A Comparison of Systematic, Targeted and Combined Biopsy Using Machine Learning for Prediction of Prostate Cancer Risk: A Multi-Center Study.

OBJECTIVES: To construct a new prognostic prediction model for detecting Prostate Cancer (PCa) patients using Machine Learning (ML) techniques and to compare those models across systematic and target biopsy detection techniques. METHODS: The records of the two main hospitals in Riyadh, Saudi Arabia, were analyzed for data on diagnosed PCa from 2019 - 2023. Four ML algorithms were utilized for the prediction and classification of PCa. RESULTS: 528 patients with prostate-specific antigen (PSA) greater than 3.5 ng/mL who had undergone transrectal ultrasound-guided prostate biopsy (TRUS) were evaluated. The total number of confirmed PCa cases was 234. Age, prostate volume, PSA, Body Mass Index (BMI), multiparametric Magnetic Resonance Imaging (mpMRI) score, number of regions of interest detected in MRI, and the diameter of the largest size lesion were significantly associated with PCa. Random Forest (RF) and XG Boost (XGB) (ML algorithms) accurately predicted PCa. Yet, their performance for classification and prediction of PCa was higher and more accurate for cases detected by targeted and combined biopsy (systematic and targeted together) compared to systematic biopsy alone. F1, the Area Under the Curve (AUC), and the accuracy of XGB and RF models for targeted biopsy and combined biopsy ranged from 0.94 - 0.97 compared to the AUC of systematic biopsy for RF and XGB algorithms, respectively. CONCLUSIONS: The Random Forest (RF) model generated and presented an excellent prediction capability for the risk of PCa detected by targeted and combined biopsy compared to systematic biopsy alone. ML models can prevent missed PCa diagnoses by serving as a screening tool.

Securing Smart Healthcare Cyber-Physical Systems against Blackhole and Greyhole Attacks Using a Blockchain-Enabled Gini Index Framework.

The increasing reliance on cyber-physical systems (CPSs) in critical domains such as healthcare, smart grids, and intelligent transportation systems necessitates robust security measures to protect against cyber threats. Among these threats, blackhole and greyhole attacks pose significant risks to the availability and integrity of CPSs. The current detection and mitigation approaches often struggle to accurately differentiate between legitimate and malicious behavior, leading to ineffective protection. This paper introduces Gini-index and blockchain-based Blackhole/Greyhole RPL (GBG-RPL), a novel technique designed for efficient detection and mitigation of blackhole and greyhole attacks in smart health monitoring CPSs. GBG-RPL leverages the analytical prowess of the Gini index and the security advantages of blockchain technology to protect these systems against sophisticated threats. This research not only focuses on identifying anomalous activities but also proposes a resilient framework that ensures the integrity and reliability of the monitored data. GBG-RPL achieves notable improvements as compared to another state-of-the-art technique referred to as BCPS-RPL, including a 7.18% reduction in packet loss ratio, an 11.97% enhancement in residual energy utilization, and a 19.27% decrease in energy consumption. Its security features are also very effective, boasting a 10.65% improvement in attack-detection rate and an 18.88% faster average attack-detection time. GBG-RPL optimizes network management by exhibiting a 21.65% reduction in message overhead and a 28.34% decrease in end-to-end delay, thus showing its potential for enhanced reliability, efficiency, and security.

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.

Optimal Path Routing Protocol for Warning Messages Dissemination for Highway VANET.

In vehicular ad hoc networks (VANETs), helpful information dissemination establishes the foundation of communication. One of the significant difficulties in developing a successful dissemination system for VANETs is avoiding traffic fatalities. Another essential success metric is the transfer of reliable and secure warning messages through the shortest path, particularly on highways with high mobility. Clustering vehicles is a general solution to these challenges, as it allows warning alerts to be re-broadcast to nearby clusters by fewer vehicles. Hence, trustworthy cluster head (CH) selections are critical to decreasing the number of retransmissions. In this context, we suggest a clustering technique called Optimal Path Routing Protocol for Warning Messages (OPRP) for dissemination in highway VANETs. OPRP relies on mobility measured to reinforce cluster creation, evade transmission overhead, and sustain message authenticity in a high mobility environment. Moreover, we consider communication between the cluster heads to reduce the number of transmissions. Furthermore, the cluster head is chosen using the median technique based on an odd or even number of vehicles for a stable and lengthy cluster life. By altering traffic densities and speeds, OPRP is compared with prominent schemes. Simulation results revealed that OPRP offers enhanced throughput, end-to-end delay, maximizing packet delivery ratio, and message validity.

Two-Hop Monitoring Mechanism Based on Relaxed Flow Conservation Constraints against Selective Routing Attacks in Wireless Sensor Networks.

In this paper, we investigate the problem of selective routing attack in wireless sensor networks by considering a novel threat, named the upstream-node effect, which limits the accuracy of the monitoring functions in deciding whether a monitored node is legitimate or malicious. To address this limitation, we propose a one-dimensional one-class classifier, named relaxed flow conservation constraint, as an intrusion detection scheme to counter the upstream node attack. Each node uses four types of relaxed flow conservation constraints to monitor all of its neighbors. Three constraints are applied by using one-hop knowledge, and the fourth one is calculated by monitoring two-hop information. The latter is obtained by proposing two-hop energy-efficient and secure reporting scheme. We theoretically analyze the security and performance of the proposed intrusion detection method. We also show the superiority of relaxed flow conservation constraint in defending against upstream node attack compared to other schemes. The simulation results show that the proposed intrusion detection system achieves good results in terms of detection effectiveness.

A Blockchain-Based Multi-Mobile Code-Driven Trust Mechanism for Detecting Internal Attacks in Internet of Things.

A multitude of smart things and wirelessly connected Sensor Nodes (SNs) have pervasively facilitated the use of smart applications in every domain of life. Along with the bounties of smart things and applications, there are hazards of external and internal attacks. Unfortunately, mitigating internal attacks is quite challenging, where network lifespan (w.r.t. energy consumption at node level), latency, and scalability are the three main factors that influence the efficacy of security measures. Furthermore, most of the security measures provide centralized solutions, ignoring the decentralized nature of SN-powered Internet of Things (IoT) deployments. This paper presents an energy-efficient decentralized trust mechanism using a blockchain-based multi-mobile code-driven solution for detecting internal attacks in sensor node-powered IoT. The results validate the better performance of the proposed solution over existing solutions with 43.94% and 2.67% less message overhead in blackhole and greyhole attack scenarios, respectively. Similarly, the malicious node detection time is reduced by 20.35% and 11.35% in both blackhole and greyhole attacks. Both of these factors play a vital role in improving network lifetime.

An Efficient Routing Protocol Based on Stretched Holding Time Difference for Underwater Wireless Sensor Networks.

Underwater Wireless Sensors Networks (UWSNs) use acoustic waves as a communication medium because of the high attenuation to radio and optical waves underwater. However, acoustic signals lack propagation speed as compared to radio or optical waves. In addition, the UWSNs also pose various intrinsic challenges, i.e., frequent node mobility with water currents, high error rate, low bandwidth, long delays, and energy scarcity. Various UWSN routing protocols have been proposed to overcome the above-mentioned challenges. Vector-based routing protocols confine the communication within a virtual pipeline for the sake of directionality and define a fixed pipeline radius between the source node and the centerline station. Energy-Scaled and Expanded Vector-Based Forwarding (ESEVBF) protocol limits the number of duplicate packets by expanding the holding time according to the propagation delay, and thus reduces the energy consumption via the remaining energy of Potential Forwarding Nodes (PFNs) at the first hop. The holding time mechanism of ESEVBF is restricted only to the first-hop PFNs of the source node. The protocol fails when there is a void or energy hole at the second hop, affecting the reliability of the system. Our proposed protocol, Extended Energy-Scaled and Expanded Vector-Based Forwarding Protocol (EESEVBF), exploits the holding time mechanism to suppress duplicate packets. Moreover, the proposed protocol tackles the hidden terminal problem due to which a reasonable reduction in duplicate packets initiated by the reproducing nodes occurs. The holding time is calculated based on the following four parameters: (i) the distance from the boundary of the transmission area relative to the PFNs' inverse energy at the 1st and 2nd hop, (ii) distance from the virtual pipeline, (iii) distance from the source to the PFN at the second hop, and (iv) distance from the first-hop PFN to its destination. Therefore, the proposed protocol stretches the holding time difference based on two hops, resulting in lower energy consumption, decreased end-to-end delay, and increased packet delivery ratio. The simulation results demonstrate that compared to ESEVBF, our proposed protocol EESEVBF experiences 20.2 % lesser delay, approximately 6.66 % more energy efficiency, and a further 11.26 % reduction in generating redundant packets.

Blockchain and Random Subspace Learning-Based IDS for SDN-Enabled Industrial IoT Security.

The industrial control systems are facing an increasing number of sophisticated cyber attacks that can have very dangerous consequences on humans and their environments. In order to deal with these issues, novel technologies and approaches should be adopted. In this paper, we focus on the security of commands in industrial IoT against forged commands and misrouting of commands. To this end, we propose a security architecture that integrates the Blockchain and the Software-defined network (SDN) technologies. The proposed security architecture is composed of: (a) an intrusion detection system, namely RSL-KNN, which combines the Random Subspace Learning (RSL) and K-Nearest Neighbor (KNN) to defend against the forged commands, which target the industrial control process, and (b) a Blockchain-based Integrity Checking System (BICS), which can prevent the misrouting attack, which tampers with the OpenFlow rules of the SDN-enabled industrial IoT systems. We test the proposed security solution on an Industrial Control System Cyber attack Dataset and on an experimental platform combining software-defined networking and blockchain technologies. The evaluation results demonstrate the effectiveness and efficiency of the proposed security solution.

Key Agreement Schemes in Wireless Body Area Networks: Taxonomy and State-of-the-Art.

Advances in wearable and implantable biosensors have enabled the applicability and usability of wireless body area networks (WBANs). A WBAN allows biosensors to collect and communicate human physiological data using wireless communication. The communication security of the collected data in WBAN is a major concern. Because of the dependability of cryptographic schemes for key management, these have become an important aspect of this security. However, the extremely constrained nature of biosensors has made designing key management schemes a challenging task. For this reason, many lightweight key management schemes have been proposed to overcome these constraints. In this article, we present a review of the state of the art of these solutions. We classify the WBAN schemes into three classes and evaluate them based on adequate metrics for key management in WBAN.

A broadcast-based key agreement scheme using set reconciliation for wireless body area networks.

Information and communication technologies have thrived over the last few years. Healthcare systems have also benefited from this progression. A wireless body area network (WBAN) consists of small, low-power sensors used to monitor human physiological values remotely, which enables physicians to remotely monitor the health of patients. Communication security in WBANs is essential because it involves human physiological data. Key agreement and authentication are the primary issues in the security of WBANs. To agree upon a common key, the nodes exchange information with each other using wireless communication. This information exchange process must be secure enough or the information exchange should be minimized to a certain level so that if information leak occurs, it does not affect the overall system. Most of the existing solutions for this problem exchange too much information for the sake of key agreement; getting this information is sufficient for an attacker to reproduce the key. Set reconciliation is a technique used to reconcile two similar sets held by two different hosts with minimal communication complexity. This paper presents a broadcast-based key agreement scheme using set reconciliation for secure communication in WBANs. The proposed scheme allows the neighboring nodes to agree upon a common key with the personal server (PS), generated from the electrocardiogram (EKG) feature set of the host body. Minimal information is exchanged in a broadcast manner, and even if every node is missing a different subset, by reconciling these feature sets, the whole network will still agree upon a single common key. Because of the limited information exchange, if an attacker gets the information in any way, he/she will not be able to reproduce the key. The proposed scheme mitigates replay, selective forwarding, and denial of service attacks using a challenge-response authentication mechanism. The simulation results show that the proposed scheme has a great deal of adoptability in terms of security, communication overhead, and running time complexity, as compared to the existing EKG-based key agreement scheme.

Melanoma identification and classification model based on fine-tuned convolutional neural network.

Background: Breakthroughs in skin cancer diagnostics have resulted from recent image recognition and Artificial Intelligence (AI) technology advancements. There has been growing recognition that skin cancer can be lethal to humans. For instance, melanoma is the most unpredictable and terrible form of skin cancer. Materials and Methodology: This paper aims to support Internet of Medical Things (IoMT) applications by developing a robust image classification model for the early detection of melanoma, a deadly skin cancer. It presents a novel approach to melanoma detection using a Convolutional Neural Network (CNN)-based method that employs image classification techniques based on Deep Learning (DL). We analyze dermatoscopic images from publicly available datasets, including DermIS, DermQuest, DermIS&Quest, and ISIC2019. Our model applies convolutional and pooling layers to extract meaningful features, followed by fully connected layers for classification. Results: The proposed CNN model achieves high accuracy demonstrates the model's effectiveness in distinguishing between malignant and benign skin lesions. We developed deep features and used transfer learning to improve the categorization accuracy of medical images. Soft-max classification layer and support vector machine have been used to assess the classification performance of deep features. The proposed model's efficacy is rigorously evaluated using benchmark datasets: DermIS, DermQuest, and ISIC2019, having 621, 1233, and 25000 images, respectively. Its performance is compared to current best practices showing an average of 5% improved detection accuracy in DermIS, 6% improvement in DermQuest, and 0.81% in ISIC2019 datasets. Conclusion: Our study showcases the potential of CNN in melanoma detection, contributing to early diagnosis and improved patient outcomes. The developed model proves its capability to aid dermatologists in accurate decision-making, paving the way for enhanced skin cancer diagnosis.

Unveiling the Significance of MRP 1 in Achieving Complete Remission Following Induction Therapy in Acute Myeloid Leukemia.

BACKGROUND: Increased expression of MRP 1 in AML patients results in the efflux of drugs from the cells, preventing the patient from achieving remission or potentially leading to relapse. Several studies have demonstrated that early identification of ABC transporter may yield favorable outcomes. AIMS AND OBJECTIVES: The objectives of the study were to investigate the correlation between MRP 1 gene expression and MRP 1 protein levels and the response to remission induction in AML patients. METHOD: A total of 40 AML patients were recruited from March 2021 to June 2022. Peripheral blood was collected in two tubes (yellow and purple top) to assess the MRP 1 gene and protein. For MRP 1 gene assessment, RNA was isolated from blood samples, cDNA was prepared, and qRT-PCR was performed to analyze gene expression. The relationship between the gene and complete remission was determined. Identification of MRP 1 protein was conducted using ELISA, and the relationship between protein levels and complete remission (CR) was explored. RESULTS: Most of the patients were aged between 25 and 39 years, encompassing both males and females. This study observed a clinical correlation between MRP 1 gene expression and complete remission. The findings revealed that 69.2 percent of patients with high gene expression failed to achieve complete remission, whereas the analysis of MRP 1 protein in relation to complete remission showed no statistical significance. The MRP1 gene showed high expression (66.7%) in patients with FLT3 mutation, whereas low expression of MRP1 was associated with a high occurrence (60%) of NMP1 mutation. CONCLUSION: Further comprehensive multicenter studies with larger sample sizes are required to validate the findings of this study. It is recommended to pinpoint the mechanism and regulation of MRP 1 and its interaction with other molecular pathways.

The characteristics and predictors of mortality in periprosthetic fractures around the knee.

Aims: Periprosthetic fractures (PPFs) around the knee are challenging injuries. This study aims to describe the characteristics of knee PPFs and the impact of patient demographics, fracture types, and management modalities on in-hospital mortality. Methods: Using a multicentre study design, independent of registry data, we included adult patients sustaining a PPF around a knee arthroplasty between 1 January 2010 and 31 December 2019. Univariate, then multivariable, logistic regression analyses were performed to study the impact of patient, fracture, and treatment on mortality. Results: Out of a total of 1,667 patients in the PPF study database, 420 patients were included. The in-hospital mortality rate was 6.4%. Multivariable analyses suggested that American Society of Anesthesiologists (ASA) grade, history of peripheral vascular disease (PVD), history of rheumatic disease, fracture around a loose implant, and cerebrovascular accident (CVA) during hospital stay were each independently associated with mortality. Each point increase in ASA grade independently correlated with a four-fold greater mortality risk (odds ratio (OR) 4.1 (95% confidence interval (CI) 1.19 to 14.06); p = 0.026). Patients with PVD have a nine-fold increase in mortality risk (OR 9.1 (95% CI 1.25 to 66.47); p = 0.030) and patients with rheumatic disease have a 6.8-fold increase in mortality risk (OR 6.8 (95% CI 1.32 to 34.68); p = 0.022). Patients with a fracture around a loose implant (Unified Classification System (UCS) B2) have a 20-fold increase in mortality, compared to UCS A1 (OR 20.9 (95% CI 1.61 to 271.38); p = 0.020). Mode of management was not a significant predictor of mortality. Patients managed with revision arthroplasty had a significantly longer length of stay (median 16 days; p = 0.029) and higher rates of return to theatre, compared to patients treated nonoperatively or with fixation. Conclusion: The mortality rate in PPFs around the knee is similar to that for native distal femur and neck of femur fragility fractures. Patients with certain modifiable risk factors should be optimized. A national PPF database and standardized management guidelines are currently required to understand these complex injuries and to improve patient outcomes.

False-positive magnetic resonance imaging prostate cancer correlates and clinical implications.

Background: False-positive (FP) multiparametric magnetic resonance imaging (MPMRI) obscures and swift needless biopsies in men with a high prostate-specific antigen. Materials and Methods: This was a retrospective study, in which all patients who had been exposed to consecutive MP-MRI of the prostate combined with transrectal ultrasound-guided-magnetic resonance imaging fusion-guided prostate biopsy between 2017 and 2020 were involved in the study. The FP was measured as the number of biopsies that did not encompass prostate cancer divided by the whole number of biopsies. Results: The percentage of FP cases was 51.1%, the highest percentage was found in Prostate Imaging-Reporting and Data System (PI-RADs) 3 (37.7%) and the lowest was detected in PI-RAD 5 (14.5%). Those with FP biopsies are younger, and their total prostate antigen (PSA) and PSA density (PSAD) are significantly lesser. The area under the curve PSAD, age, and total PSA are 0.76, 0.74, and 0.69, respectively. An optimum PSAD value of 0.135 was chosen as a cutoff because it showed the highest sum of sensitivity and specificity, 68% and 69%, respectively. Conclusion: FP results of mpMRI were detected in more than half of our sample, more than one-third were presented in Pi-RAD3, improved imaging techniques to decrease FP rates are highly needed.

Prostate-specific Antigen Density as a Proxy for Predicting Prostate Cancer Severity: Is There Any Difference between Systematic and Targeted Biopsy?

Background: Prostate cancer screening with prostate-specific antigen (PSA) can result in unnecessary biopsies and overdiagnosis. Alternately, PSA density (PSAD) calculation may help support biopsy decisions; however, evidence of its usefulness is not concrete. Objective: To evaluate the predictive value of PSAD for clinically significant prostate cancer detection by systematic and MRI-targeted biopsies. Methods: This prospective study was conducted at two tertiary hospitals in Riyadh, Saudi Arabia, between December 2018 and November 2021. Patients suspected of prostate cancer were subjected to multi-parametric MRI, and for those with positive findings, systematic and targeted biopsies were performed. Clinically non-significant and significant prostate cancer cases were classified based on histopathology-defined ISUP grade or Gleason score. The PSAD was measured using the prostate volume determined by the MRI and categorized into ≤0.15, 0.16-0.20, and >0.20 ng/ml2 subgroups. Results: Systematic and targeted biopsies were carried out for 284 patients. The discriminant ability of PSAD is higher in MRI-targeted biopsy compared with systematic biopsy (AUC: 0.77 vs. 0.73). The highest sensitivity (97%) and specificity (87%) were detected at 0.07 ng/ml2 in targeted biopsy. More than half of the clinically significant cases were detected in the >0.2 ng/ml2 PSAD category (systematic: 52.4%; targeted: 51.1%). The CHAID methodology found that the probability of having clinically significant cancer (CSC) in patients with PSAD >0.15 ng/ml2 was more than threefold than that in patients with PSAD ≤0.15 ng/ml2 (64% vs. 20.2%). When considered by age, in PSAD ≤0.15 ng/ml2 subgroup, the percentage of CSC detection rate increased from 20.2% to 24.6% in patients aged ≥60 years. Conclusion: PSAD has good discriminant power for predicting clinically significant prostate cancer. A cutoff of 0.07 ng/ml2 should be adopted, but should be interpreted with caution and by considering other parameters such as age.

Haematopoietic Stem Cell Transplant Trends in Pakistan: Activity Survey from Pakistan Bone Marrow Transplant Group.

Pakistan is the fifth most populous country with a population of 225 million and has health expenditure accounting for only 2.8 percent of gross domestic product (GDP). Accordingly, there are a limited number of haematology-oncology and transplant centers in the country. The Pakistan Blood and Marrow Transplant (PBMT) group was established in 2020, and this report is the first activity survey from January 2021 to December 2022 focusing on the trends of matched-related donor, haploidentical, and autologous transplants in a developing country. A total of 12 transplant centers contributed data on the modified PBMT survey form retrospectively and 806 haematopoietic stem cell transplants (HSCTs) were carried out during the study duration. Allogeneic HSCT constituted 595 (73.8%) of all the transplants; this is in stark contrast to Western data, where autologous HSCT accounts for the majority of transplants. ß-thalassemia major and aplastic anemia were the commonest indications for allogeneic HSCT, in contrast to Western data, where acute leukemia is the leading transplant indication. Autologous transplants were more frequently performed for Hodgkin's lymphoma as compared to non-Hodgkin's lymphoma and multiple myeloma. The use of peripheral and bone marrow stem cells was comparable. A myeloablative conditioning regimen was routinely used in patients with acute leukemia. This report provides an insight of HSCT trends in Pakistan which are different from those of Western centers contributing to transplant data from South Asia.

Predictors of mortality in periprosthetic fractures of the hip: Results from the national PPF study.

INTRODUCTION: Periprosthetic fractures (PPFs) around the hip joint are increasing in prevalence. In this collaborative study, we aimed to investigate the impact of patient demographics, fracture characteristics, and modes of management on in-hospital mortality of PPFs involving the hip. METHODS: Using a multi-centre cohort study design, we retrospectively identified adults presenting with a PPF around the hip over a 10-year period. Univariate and multivariable logistic regression analyses were performed to study the independent correlation between patient, fracture, and treatment factors on mortality. RESULTS: A total of 1,109 patients were included. The in-hospital mortality rate was 5.3%. Multivariable analyses suggested that age, male sex, abbreviated mental test score (AMTS), pneumonia, renal failure, history of peripheral vascular disease (PVD) and deep surgical site infection were each independently associated with mortality. Each yearly increase in age independently correlates with a 7% increase in mortality (OR 1.07, p=0.019). The odds of mortality was 2.99 times higher for patients diagnosed with pneumonia during their hospital stay [OR 2.99 (95% CI 1.07-8.37) p=0.037], and 7.25 times higher for patients that developed renal failure during their stay [OR 7.25 (95% CI 1.85-28.47) p=0.005]. Patients with history of PVD have a six-fold greater mortality risk (OR 6.06, p=0.003). Mode of treatment was not a significant predictor of mortality. CONCLUSION: The in-hospital mortality rate of PPFs around the hip exceeds 5%. The fracture subtype and mode of management are not independent predictors of mortality, while patient factors such as age, AMTS, history of PVD, pneumonia, and renal failure can independently predict mortality. Peri-operative optimisation of modifiable risk factors such as lung and kidney function in patients with PPFs around the hip during their hospital stay is of utmost importance.

Clinical spectrum of post-stroke seizures.

OBJECTIVE: To determine the characteristics of post-stroke seizures and compare these in early vs. late post-stroke seizures. STUDY DESIGN: Observational study. PLACE AND DURATION OF STUDY: Department of Neurology, Liaquat National Hospital, Karachi, from March to September 2007. METHODOLOGY: All admitted patients aged over 25 years, with diagnosis of post-stroke seizures were included. Those with known epilepsy, sepsis and electrolyte imbalance were excluded. Age, co-morbid condition, details of seizures and radiological findings regarding type and location of stroke were collected and entered in a pre-formed proforma. Results were described as frequency and mean. Association of variables was determined through chi-square test with significance at p < 0.05. RESULTS: Out of the 50 patients, there were 28 (56%) males and 22 (44%) females with the mean age of 56.86 ± 15.26 years. Thirty-one (62%) patients had history of hypertension. Early seizures i.e. within 2 weeks were seen in 29 (58%) patients. Generalized seizures were seen more frequently i.e. in 37 (74%) patients. Thirty-one (62%) subjects experienced more than 2 seizures. Forty (80%) had an ischemic stroke including 36 (72%) arterial infarct and 3 (6%) venous infarcts. Intracerebral hematoma was seen in 10 (20%) of subjects. Comparison between early and late onset seizures revealed significant association between ischemic heart disease (IHD), old stroke, hypertension and late onset seizures (p < 0.05). CONCLUSION: Post-stroke seizures were more frequent in males, with history of hypertension, and with cortical ischemic strokes. Early seizures, multiple episodes and generalized seizure type were more common. Venous infarcts were chiefly associated with seizures at presentation. History of old stroke, ischemic heart disease, hypertension and hypercholesterolemia showed a strong relationship with the occurrence of late onset seizures.

Effect of hyperventilation on electroencephalographic activity.

OBJECTIVE: To observe the effect of voluntary hyperventilation on electroencephalographic activity during routine EEG recording on patients referred to a tertiary care hospital. METHODS: This was an observational study conducted at Neurophysiology Lab, Department of Neurology at Liaquat National Hospital, Karachi from May 2007 to September 2007. Data of 326 patients was collected prospectively and analyzed by SPSS version 10.0. At least 3 minutes voluntary hyperventilation was performed by the subjects. All those patients who were able to perform voluntary hyperventilation adequately were included in the study. RESULTS: Of 326 recordings, 256 (78.8%) were normal and 69 (21.2%) were abnormal. Focal epileptiform discharges were identified in 8.6% and generalized in 8.3% of subjects. Physiological slowing was found in 31 records. Out of 55 epileptics, 3 had discharges only during HV and 9 had increase in epileptiform discharges. CONCLUSION: Hyperventilation has significant effect on background rhythm during EEG recording. It is a useful activation method utilized to increase the yield of EEG.

Hand-based multibiometric systems: state-of-the-art and future challenges.

The traditional methods used for the identification of individuals such as personal identification numbers (PINs), identification tags, etc., are vulnerable as they are easily compromised by the hackers. In this paper, we aim to focus on the existing multibiometric systems that use hand based modalities for the identification of individuals. We cover the existing multibiometric systems in the context of various feature extraction schemes, along with an analysis of their performance using one of the performance measures used for biometric systems. Later, we cover the literature on template protection including various cancelable biometrics and biometric cryptosystems and provide a brief comment about the methods used for multibiometric template protection. Finally, we discuss various open issues and challenges faced by researchers and propose some future directions that can enhance the security of multibiometric templates.