BFT-IoMT: A Blockchain-Based Trust Mechanism to Mitigate Sybil Attack Using Fuzzy Logic in the Internet of Medical Things.

Numerous sensitive applications, such as healthcare and medical services, need reliable transmission as a prerequisite for the success of the new age of communications technology. Unfortunately, these systems are highly vulnerable to attacks like Sybil, where many false nodes are created and spread with deceitful intentions. Therefore, these false nodes must be instantly identified and isolated from the network due to security concerns and the sensitivity of data utilized in healthcare applications. Especially for life-threatening diseases like COVID-19, it is crucial to have devices connected to the Internet of Medical Things (IoMT) that can be believed to respond with high reliability and accuracy. Thus, trust-based security offers a safe environment for IoMT applications. This study proposes a blockchain-based fuzzy trust management framework (BFT-IoMT) to detect and isolate Sybil nodes in IoMT networks. The results demonstrate that the proposed BFT-IoMT framework is 25.43% and 12.64%, 12.54% and 6.65%, 37.85% and 19.08%, 17.40% and 8.72%, and 13.04% and 5.05% more efficient and effective in terms of energy consumption, attack detection, trust computation reliability, packet delivery ratio, and throughput, respectively, as compared to the other state-of-the-art frameworks available in the literature.

Cross-Sensor Fingerprint Enhancement Using Adversarial Learning and Edge Loss.

A fingerprint sensor interoperability problem, or a cross-sensor matching problem, occurs when one type of sensor is used for enrolment and a different type for matching. Fingerprints captured for the same person using various sensor technologies have various types of noises and artifacts. This problem motivated us to develop an algorithm that can enhance fingerprints captured using different types of sensors and touch technologies. Inspired by the success of deep learning in various computer vision tasks, we formulate this problem as an image-to-image transformation designed using a deep encoder-decoder model. It is trained using two learning frameworks, i.e., conventional learning and adversarial learning based on a conditional Generative Adversarial Network (cGAN) framework. Since different types of edges form the ridge patterns in fingerprints, we employed edge loss to train the model for effective fingerprint enhancement. The designed method was evaluated on fingerprints from two benchmark cross-sensor fingerprint datasets, i.e., MOLF and FingerPass. To assess the quality of enhanced fingerprints, we employed two standard metrics commonly used: NBIS Fingerprint Image Quality (NFIQ) and Structural Similarity Index Metric (SSIM). In addition, we proposed a metric named Fingerprint Quality Enhancement Index (FQEI) for comprehensive evaluation of fingerprint enhancement algorithms. Effective fingerprint quality enhancement results were achieved regardless of the sensor type used, where this issue was not investigated in the related literature before. The results indicate that the proposed method outperforms the state-of-the-art methods.

Context-Aware Pending Interest Table Management Scheme for NDN-Based VANETs.

In terms of delivery effectiveness, Vehicular Adhoc NETworks (VANETs) applications have multiple, possibly conflicting, and disparate needs (e.g., latency, reliability, and delivery priorities). Named Data Networking (NDN) has attracted the attention of the research community for effective content retrieval and dissemination in mobile environments such as VANETs. A vehicle in a VANET application is heavily reliant on information about the content, network, and application, which can be obtained from a variety of sources. The information gathered can be used as context to make better decisions. While it is difficult to obtain the necessary context information at the IP network layer, the emergence of NDN is changing the tide. The Pending Information Table (PIT) is an important player in NDN data retrieval. PIT size is the bottleneck due to the limited opportunities provided by current memory technologies. PIT overflow results in service disruptions as new Interest messages cannot be added to PIT. Adaptive, context-aware PIT entry management solutions must be introduced to NDN-based VANETs for effective content dissemination. In this context, our main contribution is a decentralised, context-aware PIT entry management (CPITEM) protocol. The simulation results show that the proposed CPITEM protocol achieves lower Interest Satisfaction Delay and effective PIT utilization based on context when compared to existing PIT entry replacement protocols.

Spatial Attention-Based 3D Graph Convolutional Neural Network for Sign Language Recognition.

Sign language is the main channel for hearing-impaired people to communicate with others. It is a visual language that conveys highly structured components of manual and non-manual parameters such that it needs a lot of effort to master by hearing people. Sign language recognition aims to facilitate this mastering difficulty and bridge the communication gap between hearing-impaired people and others. This study presents an efficient architecture for sign language recognition based on a convolutional graph neural network (GCN). The presented architecture consists of a few separable 3DGCN layers, which are enhanced by a spatial attention mechanism. The limited number of layers in the proposed architecture enables it to avoid the common over-smoothing problem in deep graph neural networks. Furthermore, the attention mechanism enhances the spatial context representation of the gestures. The proposed architecture is evaluated on different datasets and shows outstanding results.

VKORC1 gene polymorphism (-1639G>A) in warfarin therapy patients of Pakistani population.

OBJECTIVE: To observe vitamin K epoxide reductase complex subunit 1-1639 G>A polymorphism in patients resistant to warfarin therapy, and to calculate the allele frequency of the polymorphism in local patients. Methods: The cross-sectional case-control study was conducted at the Punjab Institute of Cardiology, Lahore, from 2013 to 2014 and comprised patients with heart valve replacement. Thy were divided into warfarin-resistant group 1 taking 10mg/day, 70mg/week and control group 2 taking a standard dose of 5mg/day, 35mg/week. The vitamin K epoxide reductase complex subunit 1-1639 G>A polymorphism analysis was done by polymerase chain reaction, followed by restriction fragment length polymorphism technique. Data was analysed using SPSS 20. RESULTS: Of the 146 patients, there were 73(50%) in each of the two groups. In group 1, there were 37(50.68%) males and 36(49.32%) were females with an overall mean age of 33±12 years, while group 2 had 36(49.32%) males and 37(50.68%) females with an overall mean age of 37±13 years. There were no significant differences in mean values of age, serum cholesterol, triglycerides and albumin levels between the groups (p>0.05). The G allele was the most frequently found in both groups, with 140(96%) in group-1 and 137(94%) in group-2. Overall, the homozygous GG genotype was significantly higher in the sample 132(90.4%) (p<0.05). CONCLUSIONS: There was evidence found that vitamin K epoxide reductase complex subunit 1-1639 G>A polymorphism alone may not be the dominant genetic factor associated with warfarin response variability.

Sensor-Based Human Activity Recognition with Spatio-Temporal Deep Learning.

Human activity recognition (HAR) remains a challenging yet crucial problem to address in computer vision. HAR is primarily intended to be used with other technologies, such as the Internet of Things, to assist in healthcare and eldercare. With the development of deep learning, automatic high-level feature extraction has become a possibility and has been used to optimize HAR performance. Furthermore, deep-learning techniques have been applied in various fields for sensor-based HAR. This study introduces a new methodology using convolution neural networks (CNN) with varying kernel dimensions along with bi-directional long short-term memory (BiLSTM) to capture features at various resolutions. The novelty of this research lies in the effective selection of the optimal video representation and in the effective extraction of spatial and temporal features from sensor data using traditional CNN and BiLSTM. Wireless sensor data mining (WISDM) and UCI datasets are used for this proposed methodology in which data are collected through diverse methods, including accelerometers, sensors, and gyroscopes. The results indicate that the proposed scheme is efficient in improving HAR. It was thus found that unlike other available methods, the proposed method improved accuracy, attaining a higher score in the WISDM dataset compared to the UCI dataset (98.53% vs. 97.05%).

Progress and prospects in the management of bacterial infections and developments in Phytotherapeutic modalities.

The advent of antibiotics revolutionized medical care resulting in significantly reduced mortality and morbidity caused by infectious diseases. However, excessive use of antibiotics has led to the development of antibiotic resistance and indeed, the incidence of multidrug-resistant pathogens is considered as a major disadvantage in medication strategy, which has led the scholar's attention towards innovative antibiotic sources in recent years. Medicinal plants contain a variety of secondary metabolites with a wide range of therapeutic potential against the resistant microbes. Therefore, the aim of this review is to explore the antibacterial potential of traditional herbal medicine against bacterial infections. More than 200 published research articles reporting the therapeutic potential of medicinal plants against drug-resistant microbial infections were searched using different databases such as Google Scholar, Science Direct, PubMed and the Directory of Open Access Journals (DOAJ), etc., with various keywords like medicinal plants having antibacterial activities, antimicrobial potentials, phytotherapy of bacterial infection, etc. Articles were selected related to the efficacious herbs easily available to local populations addressing common pathogens. Various plants such as Artocarpus communis, Rheum emodi, Gentiana lutea L., Cassia fistula L., Rosemarinus officinalis, Argemone maxicana L, Hydrastis canadensis, Citrus aurantifolia, Cymbopogon citrates, Carica papaya, Euphorbia hirta, etc, were found to have significant antibacterial activities. Although herbal preparations have promising potential in the treatment of multidrug-resistant bacterial infection, still more research is required to isolate phytoconstituents, their mechanism of action as well as to find their impacts on the human body.

Susceptibility pattern of tracheal tube isolates from Intensive Care Unit of Fauji Foundation Hospital Rawalpindi.

OBJECTIVE: To determine the prevalence of resistant pathogens and their antimicrobial susceptibility pattern in an intensive care unit. METHODS: The cross-sectional observational study was conducted at Foundation Hospital, Rawalpindi, Pakistan, from May to September 2016, and comprised tracheal tubes which were collected in sputum culture bottles from patients with clinical findings of ventilator associated pneumonia. The tubes were cultured to locate the resistant pathogens. RESULTS: A total of 113 different strains of bacteria were isolated from 80 patients. The main isolated bacteria was acinetobacter baumannii 45(39.8%) followed by klebsiella pneumonia 14(12.3%) and methicillin-resistant staphylococcus aureus 13(11.5%). Polymyxin B was the most appropriate drug for treating patients infected with acinetobacter baumannii with a sensitivity of 64% while vancomycin and linez oli dhad 100% sensitivity for methicill in - resistant staphylococcusaureus. CONCLUSIONS: Acinetobacter baumannii was the most prevalent strain in tracheal tubes and polymyxin B was the most effective medicine.

Rhino Physiognomy: a Myth or Science?

Face reading has been practised since time immemorial by different cultures. Different personality traits have been attributed to different characters of face. It is argued that everyone uses face reading in their daily life when they choose one person to another.One would not prefer to sit beside a tidy, handsome, well-dressed man if he had narrow mean eyes. People tend to artificially change the features of their face to gain acceptance in interviews. Most of these preferences are arbitrary and are born out of the authors' preconceived cultural and social influences. But is there a science behind these observations. The proponents of face reading argue that this is based on clearly stated rules and observation. Nose is an important part of the face. According to face reading the shape and size of nose determines the aggressiveness of the person. The present study tries to scientifically test this statement.

Cooperative Opportunistic Pressure Based Routing for Underwater Wireless Sensor Networks.

In this paper, three opportunistic pressure based routing techniques for underwater wireless sensor networks (UWSNs) are proposed. The first one is the cooperative opportunistic pressure based routing protocol (Co-Hydrocast), second technique is the improved Hydrocast (improved-Hydrocast), and third one is the cooperative improved Hydrocast (Co-improved Hydrocast). In order to minimize lengthy routing paths between the source and the destination and to avoid void holes at the sparse networks, sensor nodes are deployed at different strategic locations. The deployment of sensor nodes at strategic locations assure the maximum monitoring of the network field. To conserve the energy consumption and minimize the number of hops, greedy algorithm is used to transmit data packets from the source to the destination. Moreover, the opportunistic routing is also exploited to avoid void regions by making backward transmissions to find reliable path towards the destination in the network. The relay cooperation mechanism is used for reliable data packet delivery, when signal to noise ratio (SNR) of the received signal is not within the predefined threshold then the maximal ratio combining (MRC) is used as a diversity technique to improve the SNR of the received signals at the destination. Extensive simulations validate that our schemes perform better in terms of packet delivery ratio and energy consumption than the existing technique; Hydrocast.

Cooperative Position Aware Mobility Pattern of AUVs for Avoiding Void Zones in Underwater WSNs.

In this paper, we propose two schemes; position-aware mobility pattern (PAMP) and cooperative PAMP (Co PAMP). The first one is an optimization scheme that avoids void hole occurrence and minimizes the uncertainty in the position estimation of glider's. The second one is a cooperative routing scheme that reduces the packet drop ratio by using the relay cooperation. Both techniques use gliders that stay at sojourn positions for a predefined time, at sojourn position self-confidence (s-confidence) and neighbor-confidence (n-confidence) regions that are estimated for balanced energy consumption. The transmission power of a glider is adjusted according to those confidence regions. Simulation results show that our proposed schemes outperform the compared existing one in terms of packet delivery ratio, void zones and energy consumption.

Procalcitonin levels and other biochemical parameters in patients with or without diabetic foot complications.

BACKGROUND: Diagnosis of infection in diabetic foot ulcer (DFU) is not always simple. The analytic precision of procalcitonin (PCT) was evaluated to clarify the use of PCT for distinguish the presence of infection in DFU in comparison to other inflammatory markers. MATERIALS AND METHODS: This study comprised 88 subjects distributed into four groups: 16 nondiabetic healthy subjects (group control), 17 patients with type 2 diabetes mellitus without foot Complication (group DM), 25 patients with noninfected diabetic foot (group NIDF), and 30 patients with infected diabetic foot (group IDF). Fasting blood samples were taken for measurement of glucose, hemoglobin A1C, lipid profile, renal function, erythrocyte sedimentation rate (ESR), and white blood cell (WBC) and its derivatives. Plasma PCT was determined using an enzyme-linked immunosorbent assay. RESULTS: PCT, WBC, ESR, and neutrophils (NEU) were found significantly higher in IDF group than other groups. The receiver operating characteristic analysis showed that sensitivity, specificity, the best cutoff value, and the area under the curve were for ESR (100%, 93%, 31.5 mm/h, 1; P < 0.001), for PCT (87.5%, 86.7%, 66.55 pg/dl, 0.977; P < 0.001), for NEU (93.8%, 93.3%, 5.35, 0.957; P < 0.001) and for WBC (93.8%, 90%, 9.29 × 109/L, 0.942; P < 0.001), respectively. CONCLUSION: The outcomes of this study recommend that PCT can be an asymptomatic marker in the diagnosis of infection in DFU with higher Wagner grades in combination with different inflammatory markers.

An Improved Forwarding of Diverse Events with Mobile Sinks in Underwater Wireless Sensor Networks.

In this paper, a novel routing strategy to cater the energy consumption and delay sensitivity issues in deep underwater wireless sensor networks is proposed. This strategy is named as ESDR: Event Segregation based Delay sensitive Routing. In this strategy sensed events are segregated on the basis of their criticality and, are forwarded to their respective destinations based on forwarding functions. These functions depend on different routing metrics like: Signal Quality Index, Localization free Signal to Noise Ratio, Energy Cost Function and Depth Dependent Function. The problem of incomparable values of previously defined forwarding functions causes uneven delays in forwarding process. Hence forwarding functions are redefined to ensure their comparable values in different depth regions. Packet forwarding strategy is based on the event segregation approach which forwards one third of the generated events (delay sensitive) to surface sinks and two third events (normal events) are forwarded to mobile sinks. Motion of mobile sinks is influenced by the relative distribution of normal nodes. We have also incorporated two different mobility patterns named as; adaptive mobility and uniform mobility for mobile sinks. The later one is implemented for collecting the packets generated by the normal nodes. These improvements ensure optimum holding time, uniform delay and in-time reporting of delay sensitive events. This scheme is compared with the existing ones and outperforms the existing schemes in terms of network lifetime, delay and throughput.

Analysis of CYP2C9 polymorphisms (*2 and *3) in warfarin therapy patients in Pakistan. Association of CYP2C9 polymorphisms (*2 and*3) with warfarin dose, age, PT and INR.

Warfarin is a widely used anticoagulant characterized by having a narrow therapeutic index and exhibiting a wide range of inter-individual and inter-ethnic variation. Single nucleotide polymorphisms in hepatic VKORC1 and CYP2C9 genes causes decreased and increased metabolism of warfarin respectively. The objective of this study was to evaluate the allele frequency of CYP2C9 polymorphic variants *2 and *3 and the association of these allelic variants with PT/INR and daily/weekly dose of warfarin. Seventy-four patients with heart valve replacement were selected. Patients taking low warfarin dose (4.90-17.50 mg weekly) for at least last 3 months and had a stable INR in the range of 2-3 were included in this study. CYP2C9 polymorphism was analyzed by polymerase chain reaction followed by restriction fragment length polymorphism (PCR-RFLP) technique. Among 74 patients, 9 (12.1 %) showed to have *2 allele, whereas 11 (14.1 %) had *3 allele. Genotype frequencies of wild and variant alleles were, 54.1, 17.6, 21.6 and 6.8 % for *1/*1, *1/*2, *1/*3 and *2/*3 respectively. None of the patient was homozygous for *2 and *3. Statistical analysis showed that low warfarin dose (weekly) is significantly associated with *1/*2 and *1/*3 genotypes (p value ≥ 0.001), whereas PT/INR showed no significant association with the any genotypes of CYP2C9. Our study suggest that polymorphic variants of CYP2C9 (*2 and *3) might influence warfarin dose requirements and associated with the low dose of warfarin in patients.

Enhanced dissolution and bioavailability of grapefruit flavonoid Naringenin by solid dispersion utilizing fourth generation carrier.

CONTEXT: Naringenin (NRG), the aglycone flavonoid present in grapefruits, possesses anti-inflammatory, anti-carcinogenic, anti-lipid peroxidation and hepato-protective effects. However, it is poorly soluble in water and exhibits slow dissolution after oral ingestion, thus restricting its therapeutic efficacy. OBJECTIVE: With the aim to enhance the dissolution rate and oral bioavailability of NRG, solid dispersion technique has been applied using Soluplus® as carrier. METHODS: Solid dispersions of NRG were prepared by solvent evaporation and kneading methods using various ratios (1:4, 3:7, 2:3 and 1:1) of NRG:Carrier. Characterization of the optimized formulations was performed using Fourier transform infrared spectroscopy, differential scanning calorimetry (DSC) and X-ray diffraction (XRD) analysis. The in vivo behavior of the optimized formulations was also investigated in Wistar Albino rats. RESULTS: NRG solid dispersion showed a significantly higher solubility and drug dissolution rate than pure NRG (p < 0.001) and it followed the Higuchi model. Among the different methods employed for the preparation of solid dispersions, solvent evaporation showed better drug release profile. DSC analysis indicated reduced crystallinity of NRG as no discrete peaks of NRG were observed. This was further substantiated by XRD analysis. Furthermore, area under the drug concentration time-curve (AUC) of NRG from solid dispersion revealed a significant increase in NRG absorption compared to NRG alone. CONCLUSION: Based on these results, it was concluded that solid dispersion technique markedly enhances the in vitro drug release and in vivo behavior of the grapefruit flavonoid NRG.

NEW ALIPHATIC ESTER, ß-SITOSTEROL DIGLUCOSIDE AND VESICARIA BIFLAVONES FROM THE SEEDS OF RUMEX VESICARIUS L.

Rumex vesicalius L. (Polygonaceae) is an annual, monoecious, glabrous, pale green herb cultivated as a leafy vegetable in south western Asia and northern Africa. Its seeds are prescribed as a refrigerant, laxa- tive, antidote for scorpion venom and to cure dysentery and liver diseases. Phytochemical investigation of a methanolic extract of the seeds of R. vesicarius resulted in the isolation of a new aliphatic ester n-heptacosanyl n-hexanoate (2), a steroidal diglucoside stigmasta-5-en-3-ol-3-O-ß-D-glucopyranosido-(4--->1")-O-ß-D-glu- copyranoside (3) and two bioflavonoids characterized as (2a,3a-trans)-3a(ß),5a,7a,3'a,4'a-pentahydroxyfla- vanolyl-(8a-2')-5,7,3'-trihydroxy-4'-methoxy-8-n-but-3"-enyl-flavanone (4) and 5,7,3',4',5'-pentahydroxy- 8-(cis-1" α,2"ß-dihydroxyhept-4"-enyl-7"-oic acid)-flavanoyl-(2'--8a)-5a,7a,3'a,5'a-tetrahydroxy-4'a- methoxyflavanone (5) together with stigmasterol (1). The structures of all the isolated phytoconstituents have been established on the basis of spectral data analysis and chemical reactions.

AI-CADR: Artificial Intelligence Based Risk Stratification of Coronary Artery Disease using Novel Non-invasive Biomarkers.

Coronary artery disease (CAD) is one of the most common causes of sudden cardiac arrest, accounting for a large percentage of global mortality. A timely diagnosis and detection may save a person's life. The research suggests a methodological framework for non-invasive risk stratification based on information only possible after invasive coronary angiography. Novel clinical, chemical, and molecular cardiac biomarkers were used as input features from an especially collected dataset. Following a thorough evaluative search in the biomarker feature space, the optimum feature and classifier or regression technique (regressor) set were selected using K-fold cross-validation. Ten machine learning (ML) classifiers were employed in classification tasks to determine the number of affected cardiac vessels, the Gensini group, and the severity of CAD with 82.58%, 86.26%, and 90.91% accuracy, respectively. Eleven approaches were used in regression tasks to calculate stenosis percentage and Gensini score, with R-squared values of 0.58 and 0.56, respectively. Following a thorough evaluative search in the biomarkers feature space, the optimum feature and classifier or regressor set were selected using K-fold cross-validation. The biomarkers and classifier or regressor combinations serve as the foundation for the proposed risk stratification framework, incorporating clinical protocol. Finally, our proposed framework is compared to state-of-the-art studies, offering a robust, well-rounded, early detection capable, and novel 'biomarkers-ML combination' approach to risk stratification.

Geospatial approach to pluvial flood-risk and vulnerability assessment in Sunyani Municipality.

Historically, and in recent times, efforts have been to understand, predict, analyze, and quantify floods and their impacts in various countries of the globe. Although recent scientific advances have introduced approaches to assessing the risks presented by flooding, little studies have been carried out in the Sunyani Municipality of Ghana for generating a pluvial flood-risk and vulnerability map for risk identification, resilience, emergency preparedness, and urban spatial planning. In this study, five parameters that influence both pluvial and fluvial flooding were assessed to map flood-prone areas within the Sunyani Municipality. These are precipitation, drainage density, LULC, elevation, and slope, which were integrated in GIS. Using an AHP, weights were assigned to each parameter based on its level of influence on flooding. The findings reveal that 21.32 % of the Sunyani Municipality lies within a highly flood-prone area, 39.65 % in a flood-prone area, while 28.06 % and 10.97 % in slightly flood-prone and not flood-prone areas respectively. Built-up areas close to watersheds with lower elevations and larger drainage density are the places that are highly flood-prone. Some towns within the highly flood-prone and flood-prone areas are Abesim, Newtown, Nkwarbeng, Baakoniaba, Kootokrom, and Penkwase. Highly valued infrastructure such as schools, churches, and hospitals have also been found within these highly flood-prone areas. These findings can aid the government and relevant stakeholders in disaster risk management to be better informed, and to effectively plan and prevent flood challenges in the Sunyani Municipality. Moreover, urban spatial planners in the study setting can consider incorporating the flood hazard maps generated from this study into their spatial plans for proactive physical developments.

Prognostic Value of Intracellular Transcription of Factors HIF-1α and p53 and Their Relation to Estradiol and TNM Parameters of Breast Cancer Tissues in Women with Invasive Ductal Carcinoma in Thi-Qar Province, Iraq.

Breast cancer is the most common malignancy affecting women's health, with an increasing incidence worldwide. This study aimed to measure the intracellular concentration of the hypoxia-inducible factor 1 α (HIF-1α), tumor suppression protein p53, and estradiol (E2) in tumor tissues of adult females with breast cancer and their relation to tumor grade, tumor size, and lymph node metastases (LNM). The study was conducted on 65 adult female participants with breast mass admitted to the operating theater in Al-Hussein Teaching Hospital and Al-Habboby Teaching Hospital in Nasiriyah, Iraq, from January to November 2021. Fresh breast tumor tissues were collated and homogenized for intracellular biochemical analysis using the enzyme-linked immunosorbent assay method. In total, 44 (58%) out of 65 patients, in the age range of 18-42 years and the mean±SD age of 32.55±6.40 years, had fibroadenomas, and other 21 (42%) cases, in the age range of 32-80 years and the mean±SD age of 56±14.4 years had invasive ductal carcinoma (IDC) breast cancer. Intracellular levels of HIF-1α, p53, and E2 were elevated significantly (P<0.001) in IDC cases compared to the benign group. The most malignant tumors of IDC cases were in grade III and sizes T2 and T3. The tissue concentrations of HIF-1α, P53, and E2 were significantly elevated in patients with tumor stage T3 compared to T2 and T1. A significant elevation was found in the levels of HIF-1α, p53, and E2 in the positive LNM subgroup compared to the negative LNM group. Based on the obtained results, the prognostic value of the intracellular HIF-1α is considered to be a useful prognostic factor in Iraqi women with ICD and the combination of a HIF-1α protein with the nonfunctional p53 and E2 tends to indicate the proliferation, invasiveness, and metastases of the breast tumors.

A Prolonged History of Denture Impaction for 12 Weeks In Situ.

Foreign body ingestion has serious consequences if left untreated. Impacted dentures for a prolonged period can lead to life-threatening complications. Therefore, prompt diagnosis and immediate intervention are lifesaving. Our patient presented to his local accident and emergency department after having swallowed his dentures during a meal. Initial investigations and workup detected no abnormalities and he was discharged back to the community. Twelve weeks following ingestion, he had developed dysphagia and weight loss which prompted an urgent referral for oesophago-gastro-duodenoscopy (OGD). This identified the dentures impacted within the upper oesophagus and initial attempts at removal were unsuccessful, therefore he required hospital admission for alternative feeding in the interim. A joint procedure with the Ear, Nose and Throat and upper gastrointestinal surgeons was carried out to successfully remove the dentures endoscopically. The patient made an immediate recovery, resuming his normal oral diet with appropriate follow up after discharge. It is suspected our patient had an impacted denture for a period of 12 weeks without sustaining any life-threatening complications, which makes this case rather unique. This case highlights the importance of thorough and careful clinical history taking and examination.