Implementation of frailty screening for older people living with HIV in Brighton, UK.

OBJECTIVES: To evaluate the implementation of frailty screening in people living with HIV (PLWH) in a large urban cohort of patients in Brighton, UK. METHODS: Focus group discussions with HIV professionals and PLWH interviews helped inform the design and implementation of the frailty screening pathway in the clinic. Data were collected from PLWH aged over 60 years attending their HIV annual health check from July 2021 to January 2023 (n = 590), who were screened for frailty by nurses using the FRAIL scale. We assessed the proportions of PLWH who screened as frail, prefrail or robust and compared patient characteristics across groups. All PLWH identified as frail were offered a comprehensive geriatric assessment delivered by a combined HIV geriatric clinic, and uptake was recorded. RESULTS: A total of 456/590 (77.3%) PLWH aged over 60 years were screened for frailty. Median age and time since HIV diagnosis (range) for those screened were 66 (60-99) years and 21 (0-32) years, respectively. In total, 56 (12.1%) of those screened were identified as frail, 118 (25.9%) as prefrail and 282 (61.8%) as robust. A total of 10/56 (18%) people identified as frail declined an appointment in the geriatric clinic. Compared with non-frail individuals, frail PLWH had been living with HIV for longer and had a greater number of comorbidities and comedications but were not chronologically older. CONCLUSIONS: Implementing frailty screening in PLWH over 60 years old is feasible in a large cohort of PLWH, as recommended by the European AIDS Clinical Society. More research is needed to determine if frailty screening can improve clinical outcomes of older PLWH and the use of the comprehensive geriatric assessment within HIV services.

Efficient Classification of ECG Images Using a Lightweight CNN with Attention Module and IoT.

Cardiac disorders are a leading cause of global casualties, emphasizing the need for the initial diagnosis and prevention of cardiovascular diseases (CVDs). Electrocardiogram (ECG) procedures are highly recommended as they provide crucial cardiology information. Telemedicine offers an opportunity to provide low-cost tools and widespread availability for CVD management. In this research, we proposed an IoT-based monitoring and detection system for cardiac patients, employing a two-stage approach. In the initial stage, we used a routing protocol that combines routing by energy and link quality (REL) with dynamic source routing (DSR) to efficiently collect data on an IoT healthcare platform. The second stage involves the classification of ECG images using hybrid-based deep features. Our classification system utilizes the "ECG Images dataset of Cardiac Patients", comprising 12-lead ECG images with four distinct categories: abnormal heartbeat, myocardial infarction (MI), previous history of MI, and normal ECG. For feature extraction, we employed a lightweight CNN, which automatically extracts relevant ECG features. These features were further optimized through an attention module, which is the method's main focus. The model achieved a remarkable accuracy of 98.39%. Our findings suggest that this system can effectively aid in the identification of cardiac disorders. The proposed approach combines IoT, deep learning, and efficient routing protocols, showcasing its potential for improving CVD diagnosis and management.

Pashto Handwritten Invariant Character Trajectory Prediction Using a Customized Deep Learning Technique.

Before the 19th century, all communication and official records relied on handwritten documents, cherished as valuable artefacts by different ethnic groups. While significant efforts have been made to automate the transcription of major languages like English, French, Arabic, and Chinese, there has been less research on regional and minor languages, despite their importance from geographical and historical perspectives. This research focuses on detecting and recognizing Pashto handwritten characters and ligatures, which is essential for preserving this regional cursive language in Pakistan and its status as the national language of Afghanistan. Deep learning techniques were employed to detect and recognize Pashto characters and ligatures, utilizing a newly developed dataset specific to Pashto. A further enhancement was done on the dataset by implementing data augmentation, i.e., scaling and rotation on Pashto handwritten characters and ligatures, which gave us many variations of a single trajectory. Different morphological operations for minimizing gaps in the trajectories were also performed. The median filter was used for the removal of different noises. This dataset will be combined with the existing PHWD-V2 dataset. Various deep-learning techniques were evaluated, including VGG19, MobileNetV2, MobileNetV3, and a customized CNN. The customized CNN demonstrated the highest accuracy and minimal loss, achieving a training accuracy of 93.98%, validation accuracy of 92.08% and testing accuracy of 92.99%.

Investigating the Effectiveness of Novel Support Vector Neural Network for Anomaly Detection in Digital Forensics Data.

As criminal activity increasingly relies on digital devices, the field of digital forensics plays a vital role in identifying and investigating criminals. In this paper, we addressed the problem of anomaly detection in digital forensics data. Our objective was to propose an effective approach for identifying suspicious patterns and activities that could indicate criminal behavior. To achieve this, we introduce a novel method called the Novel Support Vector Neural Network (NSVNN). We evaluated the performance of the NSVNN by conducting experiments on a real-world dataset of digital forensics data. The dataset consisted of various features related to network activity, system logs, and file metadata. Through our experiments, we compared the NSVNN with several existing anomaly detection algorithms, including Support Vector Machines (SVM) and neural networks. We measured and analyzed the performance of each algorithm in terms of the accuracy, precision, recall, and F1-score. Furthermore, we provide insights into the specific features that contribute significantly to the detection of anomalies. Our results demonstrated that the NSVNN method outperformed the existing algorithms in terms of anomaly detection accuracy. We also highlight the interpretability of the NSVNN model by analyzing the feature importance and providing insights into the decision-making process. Overall, our research contributes to the field of digital forensics by proposing a novel approach, the NSVNN, for anomaly detection. We emphasize the importance of both performance evaluation and model interpretability in this context, providing practical insights for identifying criminal behavior in digital forensics investigations.

Customer Analysis Using Machine Learning-Based Classification Algorithms for Effective Segmentation Using Recency, Frequency, Monetary, and Time.

Customer segmentation has been a hot topic for decades, and the competition among businesses makes it more challenging. The recently introduced Recency, Frequency, Monetary, and Time (RFMT) model used an agglomerative algorithm for segmentation and a dendrogram for clustering, which solved the problem. However, there is still room for a single algorithm to analyze the data's characteristics. The proposed novel approach model RFMT analyzed Pakistan's largest e-commerce dataset by introducing k-means, Gaussian, and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) beside agglomerative algorithms for segmentation. The cluster is determined through different cluster factor analysis methods, i.e., elbow, dendrogram, silhouette, Calinsky-Harabasz, Davies-Bouldin, and Dunn index. They finally elected a stable and distinctive cluster using the state-of-the-art majority voting (mode version) technique, which resulted in three different clusters. Besides all the segmentation, i.e., product categories, year-wise, fiscal year-wise, and month-wise, the approach also includes the transaction status and seasons-wise segmentation. This segmentation will help the retailer improve customer relationships, implement good strategies, and improve targeted marketing.

Physical and electrochemical properties of new structurally flexible imidazolium phosphate ionic liquids.

New structurally flexible 1-methyl- and 1,2-dimethyl-imidazolium phosphate ionic liquids (ILs) bearing oligoethers have been synthesized and thoroughly characterized. These novel ILs revealed high thermal stabilities, low glass transitions, high conductivity and wide electrochemical stability windows up to 6 V. Both the cations and anions of 1-methyl-imidazolium ILs diffuse faster than the ions of 1,2-dimethyl-imidazolium ILs, as determined by pulsed field gradient nuclear magnetic resonance (PFG-NMR). The 1-methyl-imidazolium phosphate ILs showed relatively higher ionic conductivities and ion diffusivity as compared with the 1,2-dimethyl-imidazolium phosphate ILs. As expected, the diffusivity of all the ions increases with an increase in the temperature. The 1-methyl-imidazolium phosphate ILs formed hydrogen bonds with the phosphate anions, the strength of which is decreased with increasing temperature, as confirmed by variable temperature 1H and 31P NMR spectroscopy. One of the representative IL, [EmDMIm][DEEP], presented promising elevated temperature performance as an electrolyte in a supercapacitor composed of multiwall carbon nanotubes and activated charcoal (MWCNT/AC) composite electrodes.

Haptic Feedback to Assist Blind People in Indoor Environment Using Vibration Patterns.

Feedback is one of the significant factors for the mental mapping of an environment. It is the communication of spatial information to blind people to perceive the surroundings. The assistive smartphone technologies deliver feedback for different activities using several feedback mediums, including voice, sonification and vibration. Researchers 0have proposed various solutions for conveying feedback messages to blind people using these mediums. Voice and sonification feedback are effective solutions to convey information. However, these solutions are not applicable in a noisy environment and may occupy the most important auditory sense. The privacy of a blind user can also be compromised with speech feedback. The vibration feedback could effectively be used as an alternative approach to these mediums. This paper proposes a real-time feedback system specifically designed for blind people to convey information to them based on vibration patterns. The proposed solution has been evaluated through an empirical study by collecting data from 24 blind people through a mixed-mode survey using a questionnaire. Results show the average recognition accuracy for 10 different vibration patterns are 90%, 82%, 75%, 87%, 65%, and 70%.

Characterization of English Braille Patterns Using Automated Tools and RICA Based Feature Extraction Methods.

Braille is used as a mode of communication all over the world. Technological advancements are transforming the way Braille is read and written. This study developed an English Braille pattern identification system using robust machine learning techniques using the English Braille Grade-1 dataset. English Braille Grade-1 dataset was collected using a touchscreen device from visually impaired students of the National Special Education School Muzaffarabad. For better visualization, the dataset was divided into two classes as class 1 (1-13) (a-m) and class 2 (14-26) (n-z) using 26 Braille English characters. A position-free braille text entry method was used to generate synthetic data. N = 2512 cases were included in the final dataset. Support Vector Machine (SVM), Decision Trees (DT) and K-Nearest Neighbor (KNN) with Reconstruction Independent Component Analysis (RICA) and PCA-based feature extraction methods were used for Braille to English character recognition. Compared to PCA, Random Forest (RF) algorithm and Sequential methods, better results were achieved using the RICA-based feature extraction method. The evaluation metrics used were the True Positive Rate (TPR), True Negative Rate (TNR), Positive Predictive Value (PPV), Negative Predictive Value (NPV), False Positive Rate (FPR), Total Accuracy, Area Under the Receiver Operating Curve (AUC) and F1-Score. A statistical test was also performed to justify the significance of the results.

Cooperative Energy-Efficient Routing Protocol for Underwater Wireless Sensor Networks.

Underwater wireless sensor networks (UWSNs) contain sensor nodes that sense the data and then transfer them to the sink node or base station. Sensor nodes are operationalized through limited-power batteries. Therefore, improvement in energy consumption becomes critical in UWSNs. Data forwarding through the nearest sensor node to the sink or base station reduces the network's reliability and stability because it creates a hotspot and drains the energy early. In this paper, we propose the cooperative energy-efficient routing (CEER) protocol to increase the network lifetime and acquire a reliable network. We use the sink mobility scheme to reduce energy consumption by eliminating the hotspot issue. We have divided the area into multiple sections for better deployment and deployed the sink nodes in each area. Sensor nodes generate the data and send it to the sink nodes to reduce energy consumption. We have also used the cooperative technique to achieve reliability in the network. Based on simulation results, the proposed scheme performed better than existing routing protocols in terms of packet delivery ratio (PDR), energy consumption, transmission loss, and end-to-end delay.

RM-ADR: Resource Management Adaptive Data Rate for Mobile Application in LoRaWAN.

LoRaWAN is renowned and a mostly supported technology for the Internet of Things, using an energy-efficient Adaptive Data Rate (ADR) to allocate resources (e.g., Spreading Factor (SF)) and Transmit Power (TP) to a large number of End Devices (EDs). When these EDs are mobile, the fixed SF allocation is not efficient owing to the sudden changes caused in the link conditions between the ED and the gateway. As a result of this situation, significant packet loss occurs, increasing the retransmissions from EDs. Therefore, we propose a Resource Management ADR (RM-ADR) at both ED and Network Sides (NS) by considering the packet transmission information and received power to address this issue. Through simulation results, RM-ADR showed improved performance compared to the state-of-the-art ADR techniques. The findings indicate a faster convergence time by minimizing packet loss ratio and retransmission in a mobile LoRaWAN network environment.

The need to manage the risk of thromboembolism in COVID-19 patients.

COVID-19 first appeared in Wuhan, Hubei Province, China, in December 2019. Thought to be of zoonotic origin, it has been named SARS-CoV-2 (COVID-19) and has spread rapidly. As of April 20, 2020, there have been >2.4 million cases recorded worldwide. The inflammatory process, cytokine storm, and lung injury that are associated with COVID-19 can put patients at an increased risk of thrombosis. The total incidence of thrombotic events in COVID-19 patients is currently uncertain. Those with more severe disease and with other risk factors, including increasing age, male sex, obesity, cancer, comorbidities, and intensive care unit admission, are at higher risk of these events. However, there is little international guidance on managing these risks in COVID-19 patients. In this paper, we explore the current evidence and theories surrounding thrombosis in these unique patients and reflect on experience from our center.

At the heart of COVID-19.

Coronavirus disease (COVID-19) first presented in Wuhan, Hubei province, China in December 2019. Since then, it has rapidly spread across the world, and is now formally considered a pandemic. The disease does not discriminate but increasing age and the presence of comorbidities are associated with severe form of the disease and poor outcomes. Although the prevalence of COVID-19 in patients with cardiovascular disease is under-reported, there is evidence that pre-existing cardiac disease can render individuals vulnerable. It is thought that COVID-19 may have both a direct and indirect effect on the cardiovascular system; however, the primary mechanism of underlying cardiovascular involvement is still uncertain. Of particular interest is the role of angiotensin-converting enzyme 2, which is well known for its cardiovascular effects and is also considered to be important in the pathogenesis of COVID-19. With a range of different drug candidates being suggested, effective anti-virals and vaccines are an area of on-going research. While our knowledge of COVID-19 continues to rapidly expand, this review highlights recent advances in our understanding of the interaction between COVID-19 and the cardiovascular system.

Comparing the Thermal and Electrochemical Stabilities of Two Structurally Similar Ionic Liquids.

Here we focus on the thermal and variable temperature electrochemical stabilities of two ionic liquids (ILs) having a common tributyloctyl phosphonium cation [P4,4,4,8]+ and two different orthoborate anions: bis(mandelato)borate [BMB]- and bis(salicylato)borate [BScB]-. The thermo-gravimetric analysis data suggest that [P4,4,4,8][BScB] is thermally more stable than [P4,4,4,8][BMB] in both nitrogen atmosphere and air, while the impedance spectroscopy reveals that [P4,4,4,8][BScB] has higher ionic conductivity than [P4,4,4,8][BMB] over the whole studied temperature range. In contrast, the electrochemical studies confirm that [P4,4,4,8][BMB] is more stable and exhibits a wider electrochemical stability window (ESW) on a glassy carbon electrode surface as compared to [P4,4,4,8][BScB]. A continuous decrease in the ESWs of both ILs is observed as a function of operation temperature.

Electrocatalysts Derived from Metal-Organic Frameworks for Oxygen Reduction and Evolution Reactions in Aqueous Media.

Electrochemical energy conversion and storage devices such as fuel cells and metal-air batteries have been extensively studied in recent decades for their excellent conversion efficiency, high energy capacity, and low environmental impact. However, sluggish kinetics of the oxygen-related reactions at air cathodes, i.e., oxygen reduction reaction (ORR) and oxygen evolution reaction (OER), are still worth improving. Noble metals such as platinum (Pt), iridium (Ir), ruthenium (Ru) and their oxides are considered as the benchmark ORR and OER electrocatalysts, but they are expensive and prone to be poisoned due to the fuel crossover effect, and may suffer from agglomeration and leaching after long-term usage. To mitigate these limits, it is highly desirable to design alternative ORR/OER electrocatalysts with prominent performance. Metal-organic frameworks (MOFs) are a class of porous crystalline materials consisting metal ions/clusters coordinated by organic ligands. Their crystalline structure, tunable pore size and high surface area afford them wide opportunities as catalytic materials. This Review covers MOF-derived ORR/OER catalysts in electrochemical energy conversion, with a focus on the different strategies of material design and preparation, such as composition control and nanostructure fabrication, to improve the activity and durability of MOF-derived electrocatalysts.

In vivo effects of a selected thiourea derivative 1-(2-chlorobenzoyl)-3-(2,3-dichlorophenyl) against nociception, inflammation and gastric ulcerogenicity: Biochemical, histopathological and in silico approaches.

The current study was designed to investigate the potential of a synthetic therapeutic agent for better management of pain and inflammation, exhibiting minimal to non-existent ulcerogenic effects. The effect of 1-(2-chlorobenzoyl)-3-(2,3-dichlorophenyl) thiourea was assessed through model systems of nociception and anti-inflammatory activities in mice. In addition, the ulcerogenic potential was evaluated in rats using the NSAID-induced pyloric ligation model, followed by histopathological and biochemical analysis. The test was conducted on eight groups of albino rats, comprising of group I (normal saline), groups II and III (aspirin® at doses of 100 mg/kg and 150 mg/kg, respectively), groups IV and V (indomethacin at doses of 100 mg/kg and 150 mg/kg, respectively), and groups VI, VII, and VIII (lead-compound at 15 mg/kg, 30 mg/kg and 45 mg/kg doses, respectively). Furthermore, molecular docking analyses were performed to predict potential molecular target site interactions. The results showed that the lead-compound, administered at doses of 15, 30, and 45 mg/kg, yielded significant reductions in chemically and thermally induced nociceptive pain, aligning with the levels observed for aspirin® and tramadol. The compound also effectively suppressed inflammatory response in the carrageenan-induced paw edema model. As for the ulcerogenic effects, the compound groups displayed no considerable alterations compared to the aspirin® and indomethacin groups, which displayed substantial increases in ulcer scores, total acidity, free acidity, and gastric juice volume, and a decrease in gastric juice pH. In conclusion, these findings suggest that our test compound exhibits potent antinociceptive, anti-inflammatory properties and is devoid of ulcerogenic effects.

Vermicomposting technology as a dynamic strategy to mitigate environmental crisis: a bibliometric study of last three decades.

Efficient recycling of resources forms the cornerstone of sustainable development. Among multiple options in stock for waste recycling, vermicomposting technology is regarded as a futuristic strategy, being tested in every part of the globe due to easy accessibility. Hence, a bibliometric study was planned to set a sight on global scientific trends encompassing vermicomposting research in last three decades. The data were retrieved from Google Scholar, Scopus and PubMed. Publications from different search engines were filtered out and 2064 unique documents were collected and illustrated in MS Excel and Vos-viewer. Inferences were drawn on significant aspects, such as publication growth trend, journal analysis and co-occurrence of keywords. The study revealed that the number of publications increased from 3 in 1992 to 166 in 2021. The number of citations also increased and peaked at 4314 in 2015. Following this, we clustered keywords using principle component analysis and worked out links between domains of vermicomposting. Vermicomposting conjoined to words substrate manipulation, quality improvement, heavy metal adsorption, and yield parameters. This implies that vermicompost is being explored for many alternate uses in addition to its use as a fertiliser. We concluded that vermicomposting is one of the promising technologies for waste recycling. It modulates plant growth and subdues stress in plants. Additionally, being an efficient adsorbent, it serves bioremediation of contaminated sites. Therefore, the future of this technology lies in synthesising nano-formulations, integrating into biosensor technology, simulating for predicting timelines under different conditions and making efforts to improve their adsorption.

Effects of Exercise-based Management on Motor Symptoms in Parkinson's Disease - A Meta-analysis.

Parkinson's disease (PD) is the second most common neurological illness after Alzheimer's disease. According to research, medication alone can give palliative alleviation; however, freezing of gait (FOG) and balance can be treated with physical therapy. This meta-analysis aims to bridge gaps about exercise-based therapy's impact on balance and FOG in patients with PD. Google Scholar, CINHAL, Medline, PubMed, and PEDro were searched for 2016-2021 citations using the PIOD paradigm. Pooled effect size mean and SD were analysed using a fixed and random effects model. A total of 21 trials were included in this review, with SMD=0.60 and p=0.0007 utilising BBS. The pooled analysis revealed statistically significant impacts on exercise-based management in the experimental group. With SMD=0.87 and p<0.00001 using Mini-BESTest, the pooled analysis revealed that exercise-based management was also effective on balance in the experimental group. The fixed effect model of FOG in terms of SMD was used to draw the pooled effects of FOG in terms of SMD and FOG in terms of SMD (0.21; 95 percent CI -0.01 to 0.44; p=0.06). According to this research, several physiotherapy approaches such as exergaming, gamepad systems, virtual reality, gait exercises, and core training, help Parkinson's patients regain balance and FOG. Key Words: Parkinson's disease, Physical therapy techniques, Neurological rehabilitation, Balance, Freezing of gait, Motor symptoms.

Case of a WHO Grade II Atypical Meningioma in a 16-Year-Old Female.

Meningiomas have a high frequency of occurrence as primary intracranial tumors. We report the case of a 16-year-old female who presented with a three-week history of persistent headache, vomiting, and photophobia. Imaging studies revealed the presence of a meningioma in the right occipital lobe of the brain. The patient underwent surgical resection, and histopathological analysis confirmed the diagnosis of an atypical WHO grade 2 meningioma. The patient experienced a significant improvement in her symptoms postoperatively and had no evidence of recurrence on follow-up imaging. This case highlights the importance of considering meningioma in the differential diagnosis of relatively young patients presenting with chronic headaches, and the favorable prognosis associated with atypical WHO grade 2 meningiomas following complete surgical resection.

Seismic performance evaluation of plastered cellular lightweight concrete (CLC) block masonry walls.

The current research presents a novel and sustainable load-bearing system utilizing cellular lightweight concrete block masonry walls. These blocks, known for their eco-friendly properties and increasing popularity in the construction industry, have been studied extensively for their physical and mechanical characteristics. However, this study aims to expand upon previous research by examining the seismic performance of these walls in a seismically active region, where cellular lightweight concrete block usage is emerging. The study includes the construction and testing of multiple masonry prisms, wallets, and full-scale walls using a quasi-static reverse cyclic loading protocol. The behavior of the walls is analyzed and compared in terms of various parameters such as force-deformation curve, energy dissipation, stiffness degradation, deformation ductility factor, response modification factor, and seismic performance levels, as well as rocking, in-plane sliding, and out-of-plane movement. The results indicate that the use of confining elements significantly improves the lateral load capacity, elastic stiffness, and displacement ductility factor of the confined masonry wall in comparison to an unreinforced masonry wall by 102%, 66.67%, and 5.3%, respectively. Overall, the study concludes that the inclusion of confining elements enhances the seismic performance of the confined masonry wall under lateral loading.

Aortic valve surgery in the pediatric population: pathophysiology, clinical features and management.

There are several conditions where the function of the aortic valve can be compromised in the pediatric population. The aortic valve is composed of three leaflets which are thin and mobile and are attached to the aortic sinuses. Each leaflet is made up of connective tissue, forming a highly ordered network of extracellular matrix components. Together, this enables the aortic valve to open and close more than 100,000 times throughout the day. However, there are conditions where the structure of the aortic valve can be compromised resulting in its function being affected. Conditions such as congenital valvular aortic stenosis and abnormal valve morphology including bicuspid valves often necessitate intervention to improve symptoms and quality of life in children. Other conditions which result in requiring surgical intervention include infective endocarditis and trauma. In this article, we present the common forms of aortic valve disease in the pediatric population and the clinical presentation and pathophysiology of these. We also discuss the range of management options including medical management and percutaneous intervention. Surgical interventions such as Aortic annular enlargement techniques, the Ross procedure and the Ozaki procedure will also be discussed. The effectiveness, complications and long-term outcomes associated with these methods will be explored.