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Deep fusion of incomplete multi-omic data for molecular mechanism of Alzheimer’s disease
Adaptive deep SVM for detecting early heart disease among cardiac patients
Abstract Heart attack is one of the most common heart diseases, which causes more deaths worldwide. Early detection and continuous monitoring are essential in reducing the death rate caused by heart diseases. Machine learning gives a promising solution for early and accurate heart disease detection by analyzing the data from healthcare devices. Although existing studies have employed various machine learning techniques to detect heart disease, most of the techniques still face challenges in handling large healthcare datasets that affect the prediction outcomes. To solve this issue, the research work focuses on developing a novel framework for detecting heart disease in its early stages by using machine learning techniques. In the initial phase, the significant data required for the validation is collected from benchmark resources, and it is subjected to the weighted optimal features selection phase. Here, from the input data, the features are selected optimally and their weights are tuned using Enhanced Arbitrary Variable-based Ship Rescue Optimization (EAVSRO). Further, the optimally selected weighted features are fed into the detection phase. In this phase, an Adaptive Deep Support Vector Machine (AD-SVM) is employed to detect heart diseases. Once heart disease is detected, the Atrial Fibrillation (AF) rate is determined using the Adaptive Multiscale Convolution Capsule Network (AMCCNet). Finally, the AF rate is obtained from the developed AMCCNet, and its parameters are tuned using the same EAVSRO. Later, various experiments are performed in the recommended heart disease detection model over existing models to verify its effectiveness. The accuracy of the designed framework is 96.07%, which is enhanced than the other existing frameworks like CNN-LSTM, DCNN, Adaboost and SVM, respectively. Thus, the results proved that the developed model can effectively detect heart disease at the early stages and identify the AF rate, providing timely treatments.
Periodate oxidation of tragacanth gum and evaluation of physicochemical and biological properties of oxidized tragacanth gum
Proteomic pathways across the ejection fraction spectrum in patients with heart failure and diabetes mellitus: an EXSCEL trial substudy
Association between opioid use and survival in advanced non small cell lung cancer patients treated with immune checkpoint inhibitors
Abstract Cancer-related pain is a frequent challenge among non-small cell lung (NSCLC) cancer patients, particularly for those with advanced disease and/or bone metastases. Opioids are the mainstay of treatment for moderate to severe cancer-related pain. However, emerging lines of evidence suggest that concomitant opioid use may be associated with poor survival outcomes in advanced NSCLC patients treated with immune checkpoint inhibitors (ICIs). We analyzed the impact of concomitant opioid use on survival outcomes of advanced NSCLC patients treated with ICIs. Correlations between baseline clinical-pathological characteristics and survival outcomes were assessed using log-rank tests while multivariate survival analyses were performed using the Cox proportional hazards model. Among patients treated with ICI as monotherapy and those treated with ICI as second or subsequent lines of treatment, concomitant opioid use was correlated with decreased progression-free survival (PFS) (p = 0.0460 and p = 0.0490) and overall survival (OS) (p = 0.0380 and p = 0.0230) in univariate analyses. However, in multivariate analyses, concomitant opioid use was not independently correlated with survival outcomes. Instead, ECOG PS ≥ 2 and bone metastases emerged as strong predictors of decreased PFS and OS. Despite limitations, our findings highlight that concomitant opioid use does not independently correlate with poor survival outcomes in this setting of patients.
Deep learning for retinal non-perfusion and foveal avascular zone analysis in wide-field OCTA in diabetic retinopathy
Comparing quality of life methadone and buprenorphine for opioid substitution treatment in Iran
Analysis of collaborative robot technology patent map and research on development trends
Abstract This study conducts a systematic analysis of global patents in the field of collaborative robot technology based on applications and type of industry. The data analyzed derives from the PatSnap database covering 30,425 patents from 2006 to 2025, using explicit keywords such as “collaborative robot” to ensure specificity, with procedural filters and manual curation applied to maximize relevance and accuracy. The research focuses on patent application trends, technological hotspots, geographical distribution, as well as applicant and inventor analyses, providing a comprehensive overview of the innovation landscape and development trends in this domain. The findings indicate that collaborative robot technology has undergone three distinct phases: the initial emergence period, a phase of rapid growth, and a subsequent slowdown, and it is now transitioning into a stage of mature development. This apparent recent slowdown is significantly influenced by the standard 18-month lag in patent publication, meaning current data for the latest years is incomplete. Invention patents dominate the field, reflecting a high level of technological innovation. From a geographical perspective, the United States and China serve as the primary global innovation hubs, with their patent application volumes significantly surpassing those of other countries. In terms of applicants, multinational corporations such as Qualcomm, Intel, BRIGHT DATA LTD, and NVIDIA lead in patent filings, while emerging enterprises have demonstrated notable growth in patent applications, injecting new vitality into the industry. Furthermore, through patent citation analysis and market value assessment, this study identifies high-value patents and key technological areas, offering valuable insights for future technological advancements and market strategies. Based on these findings, the study proposes recommendations to further promote the sustainable development of collaborative robot technology, including strengthening technological innovation, optimizing market positioning, fostering emerging enterprises and talent, enhancing international cooperation, and aligning with policy directions.
A 2D inflammatory co-culture model for investigating synovial fibroblast and macrophage interactions in rheumatoid arthritis
Evaluation using artificial intelligence shows post pandemic differences in oral reading fluency between Brazilian public and private school students
Abstract The investigation into differences in academic achievement between private and public school students has long been a focal point. The aim of this study was to compare oral reading fluency between private and public schools in the post-pandemic period. A total of 1296 participants were recruited from various Brazilian cities, spanning from the 2nd to the 5th grade of elementary school. Utilizing an artificial intelligence Universal Language Model from the Azure SST platform, the audio files were analyzed reflecting the commonly spoken language in Portuguese, generating data on words read correctly per minute (WRCM), the percentage of correct words over incorrect words (PCW), average consecutive correct words (CCW), and average silence time between sentences (SBS). Statistical analysis involved linear mixed models followed by pairwise comparisons. The private school segment outperformed the public school segment in WRCM in the 4th grade and in PCW in the 3rd grade. Additionally, private schools were already reaching 5th-grade levels in WRCM during the 4th grade, and this effect was not evident within public schools. No differences were found regarding SBS and CCW. These findings highlight that differences between public and private schools concerning reading fluency may manifest during the early stages of elementary school.
Crop yield and water productivity modeling using nonlinear growth functions
A comprehensive analysis of digital inclusive finance’s influence on high quality enterprise development through fixed effects and deep learning frameworks
Simultaneously improved dipolar interaction at inorganic-inorganic and organic-inorganic interfaces for multimode energy harvesting from heterostructured CoFe2O4@BCZT loaded P(VDF-TrFE)
Lateral pretrichial subcutaneous brow lift with upper eyelid blepharoplasty
Differential effects of 17β-estradiol on knee and temporomandibular joints in ovariectomized rats
Inflammatory mediation by neutrophil percentage to albumin ratio in the association between chronic kidney disease and presbycusis
Optimization of PES-based Hollow fiber membranes incorporating MgO-modified activated carbon via response surface methodology for enhanced pure water permeability
Characterization of growth differentiation factor 15 (GDF15) as a neurotropic adipokine permeable to the brain
Abstract GDF15 (growth/differentiation factor-15) belongs to the superfamily of transforming growth factor-beta. Little is known about adipocytic regulation of GDF15, its concentrations in serum and cerebrospinal fluid (CSF), its permeability to the brain, and its correlation with neurological diseases. This knowledge is important for a potential and clinical role of GDF15 as a mediator of the fat-brain axis in metabolic and neurological diseases. GDF15 mRNA expression in 3T3-L1 adipocytes was measured by qPCR and GDF15 protein levels in supernatants, serum and CSF were determined by ELISA. In vitro, GDF15 expression is nearly absent in pre-adipocytes and strongly upregulated during adipocyte differentiation. Insulin upregulates GDF15 secretion in adipocytes under normo- and hyperglycemic conditions. In vivo, we quantified GDF15 protein concentrations in paired samples of serum and CSF in a large and well-characterized clinical cohort of n = 390 patients undergoing neurological investigation and spinal puncture. This broad data set could serve as a basis for the development of GDF15 reference values in serum and CSF. GDF15 is highly permeable to the brain according to a specific CSF / serum ratio of 306 × 10–3. GDF15 is significantly increased in overweight and type 2 diabetic patients and correlates positively with serum glucose and HbA1c. GDF15 in CSF is elevated in patients with increased CSF cell count and impaired blood–brain-barrier function. Among five subsets of neurological diagnoses, GDF15 is exclusively increased in CSF and serum of patients with infectious diseases. GDF15 represents a promising adipokine and mediator of the fat-brain-axis that is co-regulated with metabolic factors and elevated in neurological patients with infectious diseases.