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Introduction: Orthodontic adhesives should possess adequate bond strength and biocompatibility. Inadequate bond strength may lead to bond failures, and if the adhesive is not biocompatible, it may result in allergic reactions. Therefore, it is of prime importance to study the cytotoxicity and Shear Bond Strength (SBS) of commercially available orthodontic materials. Aim: This study aimed to comparatively evaluate in-vitro cytotoxicity, the Adhesive Remnant Index (ARI), and SBS of various orthodontic adhesives. Materials and Methods: This in-vitro study was conducted in an institutional setting as a collaborative effort between the Department of Orthodontics and White Lab-Blue Lab at Saveetha Dental College and Hospital, Chennai, India, from July 2023 to August 2023. Forty extracted human premolar teeth were bonded with four different ARI adhesives (U Bond, Enlight, Transbond XT, Aqualine LC) (n=10/group) and mounted on acrylic blocks. They were evaluated for SBS using a Universal Testing Machine (UTM), ARI using Scanning Electron Microscopy (SEM), and cytotoxicity using the MTT {3-(4, 5-dimethylthiazolyl-2)-2, 5-diphenyltetrazolium bromide} assay on Human Gingival Fibroblast Cells (HGFC). Statistical analysis was performed using One-way ANOVA, with a post-hoc test for pairwise comparison to evaluate the SBS values, and the Kruskal-Wallis Test to analyse ARI inter group scores and cytotoxicity levels. Statistical Package for the Social Sciences (SPSS) software (Version 23.0) was used to conduct the statistical tests. Results: Significant differences in SBS values (p=

Abstract Background Cancer survivors experience a decrement in health-related quality of life (HRQoL) resulting from the disease as well as adverse effects of therapy. We evaluated the HRQoL of cancer patients, stratified by primary cancer site, stage, treatment response and associated adverse events, along with its determinants. Methods Data were collected from 12,148 patients, sampled from seven purposively chosen leading cancer hospitals in India, to elicit HRQoL using the EuroQol questionnaire comprising of 5-dimensions and 5-levels (EQ-5D-5L). Multiple linear regression was used to determine the association between HRQoL and various socio-demographic as well as clinical characteristics. Results Majority outpatients (78.4%) and inpatients (81.2%) had solid cancers. The disease was found to be more prevalent among outpatients (37.5%) and inpatients (40.5%) aged 45–60 years and females (49.3–58.3%). Most patients were found to be in stage III (40–40.6%) or stage IV (29.4–37.3%) at the time of recruitment. The mean EQ-5D-5 L utility score was significantly higher among outpatients [0.630 (95% CI: 0.623, 0.637)] as compared to inpatients [0.553 (95% CI: 0.539, 0.567)]. The HRQoL decreased with advancing cancer stage among both inpatients and outpatients, respectively [stage IV: (0.516 & 0.557); stage III (0.609 & 0.689); stage II (0.677 & 0.713); stage I (0.638 & 0.748), p value

In computer vision, semantic segmentation precisely delineates objects at the pixel level. This fundamental idea is constantly evolving by adding new modules and adjustments to suit the unique characteristics of different object classes. Pixel-level semantic segmentation is an intricate and computationally intensive task, especially within the context of part-based approaches. The study proposes a transformer-based attention network that is edge-guided and developed for the precise partitioning of different parts of quadruped animals. The process of labeling masks at the pixel level is a challenging task for various object categories, owing to its inherent complexity, which often results in inaccurate annotations. An additional mechanism is used to enhance pixel-level accuracy between classes, which iteratively refines labels. The model is evaluated using the PascalPart and PartImageNet datasets, using various scales of transformer architectures. Performance is evaluated using metrics such as mean Intersection-over-Union (mIoU), Pixel Accuracy (PA), and mean Accuracy (mA). Ablation studies are conducted to evaluate the model’s performance based on network parameters, while the effectiveness of each component is assessed using Class Activation Maps (CAM). The results show a notable 8% improvement in mIoU scores over existing state-of-the-art architectures, indicating the effectiveness of the proposed model in achieving fine-grained part segmentation, particularly in the context of quadruped animals. The open source code is available at https://github.com/abhigoku10/EGA-Net.

Background: Older adults often have chronic diseases for which they receive multiple drugs, which may be potentially inappropriate. Objectives: We aimed to describe the potentially inappropriate medications (PIMs) leading to adverse drug events (ADEs) in older patients with cancer. Our secondary objectives were to evaluate the association of nutrition, cognition, and frailty with PIM-related ADEs and to assess the impact of PIM-related ADEs on overall survival (OS). We also investigated the cut-off for defining polypharmacy as related to ADEs. Materials and Methods: This was a retrospective observational study on patients with cancer aged 60 years and over who were assessed in the geriatric oncology clinic at the Tata Memorial Hospital (Mumbai, India) from June 2018 to August 2022. Medications, PIM assessment, nutrition (assessed by Mini Nutritional Assessment [MNA]), cognition (assessed by Mini Mental State Examination [MMSE] and Hindi Mental State Examination), and frailty (assessed by the Clinical Frailty Scale [CFS]) were extracted from the geriatric oncology clinic database. PIMs were identified using the Beers criteria, European Union-7 (EU[7])-PIM, Screening Tool of Older person's Prescriptions/Screening tool to Alert to Right Treatment (STOPP/START), Fit fOR The Aged (FORTA), and PRISCUS list. Results: In total, 1472 patients were assessed in the geriatric oncology clinic, of which 823 (55.9%) were enrolled in the study. There were 1287 PIMs detected in 823 patients, of which 431 (33.5%) led to ADEs and 856 (66.5%) did not. Proton pump inhibitors and tramadol were the most common PIMs identified. ADEs were noted in 54 (14.7%) patients on proton pump inhibitors and in 145 (61.1%) patients on tramadol. ADEs were significantly associated with malnutrition, lower cognition, and frailty. The median MNA score in patients without and with ADEs was 20.5 (interquartile range [IQR], 17.5-24.0) and 19.5 (IQR, 15.5–23.5), respectively; P, 0.001. The median MMSE score for the patients without and with ADEs was 28 (IQR, 26-29) and 27 (IQR: 25-29), respectively; P, 0.001. The median CFS scores for the patients without and with ADEs were 3 (IQR, 2-4) and 4 (IQR, 3-5), respectively; P < 0.001. The median OS in patients without and with ADEs was 13.1 months (95% confidence interval [CI], 10.64-17.87) and 10.2 months (95% CI, 8.80-12.85), respectively; P, 0.002. The optimal cut-off for polypharmacy leading to ADEs was 4.5 medications. Conclusions: There is a dire need to recognize and appropriately manage PIMs in older patients with cancer as PIM-related toxicities may negatively impact survival. Monitoring PIMs and following the recommendations to optimize the dose, avoid the drug, and find alternatives may improve the oncologic outcomes. Future studies should focus on adding a control group of patients not on PIMs, following up on PIM after recommendations, and investigating the impact of these recommendations on oncologic outcomes (Clinical Trials Registry-India: CTRI/2020/04/024675).

Introduction: The prevalence of co-existing cataract and glaucoma is increasing in the adult population. Combined surgeries have become more popular. However, there is a conflict over which technique provides the best Intraocular Pressure (IOP) control with good postoperative outcomes. Aim: To compare the efficacy of single-site versus two-site phacotrabeculectomy with mitomycin-C in patients with Primary Open-angle Glaucoma (POAG) and cataract. Materials and Methods: A prospective cohort study was conducted in the Department of Ophthalmology, S.V. Medical College, Tirupati, Andhra Pradesh, India, over a period of one year from January 2019 to January 2020. A total of 50 cases of POAG coexisting with cataract were analysed in the present study. Twentyfive cases were included in each group (Group-1 and Group-2). Phacoemulsification and trabeculectomy were both performed through a superior scleral tunnel in the single-site approach. The two-site method combines a superior trabeculectomy with a temporal clear corneal phacoemulsification. A concentration of 0.2 mg/mL of MMC was applied in both groups for three minutes. Patients were followed-up for three months after surgery to evaluate Intraocular Pressure (IOP), the need for antiglaucoma medication, and postoperative best-corrected Visual Acuity (VA). Comparative analysis was done using the Student’s t-test, and a p-value

Changes in neurovascular coupling are associated with both Alzheimer's disease and vascular dementia in later life, but this may be confounded by cerebrovascular risk. We hypothesized that hemodynamic latency would be associated with reduced cognitive functioning across the lifespan, holding constant demographic and cerebrovascular risk. In 387 adults aged 18-85 (mean = 48.82), dynamic causal modeling was used to estimate the hemodynamic response function in the left and right V1 and V3-ventral regions of the visual cortex in response to a simple checkerboard block design stimulus with minimal cognitive demands. The hemodynamic latency (transit time) in the visual cortex was used to predict general cognitive ability (Full-Scale IQ), controlling for demographic variables (age, race, education, socioeconomic status) and cerebrovascular risk factors (hypertension, alcohol use, smoking, high cholesterol, BMI, type 2 diabetes, cardiac disorders). Increased hemodynamic latency in the visual cortex predicted reduced cognitive function (p

BackgroundTo characterize acoustic features of an infant's cry and use machine learning to provide an objective measurement of behavioral state in a cry-translator. To apply the cry-translation algorithm to colic hypothesizing that these cries sound painful.MethodsAssessment of 1000 cries in a mobile app (ChatterBabyTM). Training a cry-translation algorithm by evaluating >6000 acoustic features to predict whether infant cry was due to a pain (vaccinations, ear-piercings), fussy, or hunger states. Using the algorithm to predict the behavioral state of infants with reported colic.ResultsThe cry-translation algorithm was 90.7% accurate for identifying pain cries, and achieved 71.5% accuracy in discriminating cries from fussiness, hunger, or pain. The ChatterBaby cry-translation algorithm overwhelmingly predicted that colic cries were most likely from pain, compared to fussy and hungry states. Colic cries had average pain ratings of 73%, significantly greater than the pain measurements found in fussiness and hunger (p

Over the past few years, deep learning techniques have revolutionized the field of face parsing by utilizing massive datasets to generate high-level features and achieve outstanding performance. Usually, these techniques involve Convolutional Neural Networks (CNNs) to derive features from the input image and then a decoder network to forecast the semantic labels for every pixel. Even with complex deep convolutional neural networks (DCNN), incorrect parsing leads to a semantic gap between identical features, especially at boundary levels. Face parsing has encountered additional challenges due to the inclusion of pose, illumination and facial expressions. In order to address these issues, a Boundary Aware Dilated Attention Network (BADANet) is introduced which explores the use of multi-scale techniques to improve the accuracy and robustness of the per-pixel frames. BADANet’s dilated attention module has been combined with a lightweight backbone to achieve exceptional results on LaPa, CelebAMask-HQ, and iBugMask. Extensive evaluations of the proposed method demonstrate its performance to be on par with various state-of-the-art techniques. The pipeline for the BADANet is available at https://github.com/abhigoku10/BADANet

Amodal segmentation is a critical task in the field of computer vision as it involves accurately estimating object boundaries that extend beyond occlusion. This paper introduces a network named after the Amodal Segmentation Head, ASH-Net, a novel architecture specifically designed for amodal segmentation. ASH-Net is comprised of a ResNet-50 backbone, a Feature Pyramid Network middle layer, and an Amodal Segmentation Head. The evaluation encompasses three diverse datasets, namely COCOA-cls, KINS, and D2SA, providing a comprehensive analysis of ASH-Net’s capabilities. The results obtained demonstrate the superiority of ASH-Net in accurately estimating object boundaries beyond occlusion across multiple datasets. Specifically, ASH-Net achieves an Average Precision of 62.15% on the COCOA-cls dataset, 72.58% on the KINS dataset, and an impressive 91.4% on the D2SA dataset. Through extensive evaluation using average precision and average recall metrics, ASH-Net exhibits exceptional performance compared to state-of-the-art models. These findings highlight the remarkable performance of ASH-Net in overcoming occlusion challenges and accurately delineating object boundaries. This research identifies optimal training parameters, such as coefficient dimensions and aspect ratios, that significantly enhance segmentation performance while maintaining computational efficiency. The proposed ASH-Net architecture and its performance pave the way for improved object recognition, enhanced scene understanding, and the development of practical applications in various domains.

Numerous deep perception technologies and methods are built on the foundation of pedestrian feature identification. It covers various fields, including autonomous driving, spying, and object tracking. A recent study area is the identification of personality traits that has attracted much interest in video surveillance. Identifying a person’s distinct areas is complex and plays an incredibly significant role. This paper presents a current method applied to networks of primary convolutional neurons to locate the area connected to the Person attribute. Using Individual Feature Identification, the features of a person, such as gender, age, fashion sense, and equipment, have received much attention in video surveillance analytics. This Article adopted a Conv-Attentional image transformer that broke down the most discriminating Attribute and region into multiple grades. The feed-forward system and conv-attention are the components of serial blocks, and parallel blocks have two attention-focused tactics: direct cross-layer attention and feature interpolation. It also provides a flexible Attribute Localization Module (ALM) to learn the regional aspects of each Attribute are considered at several levels, and the most discriminating areas are selected adaptively. We draw the conclusion that hybrid transformers outperform pure transformers in this instance. The extensive experimental results indicate that the proposed hybrid technique achieves higher results than the current strategies on four unique private characteristic datasets, i.e., RapV2, RapV1, PETA, and PA100K.

BackgroundThe rising economic burden of cancer on patients is an important determinant of access to treatment initiation and adherence in India. Several publicly financed health insurance (PFHI) schemes have been launched in India, with treatment for cancer as an explicit inclusion in the health benefit packages (HBPs). Although, financial toxicity is widely acknowledged to be a potential consequence of costly cancer treatment, little is known about its prevalence and determinants among the Indian population. There is a need to determine the optimal strategy for clinicians and cancer care centers to address the issue of high costs of care in order to minimize the financial toxicity, promote access to high value care and reduce health disparities.MethodsA total of 12,148 cancer patients were recruited at seven purposively selected cancer centres in India, to assess the out-of-pocket expenditure (OOPE) and financial toxicity among cancer patients. Mean OOPE incurred for outpatient treatment and hospitalization, was estimated by cancer site, stage, type of treatment and socio-demographic characteristics. Economic impact of cancer care on household financial risk protection was assessed using standard indicators of catastrophic health expenditures (CHE) and impoverishment, along with the determinants using logistic regression.ResultsMean direct OOPE per outpatient consultation and per episode of hospitalization was estimated as ₹8,053 (US$ 101) and ₹39,085 (US$ 492) respectively. Per patient annual direct OOPE incurred on cancer treatment was estimated as ₹331,177 (US$ 4,171). Diagnostics (36.4%) and medicines (45%) are major contributors of OOPE for outpatient treatment and hospitalization, respectively. The overall prevalence of CHE and impoverishment was higher among patients seeking outpatient treatment (80.4% and 67%, respectively) than hospitalization (29.8% and 17.2%, respectively). The odds of incurring CHE was 7.4 times higher among poorer patients [Adjusted Odds Ratio (AOR): 7.414] than richest. Enrolment in PM-JAY (CHE AOR = 0.426, and impoverishment AOR = 0.395) or a state sponsored scheme (CHE AOR = 0.304 and impoverishment AOR = 0.371) resulted in a significant reduction in CHE and impoverishment for an episode of hospitalization. The prevalence of CHE and impoverishment was significantly higher with hospitalization in private hospitals and longer duration of hospital stay (p

Nowadays, the penetration of renewable energy sources, mainly wind and solar photovoltaic systems, into the existing power systems, destabilizes the grid, especially in the aspect of frequency regulation. AGC is one of the most important tasks in this mixed-generation environment to maintain the balance between the generated and consumed electricity, thus keeping the system frequency at an acceptable level. Therefore, AGC needs to be optimized for the noisy and volatile output of RESs. AGTA is a recent optimization method developed based on the social foraging behavior of gorillas, being a sophisticated way of exploration and exploitation. Therefore, the method is implemented in AGC. By mimicking the social behaviour and foraging strategy, the group gives rise to a new technique for enhancing the efficiency and response of the AGC system in light of wind and PV energy generation variation. A two-area power system model has been formulated characterized by the existence of wind and PV generation in addition to the conventional sources of the power system. This model is meant to simulate diverse situations to give an idea as regards the capability of the new algorithm to enhance grid stability and adaptability. The simulated results show that the proposed AGTA significantly surpasses other conventional optimization methods for AGC and results in an effective frequency control strategy. it confirms the potential of the AGTA from a new perspective in providing a feasible option for decentralized frequency regulation in a multi-generation landscape.

The World Wide Web gives us an enormous amount of information that was once hard to find. Now, it's tough to search through lots of data for useful insights. Recently, text summarization has become a smart way to pull out important info from huge piles of text. We can think in two ways "single document" "multi document." first is about one source, while second involves many documents. often find it harder to create a precise summary when dealing with multiple sources instead of just one. To tackle this issue, this study presents the Discrete Bat Algorithm Optimization-based multi document summarizer (DBAT-MDS). This tool is designed to make multi-document summarization better. It gets compared to three different summarization techniques that are inspired by natural phenomena. All methods are tested using benchmark datasets from the Document Understanding Conference (DUC). Various metrics like the ROUGE score & F score are used for assessment. The proposed approach shows a strong improvement over the other summarizers included in this research. [ABSTRACT FROM AUTHOR]

The broad range of clinical manifestations and life-threatening infections caused by the Gram-positive bacterium, Streptococcus pyogenes or Group A Streptococcus (GAS), remains a significant concern to public health, with a subset of individuals developing neurological complications. Here, we examined the concurrent neuroimmune effects of subcutaneous GAS infections in an HLA-Class II (HLA) transgenic mouse model of subcutaneous GAS infection. To investigate changes in the skin–brain axis, HLA-DQ8 (DQA1*0301/DQB1*0302) mice (DQ8) were randomly divided into three groups: uninfected controls (No Inf), GAS infected and untreated (No Tx), and GAS infected with a resolution by clindamycin (CLN) treatment (CLN Tx) (10 mg/kg/5 days) and were monitored for 16 days post-infection. While the skin GAS burden was significantly reduced by CLN, the cortical and hippocampal GAS burden in the male DQ8 mice was not significantly reduced with CLN. Immunoreactivity to anti-GAS antibody revealed the presence of GAS bacteria in the vicinity of the neuronal nucleus in the neocortex of both No Tx and CLN Tx male DQ8 mice. GAS infection-mediated cortical cytokine changes were modest; however, compared to No Inf or No Tx groups, a significant increase in IL-2, IL-13, IL-22, and IL-10 levels was observed in CLN Tx females despite the lack of GAS burden. Western blot analysis of cortical and hippocampal homogenates showed significantly higher ionized calcium-binding adaptor-1 (Iba-1, microglia marker) protein levels in No Tx females and males and CLN Tx males compared to the No Inf group. Immunohistochemical analysis showed that Iba-1 immunoreactivity in the hippocampal CA3 and CA1 subregions was significantly higher in the CLN Tx males compared to the No Tx group. Our data support the possibility that the subcutaneous GAS infection communicates to the brain and is characterized by intraneuronal GAS sequestration, brain cytokine changes, Iba-1 protein levels, and concurrent CA3 and CA1 subregion-specific microgliosis, even without bacteremia.