased by 44.2 mm and 42.3 mm, respectively, from baseline at week 16, significantly more than in the placebo group (p<0.001). Objective gynecological examinations suggested amelioration of pelvic tenderness, uterine mobility, and cul-de sac induration in the E4/DRSP group. E4/DRSP improved the quality of life-related questionnaires (interference with daily activities and sleeping) and global impression scores. Intermenstrual bleeding was the primary treatment-emergent adverse event in the E4/DRSP group, similar to combined oral contraceptives. There were no cases of venous thromboembolism and less impact on hemostasis parameters in the E4/DRSP group. E4/DRSP is an effective treatment for dysmenorrhea, offering a safe, new treatment option with potentially reduced thromboembolic risk.

A novel multi-model feature generation technique for suicide detection

Automated expert systems (AES) analyzing depression-related content on social media have piqued the interest of researchers. Depression, often linked to suicide, requires early prediction for potential life-saving interventions. In the conventional approach, psychologists conduct patient interviews or administer questionnaires to assess depression levels. However, this traditional method is plagued by limitations. Patients might not feel comfortable disclosing their true feelings to psychologists, and counselors may struggle to accurately predict situations due to limited data. In this context, social media emerges as a potentially valuable resource. Given the widespread use of social media in daily life, individuals often express their nature and mental state through their online posts. AES can efficiently analyze vast amounts of social media content to predict depression levels in individuals at an early stage. This study contributes to this endeavor by proposing an innovative approach for predicting suicide risks using social media content and machine learning techniques. A novel multi-model feature generation technique is employed to enhance the performance of machine learning models. This technique involves the use of a feature extraction method known as term frequency-inverse document frequency (TF-IDF), combined with two machine learning models: logistic regression (LR) and support vector machine (SVM). The proposed technique calculates probabilities for each sample in the dataset, resulting in a new feature set referred to as the probability-based feature set (ProBFS). This ProBFS is compact yet highly correlated with the target classes in the dataset. The utilization of concise and correlated features yields significant outcomes. The SVM model achieves an impressive accuracy score of 0.96 using ProBFS while maintaining a low computational time of 5.63 seconds even when dealing with extensive datasets. Furthermore, a comparison with state-of-the-art approaches is conducted to demonstrate the significance of the proposed method.

Discover the Fascinating Insights: Public School Students in Jordan Embrace Distance Education

This study aimed to address the question, "How effective is the use of distance education?" by exploring it from the perspective of public-school pupils. This descriptive survey study aimed to quantify the impact of gender differences in viewpoints throughout the first half of 2023. The study's 300 participants were chosen randomly from male and female pupils enrolled in public schools under the Wadi el-seer Directorate of Education. In all, 150 males and 150 females made up the sample. Findings were encouraging; an impressive mean score of 4.78 was attained by the top-rated statement, "Through the use of distance learning, my academic experience has been greatly enhanced," highlighting the favorable influence of remote learning on academic experiences. The statements "Participating in classes remotely has increased my interest and motivation" (4.67) and "Distance education facilitates discussions and dialogues about educational problems" (4.59) illustrate its capacity to enhance motivation and encourage meaningful discussions. While statements like "Education grew in popularity and piqued my curiosity" (3.60) and "I believe that distant education sufficiently equips me to face professional problems" (3.60) indicate areas that need improvement, the overall domain score of 4.30 confirms a generally positive perception of distance education.

Elemental diffusion coefficient prediction in conventional alloys using machine learning

This paper presents the Machine Learned Diffusion Coefficient Estimator, a comprehensive machine learning framework designed to predict diffusion coefficients in impure metallic (IM) and multi-component alloy (MCA) media. The framework incorporates five machine learning models, each tailored to specific diffusion modes: (1) impurity and (2) self-diffusion in IM media, and (3) self, (4) impurity, and (5) chemical diffusion in MCA media. These models use statistical aggregations of atomic descriptors for both the diffusing elements and the diffusion media, along with the temperature of the diffusion process, as features. Models are trained using the random forest and deep neural network algorithms, with performance evaluated through the coefficient of determination (R2), mean squared error (MSE), and uncertainty estimates. The models within this framework achieve an impressive R2 score above 0.90 with MSE less than 10−16 m2/s, demonstrating high predictive accuracy and reliability for diffusion coefficient.

Features of Swallowing Function in Sporadic Inclusion Body Myositis: Preliminary Evidence Using Well-Tested Assessment Frameworks.

Evidence surrounding swallowing impairment in sporadic inclusion body myositis (IBM) is based on nonstandardized and nonvalidated assessment methods. We investigated (a) IBM's impact on swallowing function and oral intake status using well-tested assessment frameworks; (b) changes in swallowing over time; and (c) age, sex, and swallowing impairment severity's influence on oral intake status. We conducted a secondary analysis of Modified Barium Swallow Impairment Profile (MBSImP) and Functional Oral Intake Scale (FOIS) data from 13 patients with IBM (seven females; Mage = 60.2 [±13.6] years) and 13 age- and sex-matched healthy controls. We compared MBSImP Overall Impression (OI), Oral Total (OT), Pharyngeal Total (PT), and FOIS scores between groups. Specific to the IBM cohort, we analyzed repeated OT and PT scores and calculated whether age, sex, and OT and PT scores predicted FOIS scores. The IBM cohort demonstrated poorer OI scores across six swallowing components than healthy controls (each p < .05). Unlike OT scores (p = .84), PT (p = .033) and FOIS (p < .001) scores were worse in the IBM cohort. Repeated OI scores revealed changes in three swallowing components (each p < .05), but repeated OT (p = .16) and PT (p = .30) scores did not significantly change. Age, sex, and OT and PT scores did not influence FOIS scores (each p > .05). Pharyngeal impairments were most prominent in the IBM cohort, and their oral intake status was adversely affected. Our preliminary data showcase the application of robust assessment methods to investigate swallowing function in IBM, enhancing standardization and comparability across studies. https://doi.org/10.23641/asha.27165450.

Help-seeking from traditional healers in patients with severe mental illness and its relationship with internalized stigma.

Help-seeking from traditional healers (TH) is common in patients with severe mental illness. However, the differences between patients with schizophrenia and bipolar disorder are not well-known. Although internalized stigma is also common in patients with severe mental illness, its impact on help-seeking from TH is not studied. To investigate help-seeking from TH and the relationship between help-seeking from TH and internalized stigma in patients with schizophrenia and bipolar disorder. In this cross-sectional study, we collected information about help-seeking from TH and clinical characteristics by using a semi-structured interview form from 310 patients with schizophrenia and bipolar disorder in two sites with different socio-cultural backgrounds. We measured internalized stigma by using The Internalized Stigma of Mental Illness (ISMI) scale. We found that 47% of the patients visited TH in any phase of their illness, and 46% of them sought help from TH before their first contact with a psychiatrist. Those who grew up in rural areas, were less educated, who attempted suicide before, with resistance to treatment, and with a family member who also admitted to TH were more frequent among the help-seekers from TH. This group also had more hospitalizations and higher Clinical Global Impression scores. Internalized stigma was found to be higher in the schizophrenia group, and it was related to help-seeking from TH and delay in admission to psychiatric facilities. Our findings suggest that help-seeking from TH is common both in patients with schizophrenia and bipolar disorder, and it has socio-cultural, illness-related, and stigma-related predictors.

B-109 Six Sigma Analysis for Enhancing Biochemistry Internal Quality Control in a Private Laboratory in India

Abstract Background This abstract outlines a comprehensive study on the application of Six Sigma methodologies to optimize internal quality control processes in a private biochemistry laboratory in India. Data collection was performed using Bio-Rad Assayed Chemistry Control on the Roche cobas 6000 instrument, facilitated by Bio-Rad Unity software. The study adheres to Clinical Laboratory Improvement Amendments (CLIA) 2024 guidelines, specifically focusing on Total Error (TEa). Methods The analytical performances of Cobas c501 insturment as analyse by running biochemical tests (urea, creatinine, uric acid, total bilirubin, AST, Amylase, Lipase, CK, LD, ALT, ALP, LDH, HDL-C, Ca2+, cholesterol, TG, HDL LDL, total protein, albumin, Na+, K+, and Cl−) and glucose on Cobas 400 were evaluated. Biases and CV were obtained from internal quality control programs using biorad unity software. The performance of each instrument was assessed using the σ scale; the benchmark performance was &amp;lt;3 σ (improvement needed); “world-class” quality was six sigma. Results Analytes such as “Alk PO4,” “ALT,” “Amylase,” “Bilirubin,” “CK,” “GGT,” “LD,” “Phosphorus,” “HDL,” “LDL Cholesterol,” “Triglycerides,” and “Uric acid” exhibited impressive sigma scores exceeding 6. Meanwhile, analytes including “Calcium,” “Total Cholesterol,” “Potassium,” “Total Protein,” “Urea,” and “Glucose” achieved sigma scores between 4 and 6, demonstrating noteworthy performance within acceptable bounds. Conclusions This study contributes valuable insights to laboratory management, specifically in the context of biochemistry testing in private Indian laboratories. The adoption of Six Sigma principles, coupled with adherence to CLIA 2024 guidelines and the utilization of advanced instrumentation and software, ensures compliance with regulatory requirements and fosters a culture of continuous improvement. This positions the laboratory to deliver high-quality and reliable results in the dynamic landscape of healthcare diagnostics.

A clinical prediction model for safe early discharge of patients with an infection at the emergency department

BackgroundEvery hospital admission is associated with healthcare costs and a risk of adverse events. The need to identify patients who do not require hospitalization has emerged with the profound increase in hospitalization rates due to infectious diseases during the last decades, especially during the COVID-19 pandemic. This study aimed to identify predictors of safe early discharge (SED) in patients presenting to the emergency department (ED) with a suspected infection meeting the Systemic Inflammatory Response Syndrome (SIRS) criteria. MethodsWe conducted a prospective cohort study on adult non-trauma patients with a suspected infection and at least two SIRS criteria. We defined SED as hospital discharge within 24 h (e.g. direct ED discharge or rapid ward discharge) without disease-related readmission to our hospital or death during the first seven days. A prediction model for SED was developed using multivariate logistic regression analysis and tested with k-fold cross-validation. ResultsWe included 1381 patients, of whom 1027 (74.4 %) were hospitalized for longer than 24 h or re-admitted within seven days and 354 (25.6 %) met SED criteria. Parameters associated with SED were relatively young age, absence of comorbidities, living independently, yellow or green triage urgency, lack of ambulance transport or general practitioner referral, normal clinical impression scores, and risk scores (i.e., qSOFA, PIRO, MEDS, NEWS, and SIRS), normal vital sign measurements and absence of kidney and respiratory failure. The model performance metrics showed an area under the curve of 0.824. The validation showed a minimal drop in performance and indicated a good fit. ConclusionWe developed and validated a model to identify patients with an infection at the ED who can be safely discharged early.

Transliterating Javanese Script Images to Roman Script using Convolutional Neural Network with Transfer Learning

The Javanese script holds immense cultural significance within Indonesia despite its diminishing usage in contemporary contexts. Its presence remains notable in specific regions of Java and remains integral to many historical documents and texts. Consequently, there is an urgent need for a transliteration system adept at converting Javanese script into contemporary scripts like Roman or Indonesian, thereby contributing to preserving Java's linguistic and cultural legacy. However, reading or transliterating Javanese script can be time-consuming, especially for longer texts, presenting considerable challenges for non-native readers. This study aims to develop an effective transliteration system for converting Javanese script into Roman script. This system addresses the pressing need to preserve Java's linguistic and cultural heritage by facilitating the readability and accessibility of Javanese script, especially for non-native readers. This study introduces an Optical Character Recognition (OCR) system tailored to identify Javanese script characters and transcribe them into Roman characters, explicitly focusing on fundamental nglegena and sandhangan swara characters. Individual characters are isolated by leveraging horizontal and vertical projection techniques, facilitating subsequent classification using a Convolutional Neural Network (CNN) employing transfer learning methodologies. The system's achievement of an impressive average similarity score of 90.78% is noteworthy, with the Xception architecture demonstrating superior efficiency in transliteration tasks. Implementing such a system harbors significant promise in safeguarding the Javanese script and enhancing its accessibility to a broader audience. This research contributes substantially to preserving and propagating Indonesia's rich cultural and linguistic heritage amidst the digital age.

Detection of breast cancer in digital breast tomosynthesis with vision transformers.

Digital Breast Tomosynthesis (DBT) has revolutionized more traditional breast imaging through its three-dimensional (3D) visualization capability that significantly enhances lesion discernibility, reduces tissue overlap, and improves diagnostic precision as compared to conventional two-dimensional (2D) mammography. In this study, we propose an advanced Computer-Aided Detection (CAD) system that harnesses the power of vision transformers to augment DBT's diagnostic efficiency. This scheme uses a neural network to glean attributes from the 2D slices of DBT followed by post-processing that considers features from neighboring slices to categorize the entire 3D scan. By leveraging a transfer learning technique, we trained and validated our CAD framework on a unique dataset consisting of 3,831 DBT scans and subsequently tested it on 685 scans. Of the architectures tested, the Swin Transformer outperformed the ResNet101 and vanilla Vision Transformer. It achieved an impressive AUC score of 0.934 ± 0.026 at a resolution of 384 384. Increasing the image resolution from 224 to 384 not only maintained vital image attributes but also led to a marked improvement in performance (p-value = 0.0003). The Mean Teacher algorithm, a semi-supervised method using both labeled and unlabeled DBT slices, showed no significant improvement over the supervised approach. Comprehensive analyses across different lesion types, sizes, and patient ages revealed consistent performance. The integration of attention mechanisms yielded a visual narrative of the model's decision-making process that highlighted the prioritized regions during assessments. These findings should significantly propel the methodologies employed in DBT image analysis by setting a new benchmark for breast cancer diagnostic precision.

Effectiveness of Medhasagar Rasa® in Combating Aging-Associated Mild Neurocognitive Disorder: An Open-Label, Exploratory, Interventional Clinical Trial.

With the rising prevalence of neurocognitive disorders (NCDs) among the aging population, particularly in conditions like mild cognitive impairment (MCI), which often precedes dementia, there remains a significant gap in effective pharmacological interventions. This has generated interest in exploring alternative therapies to manage symptoms and enhance cognitive function in the aging population. The primary objective of this study was to evaluate the effect of Medhasagar Rasa® on cognitive functions, daily functioning, and quality of life in participants with aging-associated mild neurocognitive disorder using the Montreal Cognitive Assessment (MoCA) Scale, Ayurvedic Manasabhava Scale, and Brief Cognitive Rating Scale (BCRS). This open-label, interventional study at Karnatak Lingayat Education (KLE) Ayurveda Hospital, Belagavi, Karnataka, involved 32 screened participants, with 30 completing the study. Participants aged 50-70 years with MoCA scores of 18-25 received Medhasagar Rasa (2 tablets at bedtime, provided by M/s. Shree Dhootapapeshwar Limited, Mumbai, India) for 60 days. Assessments occurred at baseline and every 15 days until day 60. Thirty participants were recruited for the study after screening, all of whom completed the study. The median total MoCA score at baseline (visit one) was 20, which significantly improved to 25 by visit five (day 60±3) (p<0.001), indicating enhanced cognitive performance. The BCRS scores also showed significant improvement, with the median score decreasing from 12 to 7.5 (p<0.001) over 60 days. Anxiety symptoms were significantly reduced, with Hamilton Anxiety Rating Scale (HAM-A) scores dropping from 14 to 7 (p<0.001), while the Pittsburgh Sleep Quality Index (PSQI) scores indicated improved sleep quality, reducing from 9.5 to 7 (p<0.001). The Ayurvedic Manasabhava Scale also demonstrated a significant reduction in intensity (14 to 6; p<0.001) and frequency (13.5 to 6; p<0.001). Clinical Global Impression (CGI) scores showed stable illness severity, sustained global improvement, and consistent therapeutic efficacy. No adverse events were reported, and vital parameters remained normal throughout the study. Compliance with the medication was over 80%, and no significant changes were observed in laboratory values. Medhasagar Rasa effectively enhanced cognitive functions and alleviated anxiety and sleep disturbances in aging-related mild neurocognitive disorder, offering a promising therapeutic option.

B - 52 A Pilot Study for the Responsible Concussion Commentary Checklist: Evaluating Commentary of Head Injury Assessment in the Australian National Rugby League

Abstract Objective Focus on sport-related concussion has increased due to growing concerns regarding the long-term effects of repeated concussion. To improve player safety, some sports have adopted new injury assessment and management protocols. These efforts may be undercut by the live media coverage of concussion, which is known to downplay concussion. This pilot study explored how live sports commentary from the Australian National Rugby League portrays concussion in the context of the newly introduced Head Injury Assessment (HIA) protocol. Method 15 concussions were randomly selected from the 69 HIA-tiggering events from rounds 1 to 8 of the 2024 season. The live commentary for those events was evaluated using a concussion commentary checklist. This multi-item checklist was developed from existing recommendations for the responsible reporting of concussion. The checklist evaluated the extent to which the commentary met the gold standard requirements (e.g., used the correct injury terminology) and yielded an overall impression score (commentary quality). Results Impression ratings revealed that the commentary was rarely scored as high quality, indicating that it did not meet the gold standard expected for accurate and responsible commentary of on field instances of suspected concussion. The scores for the individual checklist items were also frequently low. Commonly identified issues included the use of incorrect injury terminology and injury minimisation. Conclusion This pilot study showed that the live commentary of suspected concussion was poor. The findings have implications for concussion awareness through changed practices in live sports commentary. Commentary guidance is needed to prevent the spread of misinformation about concussion among sport spectators.

Fibroblast Growth Factor 2 (FGF2) Activates Vascular Endothelial Growth Factor (VEGF) Signaling in Gastrointestinal Stromal Tumors (GIST): An Autocrine Mechanism Contributing to Imatinib Mesylate (IM) Resistance.

We showed previously that the autocrine activation of the FGFR-mediated pathway in GIST lacking secondary KIT mutations was a result of the inhibition of KIT signaling. We show here that the FGF2/FGFR pathway regulates VEGF-A/VEGFR signaling in IM-resistant GIST cells. Indeed, recombinant FGF2 increased the production of VEGF-A by IM-naive and resistant GIST cells. VEGF-A production was also increased in KIT-inhibited GIST, whereas the neutralization of FGF2 by anti-FGF2 mAb attenuated VEGFR signaling. Of note, BGJ 398, pan FGFR inhibitor, effectively and time-dependently inhibited VEGFR signaling in IM-resistant GIST T-1R cells, thereby revealing the regulatory role of the FGFR pathway in VEGFR signaling for this particular GIST cell line. This also resulted in significant synergy between BGJ 398 and VEGFR inhibitors (i.e., sunitinib and regorafenib) by enhancing their pro-apoptotic and anti-proliferative activities. The high potency of the combined use of VEGFR and FGFR inhibitors in IM-resistant GISTs was revealed by the impressive synergy scores observed for regorafenib or sunitinib and BGJ 398. Moreover, FGFR1/2 and VEGFR1/2 were co-localized in IM-resistant GIST T-1R cells, and the direct interaction between the aforementioned RTKs was confirmed by co-immunoprecipitation. In contrast, IM-resistant GIST 430 cells expressed lower basal levels of FGF2 and VEGF-A. Despite the increased expression VEGFR1 and FGFR1/2 in GIST 430 cells, these RTKs were not co-localized and co-immunoprecipitated. Moreover, no synergy between FGFR and VEGFR inhibitors was observed for the IM-resistant GIST 430 cell line. Collectively, the dual targeting of FGFR and VEGFR pathways in IM-resistant GISTs is not limited to the synergistic anti-angiogenic treatment effects. The dual inhibition of FGFR and VEGFR pathways in IM-resistant GISTs potentiates the proapoptotic and anti-proliferative activities of the corresponding RTKi. Mechanistically, the FGF2-induced activation of the FGFR pathway turns on VEGFR signaling via the overproduction of VEGF-A, induces the interaction between FGFR1/2 and VEGFR1, and thereby renders cancer cells highly sensitive to the dual inhibition of the aforementioned RTKs. Thus, our data uncovers the novel mechanism of the cross-talk between the aforementioned RTKs in IM-resistant GISTs lacking secondary KIT mutations and suggests that the dual blockade of FGFR and VEGFR signaling might be an effective treatment strategy for patients with GIST-acquired IM resistance via KIT-independent mechanisms.

Improved Architecture and Training Strategies of YOLOv7 for Remote Sensing Image Object Detection

The technology for object detection in remote sensing images finds extensive applications in production and people’s lives, and improving the accuracy of image detection is a pressing need. With that goal, this paper proposes a range of improvements, rooted in the widely used YOLOv7 algorithm, after analyzing the requirements and difficulties in the detection of remote sensing images. Specifically, we strategically remove some standard convolution and pooling modules from the bottom of the network, adopting stride-free convolution to minimize the loss of information for small objects in the transmission. Simultaneously, we introduce a new, more efficient attention mechanism module for feature extraction, significantly enhancing the network’s semantic extraction capabilities. Furthermore, by adding multiple cross-layer connections in the network, we more effectively utilize the feature information of each layer in the backbone network, thereby enhancing the network’s overall feature extraction capability. During the training phase, we introduce an auxiliary network to intensify the training of the underlying network and adopt a new activation function and a more efficient loss function to ensure more effective gradient feedback, thereby elevating the network performance. In the experimental results, our improved network achieves impressive mAP scores of 91.2% and 80.8% on the DIOR and DOTA version 1.0 remote sensing datasets, respectively. These represent notable improvements of 4.5% and 7.0% over the original YOLOv7 network, significantly enhancing the efficiency of detecting small objects in particular.

Optimized Tiny Machine Learning and Explainable AI for Trustable and Energy-Efficient Fog-Enabled Healthcare Decision Support System

The Internet of things (IoT)-based healthcare decision support system plays a crucial role in modern medicine, especially with the rise in chronic illnesses and an aging population necessitating continuous remote health monitoring. Current healthcare decision support systems struggle to deliver timely and accurate decisions with minimal latency due to limited real-time healthcare data and inefficient computational resources. There is a critical need for an optimized, energy-efficient machine learning model that reliably supports remote health monitoring within IoT and fog computing environments. Our study proposes an Optimized Tiny Machine Learning (TinyML) and Explainable AI (XAI) binary classification model for a trustable and energy-efficient healthcare decision support system, leveraging fog computing to optimize performance. The fog-based approach improves response times and enhances bandwidth usage, addressing critical needs such as reduced latency, higher bandwidth utilization, and decreased packet loss. To further improve efficiency, we incorporate the innovative mLZW data compression technique, significantly enhancing data communication efficiency and reducing response time to critical health alerts. However, limited real-time healthcare data records challenge machine learning classification performance. By implementing a TinyML algorithm, our system demonstrates superior performance to other machine learning models. The proposed optimized TinyML model achieves an impressive F1 score of 0.93 for health abnormalities detection, emphasizing its robustness and effectiveness. This paper highlights the potential of TinyML and XAI in delivering robust, trustworthy, and energy-aware healthcare solutions, making significant contributions toward effective remote health monitoring and decision support in fog-enabled IoT networks.Graphical abstract

Effect of fluid flow behavior on sustainability and exergy efficiency of solar heat collectors having multiple V‐ribs with gaps

AbstractIn the current numerical study, the viability and sustainability of a solar heat collector (SHC) have been rigorously analyzed through the lens of exergy analysis. The SHC with roughness in the form of symmetrical V‐rib gaps has been selected for detailed numerical investigation. The study focuses on deriving the dominant parameters affecting the performance of the SHC, including the relative gap width (), number of gaps () in the limbs of ribs, and fluid flow Reynolds number, which varied from 4000 to 18,000. The absorber plate with roughness was thoroughly examined to evaluate the losses and irreversibility that persist in the thermal system. The results of the numerical analysis revealed the maximum exergy efficiency to be 3.87%. Furthermore, the study examined the long‐term feasibility of the system, evaluating its sustainability index at a remarkable 1.0319 value, while the magnitude of the waste–energy ratio fetched an impressive score of 0.991. Additionally, the study found that the system has a significant improvement potential of 10.04. Overall, these findings provide critical insights into the performance and sustainability of SHCs, paving the way for future research and development in this crucial field.