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This study highlighted the role played by digitalization elements, such as information and communication technology (ICT) adoption, the social internet of things (IoT), and artificial intelligence (AI), in e-learning systems. It also examined the mediating role of digital literacy (DL) and pedagogical digital competence (PDC) and the potential moderating role of technostress (TS). A total of 262 responses to survey questionnaires were received from the IT administrators who worked under the directorate of information technology in top QS ranking Malaysian universities; the survey data was then analyzed through PLS-SEM. The study findings revealed that ICT adoption and the social IoT considerably impact e-learning systems and highlighted that artificial intelligence also has a partial impact. Digital literacy and pedagogical digital competence also significantly mediated the relationships between ICT adoption, artificial intelligence, and e-learning systems. Social IoT and technostress did not show a significant impact. The study developed a new theoretical concept for adopting innovative technology in e-learning systems through 'Four Tier Technology Gears', such as start-up, boost-up, smooth-up, and speed-up technology gears. From a theoretical perspective, the study has built-up a theoretical affiliation with the TAM and UTAUT models, which are especially proposed for technology adoption in real-life. The study provides remarkable and valuable recommendations to researchers, designers, policymakers, IT instructors, and structural developers in the context of awareness of key facts for e-learning systems in practical life - especially in an emergency condition, such as the Covid-19 pandemic in Malaysia.

Abstract Background In this report, we describe an uncommon instance of fungating synovial sarcoma affecting the posterior aspect of the neck. Although the existing literature has documented a limited number of cases, this particular case contributes to the knowledge about it, which is scarce. Case presentation A total of 5 months before the examination, a Pakistani-Asian male, age 20 years, complained of a malodorous fungating swelling on the posterior aspect of his neck. An examination revealed a foul-smelling, 10 × 13 cm fungating enlargement surrounded by maggots and hemorrhaging at the site of the incision. A hemoglobin level of 6 and a total leukocyte count (TLC) of 23,000 indicated the patient’s disoriented and pallid appearance. He was expeditiously admitted, and preoperatively, the general well-being of the patient was optimized. After a comprehensive discussion with the medical team, a strategy for marginal excision and coverage with a latissimus dorsi (LD) flap and grafting was devised. The tumor was successfully excised, and an LD flap with graft was conducted on the patient during surgery; however, the infection caused the failure of half of the graft. Following that, the lesion was debrided, and re-grafting was performed. The patient was subsequently administered 5 cycles of chemotherapy and 32 cycles of radiotherapy. He was diagnosed with pulmonary metastasis 2 years later. Sadly, the patient died during a follow-up visit 3.5 years later. Conclusions The patient’s unfavorable prognosis after surgical intervention, radiotherapy, and chemotherapy, despite undergoing all-encompassing treatments, underscores the importance of early detection and intervention in fungating tumor cases.

The economy of a developing country is often industry centric, and is recognised globally. The circular economy (CE) model involves a manufacturing approach where products are systematically remanufactured and recycled within facilities to minimize waste. The emerging domain of CE in industrialised South Asian economies offers considerable benefits for developing countries due to its sustainable production, although its adoption is obstructed by various barriers. This research aims to develop a model for adopting CE within the industrial sector of South Asian developing nations by analysing the interrelation between overcoming circular economy adoption barriers (CEABs) and sustainable circular success (SCS). To attain this, questionnaire was completed by 310 industrial experts in Pakistan. Furthermore, a partial least square structural equation modelling (PLS-SEM) approach was employed to specify the barriers and inspect the interrelation between overcoming CEABs and SCS. The findings revealed a high correlation, with addressing the CEABs contributing 66.1 % to the SCS of the industrial sector. The results of this study indicate the outer loadings and average variance extracted values for all constructs surpassed the minimum threshold of 0.5, validating their acceptance. Moreover, the constructs in the study were measured reliably, exceeding a value of 0.8, which signify strong internal consistency. Additionally, the average path coefficient value (β) of 0.172 indicates a medium and positive correlation between CEABs and SCS. The study's findings can be used as a reference for policymakers to explore the primary barriers to CE adoption in developing nations and achieve SCS in industrial projects.

Objective: To assess the accuracy of ultrasound-guided core biopsy in staging axillary lymph nodes in clinically node-negative invasive breast cancer patients using histopathology as the gold standard. Methods: This cross-sectional study was conducted in the Department of Pathology at Mekran Medical College, Turbat, Pakistan, from April 2023 to September 2023. The study included breast cancer patients who were clinically negative for lymph node involvement. Ultrasound-guided core biopsies were performed on suspicious axillary lymph nodes by experienced radiologists, with histopathological analysis serving as the gold standard. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and overall accuracy were calculated to assess diagnostic performance. Patient demographics, tumor characteristics, and axillary ultrasound findings were also recorded. Results: A total of 132 invasive breast cancer patients were included, with a mean age of 57.67 ± 13.03 years. The majority of patients were diagnosed with invasive ductal carcinoma, accounting for 113 (85.6%) cases. Hormone receptor positivity was prevalent, with estrogen receptor positivity in 119 (90.2%) and progesterone receptor positivity in 116 (87.9%) cases. The most common immunohistochemistry subtype was Luminal B, present in 66 (50%) patients, followed by Luminal A in 51 (38.6%) patients. Most tumors were of moderate grade 77 (58.4%) and early-stage (pT1) 63 (47.7%). Ultrasound-guided core biopsy exhibited a sensitivity of 87.9% and specificity of 100%, with positive and negative predictive values of 100% and 89.2% respectively, resulting in a diagnostic accuracy of 93.9%. Conclusion: Ultrasound-guided core biopsy demonstrates strong diagnostic performance in identifying metastatic axillary lymph nodes in clinically node-negative invasive breast cancer patients.

This review article provides a comprehensive overview of the multidisciplinary management of giant cell tumors of the bone (GCTB). GCTB is a rare, intermediate-grade, benign, locally aggressive osteolytic neoplasm that primarily affects the epiphyses of long bones in individuals aged 20–45, with a female predominance. The article discusses the epidemiology, clinical features, and various management approaches for GCTB.The article highlights the importance of GCTB, which can resemble other bone lesions, so an accurate diagnosis is essential. A biopsy is a definitive way to diagnose GCTB. While, radiography includes MRI, PET scans, and bone scans to help diagnose and stage GCTB. The Campanacci grading system, based on radiographic appearance and soft tissue involvement, is commonly used to stage GCTB.Surgical management options for GCTB include intralesional curettage, wide marginal resection, and en-bloc resection, depending on the location and extent of the tumor. In challenging anatomical locations, such as the axial skeleton, intralesional curettage may be the only viable option to preserve function and minimize surgical morbidity. However, it is associated with a high rate of local recurrence. Wide marginal resection is effective in reducing recurrence rates but may result in significant morbidity and require bone defect reconstruction. En-bloc resection is preferred for tumors with soft tissue extension or involvement of expendable bones.In cases where complete surgical resection is not feasible or carries a high risk of morbidity, alternative treatment options such as radiation therapy, arterial embolization, and systemic therapy with bisphosphonates (zoledronic acid) and denosumab are valuable tools in achieving disease control and improving patient outcomes. Radiotherapy is employed for cases where the disease has recurred locally and cannot be removed surgically. Arterial embolization involves interrupting the blood supply to tumors in areas that cannot be operated on. Bisphosphonates are drugs that can be given before or after surgery to reduce the chances of recurrence by inhibiting osteoclast activity.Denosumab, a monoclonal antibody that targets RANKL, has shown promising results in reducing tumor size, facilitating surgical treatment, and improving outcomes. In conclusion, this review provides valuable insights into the epidemiology, clinical features, and multidisciplinary management approaches for GCTB. Accurate diagnosis, appropriate staging, and individualized treatment strategies are essential for optimizing patient outcomes in GCTB management.

Abstract Decentralised federated learning (DFL) transforms collaborative energy consumption prediction using distributed computation across a large network of edge nodes, ensuring data confidentiality by eliminating central data aggregation. Preserving individual privacy in energy forecasting is paramount, as it safeguards personal data from unauthorised examination. This highlights the importance of effectively handling local data to provide privacy protection. The authors proposed a DFL framework for residential energy forecasting, focusing on improving the performance and convergence of the collaborative model. The proposed framework enables local training of the long short‐term memory model with real‐time household energy data in a ring topology. Importantly, the framework addresses the issue of straggler nodes, nodes that lag in computation or communication, by proposing a heuristic straggler identification and mitigation mechanism to reduce their negative impact on overall system performance and communication efficiency. This approach improves collaborative energy prediction performance and ensures an overall reduction in waiting time, thus improving the convergence performance. Experimental results consistently demonstrate a low mean absolute error ranging from 3 to 3.2 across all edge nodes. The empirical findings unequivocally illustrate the efficiency of the proposed DFL architecture, highlighting its ability to improve communication efficiency and concurrently enhance performance.

Background: Vascular injuries in simple close fractures are rare and low suspicion risk for primary surgeons. They include thrombus formation, hematomas, pseudoaneurysms, ischemic changes, and absent pulsations. These injuries can cause morbid sequelae, including compartment syndrome, reperfusion injury, exsanguination, and limb loss. Case presentation: Following a closed femoral shaft fracture, a 12-year-old boy came to us with a compromised superficial femoral artery (SFA). At the moment of fixing, there was no evidence of arterial involvement. He presented to the Out-patient Department (OPD) two weeks after the primary surgery with swelling at the fracture site. CT-Angiography was performed, and the swelling was diagnosed as a pseudoaneurysm of the superficial femoral artery, which was repaired using a Great Saphenous Vein (GSV) graft. Conclusion: These uncommon vascular injuries in closed fractures can be identified by a thorough clinical examination and specific diagnostic tests, and with prompt vascular repair, limb-threatening consequences are preventable. In a simple close femur fracture without suspicious signs in clinical findings, it's not a routine practice to check the vascular status by means of fancy and costly diagnostic tests.

ABSTRACT Background: Anxiety and depression are considered as an important mental health indicators in the community. Medical undergraduates face several emotional, mental and physical stresses during the academic years. Researches indicate that medical students experience a large amount of psychological pressure due to work required in a competitive environment, extreme working hours, examination stress, large amount of information to seek, lack of social life, responsibility of human welfare. Anxiety and depression ranks fourth as the leading cause of disability globally. Several studies suggest high prevalence depression and anxiety among medical students with distress levels consistently higher than general population. The objective was to assess the level of hypochondriasis among the medical and dental student from first year to final year. Methodology: A cross sectional survey was conducted amongst the students of medical and dental college at Ziauddin University. The study included total 404 students from both disciplines, a pre-structured questionnaire “The Illness Altitude Scale- IAS” was given to the students after validation. SPSS version 17 was used for data analysis and p value >0.05 was considered significant. Results: Of the total (n=450) 404 students completed the questionnaire. The average mean age was 22.05±2.6. Of the total (n=164) dental students, 10 students had mild, 83 moderate and 71 had severe hypochondriasis. Among (n=240) medical students, 13 had mild, 153 moderate and 74 had severe hypochondriasis. Conclusion: These finding confirms the presence of hypochondriasis among the medical and dental undergraduate students, which increases from mild to severe from initial to final years.

Accurate long-range object detection is essential for applications such as security and surveillance. However, existing datasets often lack the complexity needed to represent real-world outdoor environments, resulting in limited performance of object detection algorithms at extended distances.Synthetic data generation offers a way to address these limitations by creating varied and realistic training scenarios. To address these limitations, we propose a novel approach utilizing BlenderProc procedural generation and photorealistic rendering to create a synthetic dataset that captures diverse and realistic outdoor scenes for the objects detection. We trained YOLO model on this dataset and compared its performance with standard YOLO model. Our approach achieved a precision of 94% and recall of 96% for detecting objects at distances exceeding 120 meters, demonstrating significant improvements over existing methods. These findings underscore the potential of advanced synthetic data generation techniques to enhance long-range object detection and address critical challenges in surveillance, remote sensing, and autonomous systems.

The previous centralized machine learning methods required combining large amounts of personal data on central servers, raising privacy concerns. Federated Learning (FL) presents a solution by training the models directly on users’ devices, thus preserving data privacy. This paper proposes the use of FL in sarcasm detection and applying neural network architectures such as Bidirectional Long Short-Term Memory (BILSTM), Gated Recurrent Unit (GRU), and Long Short-Term Memory (LSTM). The experiments have been performed on multiple clients. The results have shown that BILSTM outperforms GRU and LSTM in terms of accuracy, precision, recall, and F1 score. This makes it the most effective model for sarcasm detection in a FL. Apart from evaluating these models, this study also examined how their functionality may be further improved by integrating them with attention processes. The current study results show that attention-based models can significantly improve performance by focusing on the input parts that are more contextually relevant. Based on this work, future research could focus on improving the performance of these models and exploring hybrid approaches that combine the benefits of LSTM, GRU, and BILSTM.

Monitoring wind turbine performance is vital for ensuring wind turbines’ safe, efficient, and cost-effective operation over time. Using principal component analysis (PCA), k-means clustering for labeling, and an ensemble classifier for finding outliers, this study suggests a new way to find anomalies in wind turbines. The primary objective is to improve the precision of anomaly detection in wind turbines by leveraging machine-learning techniques. The proposed methodology utilizes the output of the PCA-Kmeans model to label supervisory control and data acquisition (SCADA) data. Furthermore, a stacking ensemble classifier is employed to refine the model’s precision. Our proposed model achieved a classification accuracy of 99%, which is a significant improvement compared to existing approaches. The significance of this study lies in its potential to enable more efficient wind turbine operation by identifying and resolving anomalies that may reduce their performance. This can ultimately contribute to achieving a sustainable and renewable energy future.

Sugar in the blood can harm individuals and their vital organs, potentially leading to blindness, renal illness, as well as kidney and heart diseases. Globally, diabetic patients face an average annual mortality rate of 38%. This study employs Chi-square, mutual information, and sequential feature selection (SFS) to choose features for training multiple classifiers. These classifiers include an artificial neural network (ANN), a random forest (RF), a gradient boosting (GB) algorithm, Tab-Net, and a support vector machine (SVM). The goal is to predict the onset of diabetes at an earlier age. The classifier, developed based on the selected features, aims to enable early diagnosis of diabetes. The PIMA and early-risk diabetes datasets serve as test subjects for the developed system. The feature selection technique is then applied to focus on the most important and relevant features for model training. The experiment findings conclude that the ANN exhibited a spectacular performance in terms of accuracy on the PIMA dataset, achieving a remarkable accuracy rate of 99.35%. The second experiment, conducted on the early diabetes risk dataset using selected features, revealed that RF achieved an accuracy of 99.36%. Based on our experimental results, it can be concluded that our suggested method significantly outperformed baseline machine learning algorithms already employed for diabetes prediction on both datasets.

Abstract Solar energy is a very efficient alternative for generating clean electric energy. However, pollution on the surface of solar panels reduces solar radiation, increases surface transmittance, and raises the surface temperature. All these factors cause photovoltaic (PV) panels to be less efficient. To address this problem, a stacking ensemble classifier-based machine learning model is proposed. In this study, different sources of pollution on each solar panel are used, and their power generation is recorded. The proposed model includes gradient boost, extra tree, and random forest classifiers, with the extra tree classifier serving as a meta-learner. The model takes into account various weather features during the training process, including irradiance and temperature, aiming to increase its accuracy and robustness in identifying pollution sources on the PV panel. Moreover, the proposed model is evaluated using various methods in order to examine performance metrics such as accuracy, F1 score, and precision. Results show that the model can achieve an accuracy score of 97.37%. The model’s performance is also compared to state-of-the-art machine learning models, demonstrating its superiority in accurately classifying pollution sources on PV panels. By utilizing different sources of pollution and weather features during training, the model can accurately classify different pollution sources, resulting in increased power generation efficiency and the longevity of PV panels. The main results of this study can be used to manage and maintain PV panels since the model can identify PV modules that need to be cleaned to keep producing the most power. Furthermore, the efficiency, reliability, and sustainability of PV panels can be further enhanced by the proposed model.

Graphene oxide (GO) nanosheets, as one of the most studied graphene derivatives, have demonstrated an intrinsically strong physisorption-based gas–matter behavior, owing to its enhanced volume–surface ratio and abundant surface functional groups. The exploration of efficient and cost-effective synthesis methods for GO is an ongoing task. In this work, we explored a novel approach to upcycle inexpensive polyethylene terephthalate (PET) plastic waste into high-quality GO using a combination of chemical and thermal treatments based on a montmorillonite template. The obtained material had a nanosheet morphology with a lateral dimension of around ~2 µm and a thickness of ~3 nm. In addition, the GO nanosheets were found to be a p-type semiconductor with a bandgap of 2.41 eV and was subsequently realized as a gas sensor. As a result, the GO sensor exhibited a fully reversible sensing response towards ultra-low-concentration NO2 gas with a limit of detection of ~1.43 ppb, without the implementation of an external excitation stimulus including elevating the operating temperature or bias voltages. When given a thorough test, the sensor maintained an impressive long-term stability and repeatability with little performance degradation after 5 days of experiments. The response factor was estimated to be ~11% when exposed to 1026 ppb NO2, which is at least one order of magnitude higher than that of other commonly seen gas species including CH4, H2, and CO2.

Intrusion Detection Systems (IDSs) play a crucial role in safeguarding network infrastructures from cyber threats and ensuring the integrity of highly sensitive data. Conventional IDS technologies, although successful in achieving high levels of accuracy, frequently encounter substantial model bias. This bias is primarily caused by imbalances in the data and the lack of relevance of certain features. This study aims to tackle these challenges by proposing an advanced machine learning (ML) based IDS that minimizes misclassification errors and corrects model bias. As a result, the predictive accuracy and generalizability of the IDS are significantly improved. The proposed system employs advanced feature selection techniques, such as Recursive Feature Elimination (RFE), sequential feature selection (SFS), and statistical feature selection, to refine the input feature set and minimize the impact of non-predictive attributes. In addition, this work incorporates data resampling methods such as Synthetic Minority Oversampling Technique and Edited Nearest Neighbor (SMOTE_ENN), Adaptive Synthetic Sampling (ADASYN), and Synthetic Minority Oversampling Technique–Tomek Links (SMOTE_Tomek) to address class imbalance and improve the accuracy of the model. The experimental results indicate that our proposed model, especially when utilizing the random forest (RF) algorithm, surpasses existing models regarding accuracy, precision, recall, and F Score across different data resampling methods. Using the ADASYN resampling method, the RF model achieves an accuracy of 99.9985% for botnet attacks and 99.9777% for Man-in-the-Middle (MITM) attacks, demonstrating the effectiveness of our approach in dealing with imbalanced data distributions. This research not only improves the abilities of IDS to identify botnet and MITM attacks but also provides a scalable and efficient solution that can be used in other areas where data imbalance is a recurring problem. This work has implications beyond IDS, offering valuable insights into using ML techniques in complex real-world scenarios.

Abstract Water electrolysis to produce hydrogen has been viewed as the future of green energy. The reliance of current electrolyzer technologies on ultrapure fresh water is not feasible to meet increasing hydrogen demands. The abundant seawater emerged as the potential feedstock for water electrolysis, but its complex composition and chlorine chemistry make the process complicated. Recently designed effective catalysts have given hope for direct seawater splitting but need to be optimized for commercial use. Moreover, the inability of current electrolyzer technologies with seawater requires efforts to establish this technology. In short, freshwater as feed for water electrolysis is not sustainable; the focus must be on seawater. While desalination seems to be a viable option, the economics of scale of the plant is critical in assessing its viability. Although the complex composition of seawater makes direct seawater splitting challenging, designing effective catalysts and durable electrolyzers can make it possible for a truly sustainable hydrogen industry.

This study examines factors hindering employee engagement, focusing on perceived workplace incivility and its relationship with work engagement. It also presents the mediating and moderating roles of emotional responses and coping mechanisms in the workplace to determine “why” and “when” employee engagement is destabilized. The cross-sectional study used 391 samples from the hotel industry; data were collected from the crew staff via self-administered questionnaires and analyzed using Smart PLS. The study’s outcome draws attention to perceived workplace incivility and its significant negative impact on work engagement. The results also reveal that employees’ emotional responses and coping mechanisms play significant mediator and moderator roles in the relationship between workplace incivility and work engagement. In terms of theoretical background, the study makes a novel theoretical contribution by applying Affective Events Theory (AET) when employees confront incivility that triggers emotional reactions and then shows how those reactions lead to adverse work outcomes and behaviors. The study outcomes have significant implications for strategic hotel industry staff and policymakers and provide insights for future researchers. Hotel management might consider this study helpful for generating a crew-supportive work environment and stabilizing employee engagement.

OBJECTIVES To determine the frequency of the Hepatitis C Virus in patients with chronic kidney disease receiving regular hemodialysis. METHODOLOGY At a tertiary care hospital in Peshawar, 211 patients were observed to ascertain the frequency of the Hepatitis C Virus in chronic kidney disease patients receiving regular hemodialysis. The technique of non-probability sequential sampling was employed to acquire samples. SPSS version 26.0 was used. RESULTS Among the CKD patients with Hepatitis C Virus (HCV) infection, a higher proportion of males (48.5%) tested positive compared to females (28.1%). Furthermore, the prevalence of HCV infection was significantly higher in CKD patients residing in urban areas (98.5%) compared to those in rural areas (1.5%). The statistical analysis indicated a significant association between the prevalence of Hepatitis and the factors of gender and residency (P value = 0.002). Out of 211, hepatitis C-positive patients 66.2% were diabetics and hypertensive were 77.9%. CONCLUSION Hospitalized CKD patients have a considerably higher frequency of hepatitis C infection. To stop the spread of the hepatitis C virus, nephrology wards should undertake strict universal infection control procedures.

Background Clavicle fracture (CF) is the tenth most prevalent fracture, accounting for an annual incidence of 37/10,000. This systematic review highlights the factors contributing to the nonunion union of the clavicular fracture.Method A systematic search was conducted using three web-based databases up to August 12, 2022, for conducting qualitative analysis. Articles were screened for relevance, and only studies that met inclusion criteria based on PECOS; P (patients): participants diagnosed with clavicular fracture; E (exposure): nonunion, C (control): not applicable; O (outcomes): factors contributing to nonunion or delayed union; S (studies): trials and observational studies. The Newcastle-Ottawa Scale was used to assess the quality of the cohort studies. The Cochrane risk of bias tool was used to assess the bias in randomized control trials.Results Ten studies were selected after the final literature search. Two thousand seven hundred and sixty-six adult participants who were radiologically and clinically diagnosed with nonunion clavicular fracture were included to pool the qualitative results. Fall was the most dominant cause of clavicular fracture, followed by road traffic collisions. Open reduction was widely used to treat nonunion correction. The qualitative results suggested a prominent correlation of nonunion with advancing age, female gender, high energy trauma, high Disabilities of the Arm, Shoulder, and Hand Score, smoking, fracture displacement, clavicular shortening, the callus on radiography, and fracture movement. The mid-shaft fracture was the most dominant type of fracture in the included studies; highly associated with nonunion in comparison to medial or lateral CF. The previous history of operation was an independent factor contributing to nonunion.Conclusion The results of this systematic review suggested the predictors contributing to nonunion in the CF. Demographic factors such as advancing age with female gender are at higher risk of developing clavicular nonunion. Smoking was the most dominantly highlighted environmental factor contributing to nonunion. Diaphyseal or midshaft fracture was the most common site for nonunion. Therefore, we suggested that patients with the predictors mentioned above require special attention to prevent nonunion of the CFs. More studies should be conducted on this subject to assess the factors that pose a risk associated with the nonunion of the bone for better clinical management and outcomes of the fracture.

Production-oriented firms need operational as well as financial working capital to sustain the business in a competitive environment. However, firms in underdeveloped countries face many issues in meeting the capital and revenue nature expenditure. Due to this issue these firms arrange capital from other sources like seed money. The financial working capital is linked with working capital components. Working capital is performing as an intermediary role between financial working capital and the capital structure of the firms. This study is proved that working capital is playing an intermediary role between financial working capital and capital structure of textile sector firms through empirical findings and as well as used GLS, Random Effect, Fixed Effect and GMM Techniques.

Presents corrections to the paper, (Correction to “Detecting Anomaly Classification Using PCA-Kmeans and Ensembled Classifier for Wind Turbines”).

OBJECTIVES Haemodialysis (HD) is a form of renal replacement therapy commonly prescribed for chronic kidney disease (CKD) patients. However, it is not without deleterious haemodynamic responses which may occur either during or immediately after the termination of the procedure. These may include hypotension or hypertension. The objective of this study was to determine the frequency of intradialytic blood pressure changes in chronic kidney disease patients on maintenance hemodialysis. METHODOLOGY Patients with chronic kidney disease on maintenance hemodialysis were enrolled. Predialysis and immediate post dialysis blood pressure was recorded. Increase in postdialysis blood pressure by >20mmHg was called intradialytic hypertension and decrease by >20mmHg was called intradialytic hypotension. RESULTS A total of 203 patients on maintenance hemodialysis were enrolled. Age of the patients ranged from 18 to 50 years. Mean age of the patients was 39.80 ± 5.245 years. Male to female ratio was 1.8:1. Intradialytic hypotension was observed in 36 patients (17.7%) while intradialytic hypertension was observed in 22 patients (10.8%). CONCLUSION Intradialysis blood pressure fluctuations such as hypotension and hypertension are common complications during hemodialysis. Intradialytic hypotension is more frequent.

The use of periprocedural dual antiplatelet therapy (DAPT) has significantly evolved along with innovations in the endovascular management of intracranial aneurysms. Historically, aspirin and clopidogrel have been the most commonly employed regimen due to its safety and efficacy. However, recent studies highlight the importance of tailoring DAPT regimens to individual patient characteristics which may affect clopidogrel metabolism, such as genetic polymorphisms. In the present report, a systematic review of the literature was performed to determine optimal antiplatelet use with flow diverting stents, intracranial stents, intrasaccular devices, and stent-assisted coiling. Studies were analyzed for the number of aneurysms treated, DAPT regimen, and any thromboembolic complications. Based on inclusion criteria, 368 studies were selected, which revealed the increasing popularity of alternative DAPT regimens with the aforementioned devices. Thromboembolic or hemorrhagic complications associated with antiplatelet medications were similar across all medications. DAPT with ticagrelor, tirofiban, or prasugrel are effective and safe alternatives to clopidogrel and do not require enzymatic activation. Further clinical trials are needed to evaluate different antiplatelet regimens with various devices to establish highest-level evidence-based guidelines and recommendations.