Existence, uniqueness and stability results of impulsive stochastic semilinear neutral functional differential equations with infinite delays

This article presents the results on existence, uniqueness and stability of mild solutions of impulsive stochastic semilinear neutral functional differential equations without a Lipschitz condition and with a Lipschitz condition. The results are obtained by using the method of successive approximations

Anti-Periodic Boundary Value Problem for Impulsive Fractional Integro Differential Equations

MSC 2010: 34A37, 34B15, 26A33, 34C25, 34K37In this paper we prove the existence of solutions for fractional impulsive differential equations with antiperiodic boundary condition in Banach spaces. The results are obtained by using fractional calculus' techniques and the fixed point theorems

Pediatric Burns- Our Experience: A Single Centre Observational Study

Introduction: Pediatric burns accounts to a significant percentage of morbidity and mortality among all the burns. This is in fact holds stronger when it comes to low-moderate income countries owing to the patient’s ability to spend and lack of infrastructure.Materials & Methods: This is a single centre observational study including all the pediatric burns cases admitted in a tertiary care hospital in Chennai, India. A total of 35 children less than 12 years of age admitted between June 2023-May 2024 over a period of 12 months were included in the study.Results: Scald burns (71.4%) were the most common type, followed by flame burns (22.85%), with children aged 1–5 years being the most affected (74.3%). Extensive burns (>10% TBSA) were observed in 55.3% of cases. Skin grafting was required in five children, significantly prolonging hospital stay (30.6 ± 7.63 days vs. 6.2 ± 3.07 days for those not requiring grafting). This article highlights the treatment protocol followed at our hospital, addressing challenges from parental counseling on the necessity of admission and burn care to post-hospital management.Conclusion: This study highlights the necessity of a multidisciplinary approach involving plastic surgeons, pediatricians, intensivists, psychologists, and nutritionists for optimal pediatric burn management. Preventive strategies and timely interventions are essential in reducing burn-related morbidity and improving outcomes. Public education on burn prevention and management can facilitate better parental awareness, easing the counseling process and ensuring smoother implementation of treatment plans

Chronic Ring Tourniquet Syndrome: A Rare Case Report with A Viable Digit

Ring Tourniquet Syndrome (RTS) is an acute condition in which a circumferential object (jewellery or a metal nut) is impacted over the base of a digit causing a constriction effect over the neurovascular pedicles. If left untreated, the resultant edema and constriction will lead on to ischemia and necrosis of the affected digit, resulting in amputation. Removing the ring requires utmost gentleness in order to avoid injury to the surrounding tissues and applying force just over the metal ring to either slide it through by threading technique or by cutting it. We report a rare case of 40-year-old female presented with pain and swelling in the right index finger due to metal ring impaction after trivial trauma for over a week. Poor pain tolerance and poor compliance required intravenous sedation for removal. Intraoperatively, the ring was loosely lodged just above the extensor apparatus, allowing partial mobility preserving the vascular pedicles. It was successfully removed using a bone cutter. The patient was discharged the next day with antibiotics, and follow-up showed complete healing and functional recovery

Design of an Integrated Model for Security Establishment in Iot-Enabled Software Defined Networks

Robust network designs are provided by software-defined networks (SDNs) for Internet of Things (IoT) applications, both present and future. At the same time, because of their programmability and global network perspective, SDNs are a desirable target for cyber threats. Among its primary drawbacks is the susceptibility of standard SDN architectures to Distributed Denial of Service (DDoS) flooding attacks. DDoS flooding assaults often result in a complete failure or service outage by rendering SDN controllers useless with respect to their underlying infrastructure. This study looks at popular machine learning (ML) methods for classifying and detecting DDoS flooding attacks on SDNs. Restricted Boltzmann Machine with Restricted Whales’ Optimizer (RBM-RWO) is the classifier integrated optimizer and other machine learning techniques examined. In this case study, experimental data (jitter, throughput, and reaction time measurements) from a realistic SDN architecture appropriate for typical midsized enterprise-wide networks are used to construct classification models that effectively detect and describe DDoS flooding assaults. Attackers using DDoS floods used low orbit ion cannons (LOIC), user datagram protocol (UDP), transmission control protocol (TCP), and hypertext transfer protocol (HTTP). Despite the high effectiveness of all the ML techniques examined in identifying and categorizing DDoS flooding assaults, When it came to training time is 17.5 ms, prediction speed is 7e-3 observations/s, prediction accuracy of 98%, and overall performance, RBM-RWO performed the best

Identification and Characterization of Poorly Differentiated Invasive Carcinomas in a Mouse Model of Pancreatic Neuroendocrine Tumorigenesis

Pancreatic neuroendocrine tumors (PanNETs) are a relatively rare but clinically challenging tumor type. In particular, high grade, poorly-differentiated PanNETs have the worst patient prognosis, and the underlying mechanisms of disease are poorly understood. In this study we have identified and characterized a previously undescribed class of poorly differentiated PanNETs in the RIP1-Tag2 mouse model. We found that while the majority of tumors in the RIP1-Tag2 model are well-differentiated insulinomas, a subset of tumors had lost multiple markers of beta-cell differentiation and were highly invasive, leading us to term them poorly differentiated invasive carcinomas (PDICs). In addition, we found that these tumors exhibited a high mitotic index, resembling poorly differentiated (PD)-PanNETs in human patients. Interestingly, we identified expression of Id1, an inhibitor of DNA binding gene, and a regulator of differentiation, specifically in PDIC tumor cells by histological analysis. The identification of PDICs in this mouse model provides a unique opportunity to study the pathology and molecular characteristics of PD-PanNETs

Prediction of epigenetically regulated genes in breast cancer cell lines

Methylation of CpG islands within the DNA promoter regions is one mechanism that leads to aberrant gene expression in cancer. In particular, the abnormal methylation of CpG islands may silence associated genes. Therefore, using high-throughput microarrays to measure CpG island methylation will lead to better understanding of tumor pathobiology and progression, while revealing potentially new biomarkers. We have examined a recently developed high-throughput technology for measuring genome-wide methylation patterns called mTACL. Here, we propose a computational pipeline for integrating gene expression and CpG island methylation profles to identify epigenetically regulated genes for a panel of 45 breast cancer cell lines, which is widely used in the Integrative Cancer Biology Program (ICBP). The pipeline (i) reduces the dimensionality of the methylation data, (ii) associates the reduced methylation data with gene expression data, and (iii) ranks methylation-expression associations according to their epigenetic regulation. Dimensionality reduction is performed in two steps: (i) methylation sites are grouped across the genome to identify regions of interest, and (ii) methylation profles are clustered within each region. Associations between the clustered methylation and the gene expression data sets generate candidate matches within a fxed neighborhood around each gene. Finally, the methylation-expression associations are ranked through a logistic regression, and their significance is quantified through permutation analysis. Our two-step dimensionality reduction compressed 90% of the original data, reducing 137,688 methylation sites to 14,505 clusters. Methylation-expression associations produced 18,312 correspondences, which were used to further analyze epigenetic regulation. Logistic regression was used to identify 58 genes from these correspondences that showed a statistically signifcant negative correlation between methylation profles and gene expression in the panel of breast cancer cell lines. Subnetwork enrichment of these genes has identifed 35 common regulators with 6 or more predicted markers. In addition to identifying epigenetically regulated genes, we show evidence of differentially expressed methylation patterns between the basal and luminal subtypes. Our results indicate that the proposed computational protocol is a viable platform for identifying epigenetically regulated genes. Our protocol has generated a list of predictors including COL1A2, TOP2A, TFF1, and VAV3, genes whose key roles in epigenetic regulation is documented in the literature. Subnetwork enrichment of these predicted markers further suggests that epigenetic regulation of individual genes occurs in a coordinated fashion and through common regulators

Efficient quantitative assessment of facial paralysis using iris segmentation and active contour-based key points detection with hybrid classifier

BACKGROUND: Facial palsy or paralysis (FP) is a symptom that loses voluntary muscles movement in one side of the human face, which could be very devastating in the part of the patients. Traditional methods are solely dependent to clinician’s judgment and therefore time consuming and subjective in nature. Hence, a quantitative assessment system becomes apparently invaluable for physicians to begin the rehabilitation process; and to produce a reliable and robust method is challenging and still underway. METHODS: We introduce a novel approach for a quantitative assessment of facial paralysis that tackles classification problem for FP type and degree of severity. Specifically, a novel method of quantitative assessment is presented: an algorithm that extracts the human iris and detects facial landmarks; and a hybrid approach combining the rule-based and machine learning algorithm to analyze and prognosticate facial paralysis using the captured images. A method combining the optimized Daugman’s algorithm and Localized Active Contour (LAC) model is proposed to efficiently extract the iris and facial landmark or key points. To improve the performance of LAC, appropriate parameters of initial evolving curve for facial features’ segmentation are automatically selected. The symmetry score is measured by the ratio between features extracted from the two sides of the face. Hybrid classifiers (i.e. rule-based with regularized logistic regression) were employed for discriminating healthy and unhealthy subjects, FP type classification, and for facial paralysis grading based on House-Brackmann (H-B) scale. RESULTS: Quantitative analysis was performed to evaluate the performance of the proposed approach. Experiments show that the proposed method demonstrates its efficiency. CONCLUSIONS: Facial movement feature extraction on facial images based on iris segmentation and LAC-based key point detection along with a hybrid classifier provides a more efficient way of addressing classification problem on facial palsy type and degree of severity. Combining iris segmentation and key point-based method has several merits that are essential for our real application. Aside from the facial key points, iris segmentation provides significant contribution as it describes the changes of the iris exposure while performing some facial expressions. It reveals the significant difference between the healthy side and the severe palsy side when raising eyebrows with both eyes directed upward, and can model the typical changes in the iris region