Obstetric cerebral venous thrombosis

Pregnancy and puerperium are most prevalent prothrombotic states leading to cerebral venous thrombosis. Likelihood of stroke to be of venous origin is greater in stroke associated with pregnancy compared to stroke unrelated to pregnancy. Pregnancy induces several changes in coagulation system, which persists at least during early puerperium, rendering it a prothrombotic state. Hypercoaguability worsens further after delivery as a result of volume depletion and trauma. During puerperium additional risk factors include infection and instrumental delivery or Caesarean section. The management follows general rules as for the venous thrombosis unrelated to pregnancy, however the prognosis is different

A Context-aware and Intelligent Framework for the Secure Mission Critical Systems

Recent technological advancements in pervasive systems have shown the poten-tial to address challenges in the military domain. Research developments in mili-tary-based mission-critical systems have refined a lot as in autopilot, sensing true target behavior, battle damage conditions, acquiring and manipulating command control information. However, the application of pervasive systems in the military domain is still evolving. In this paper, an intelligent framework has been pro-posed for mission-critical systems to incorporate advanced heterogeneous com-munication protocols; service-oriented layered structure and context-aware infor-mation manipulation. The proposed framework addresses the limitation of “time-space” constraints in Mission-critical systems that have been improved signifi-cantly. This improvement is courtesy to enhancing situation-aware tactical capa-bilities such as localization, decision significance, strategic span, strategic inten-tions, resource coordination and profiling concerning the situation. A comprehen-sive use case model has been presented for a typical battle-field scenario followed by a comparison of the proposed framework with existing techniques. It is evi-dent from experiments and analyses that the proposed framework provides more effective and seamless interaction with contextual resources to improve tactical capabilities. This is the peer reviewed version of the following article: A Context-aware and Intelligent Framework for the Secure Mission Critical Systems, which has been published in final form in Transactions on Emerging Telecommunications Technologies. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Version

Drug resistance profile and biofilm forming potential of Pseudomonas aeruginosa isolated from contact lenses in Karachi-Pakistan

BACKGROUND: The contaminated contact lens provides Pseudomonas aeruginosa an ideal site for attachment and biofilm production. Continuous contact of the eye to the biofilm-infested lens can lead to serious ocular diseases, such as keratitis (corneal ulcers). The biofilms also prevent effective penetration of the antibiotics, which increase the chances of antibiotic resistance. METHODS: For this study, 22 Pseudomonas aeruginosa isolates were obtained from 36 contact lenses and 14 contact lens protective fluid samples. These isolates were tested against eight commonly used antibiotics using Kirby-Bauer disk diffusion method. The biofilm forming potential of these isolates was also evaluated using various qualitative and quantitative techniques. Finally, a relationship between biofilm formation and antibiotic resistance was also examined. RESULTS: The isolates of Pseudomonas aeruginosa tested were found resistant to most of the antibiotics tested. Qualitative and quantitative biofilm analysis revealed that most of the isolates exhibited strong biofilm production. The biofilm production was significantly higher in isolates that were multi-drug resistant (p < 0.0001). CONCLUSION: Our study indicates that multi-drug resistant, biofilm forming Pseudomonas aeruginosa isolates are mainly involved in contact lens associated infections. This appears to be the first report from Pakistan, which analyzes both antibiotic resistance profile and biofilm forming potential of Pseudomonas aeruginosa isolates from contact lens of the patients with contact lens associated infections

A Detailed Survey on Federated Learning Attacks and Defenses

A traditional centralized method of training AI models has been put to the test by the emergence of data stores and public privacy concerns. To overcome these issues, the federated learning (FL) approach was introduced. FL employs a privacy-by-design architecture to train deep neural networks utilizing decentralized data, in which numerous devices collectively build any machine learning system that does not reveal users’ personal information under the supervision of a centralized server. While federated learning (FL), as a machine learning (ML) strategy, may be effective for safeguarding the confidentiality of local data, it is also vulnerable to attacks. Increased interest in the FL domain inspired us to write this paper, which informs readers of the numerous threats to and flaws in the federated learning strategy, and introduces a multiple-defense mechanism that can be employed to fend off threats

Status of secretor and non-secretor with respect to ABO blood group system in young population in Karachi-Pakistan

The current study was carried out on 550 healthy population having 250 males and 300 females. Five ml venousblood was collected following standard biosafety measures. ABO blood grouping was done by Tile method and found blood group B dominant in both sexes i.e 180 male and 120 female were found with blood group B. Moreover, 2 ml of saliva was also collected from allvolunteers. Secretor status was detected from the saliva byhaemagglutination inhibition method and found 278 female and 234 male were secretors

Feeding of phytobiotics and exogenous protease in broilers: Comparative effect on nutrient digestibility, bone strength and gut morphology

In this feeding trial, a total of 500 Cobb‐500 broiler (day‐old) chickens were randomly assigned to a control dietary treatment (basal diet only) or supplemented with crushed seeds of coriander (Coriandrum sativum L.), black cumin (Bunium persicum (Boiss.) B. Fedtsch) and ajwain (Carum copticum L.) (at 10 mg/kg each) and exogenous protease (30,000 IU/kg), respectively, in order to assess the effect of the diets on nutrient digestibility, bone strength and gut morphology. The results indicated that the digestibility coefficients of crude protein, crude fat, nitrogen‐free extract, calcium and phosphorous were significantly (p &lt; 0.05) higher in protease‐treated birds compared to the control. The tibia bone weight was improved (p = 0.03) in Carum copticum, Coriandrum sativum and protease‐supplemented birds. Bone length increased (p &lt; 0.05) in protease and Carum copticum fed broilers, while the robusticity index decreased (p &lt; 0.05) in all treatments. Villus length and width decreased (p &lt; 0.05) in Carum copticum and Bunium persicum fed broilers. From findings, it was demonstrated that exogenous protease shows comparatively better results in improving bone quality, ileal digestibility and villus morphology in broilers

Using a deep learning method and data from two-dimensional (2D) marker-less video-based images for walking speed classification

Human body measurement data related to walking can characterize functional move ment and thereby become an important tool for health assessment. Single-camera-captured two dimensional (2D) image sequences of marker-less walking individuals might be a simple approach for estimating human body measurement data which could be used in walking speed-related health assessment. Conventional body measurement data of 2D images are dependent on body-worn garments (used as segmental markers) and are susceptible to changes in the distance between the participant and camera in indoor and outdoor settings. In this study, we propose five ratio-based body measurement data that can be extracted from 2D images and can be used to classify three walking speeds (i.e., slow, normal, and fast) using a deep learning-based bidirectional long short-term memory classification model. The results showed that average classification accuracies of 88.08% and 79.18% could be achieved in indoor and outdoor environments, respectively. Additionally, the proposed ratio-based body measurement data are independent of body-worn garments and not susceptible to changes in the distance between the walking individual and camera. As a simple but efficient technique, the proposed walking speed classification has great potential to be employed in clinics and aged care homes

Polymer Concentration and Solvent Variation Correlation with the Morphology and Water Filtration Analysis of Polyether Sulfone Microfiltration Membrane

Microfiltration flat sheet membranes of polyether sulfone (PES) were fabricated by incorporating varying concentrations of polymer and investigated the influence of substituting solvents. The membranes were prepared via immersion precipitation method. Different solvents that included NMP (N-methyl-2-pyrrolidone), DMF (dimethylformamide), and THF (tetrahydrofuran) were used to analyse their effect on the performance and morphology of the prepared membranes. Two different coagulation bath temperatures were used to investigate the kinetics of membrane formation and subsequent effect on membrane performance. The maximum water flux of 141 ml/cm2.h was observed using 21% of PES concentration in NMP + THF cosolvent system. The highest tensile strength of 29.15 MPa was observed using membrane prepared with 21% PES concentration in NMP as solvent and coagulation bath temperature of 25°C. The highest hydraulic membrane resistance was reported for membrane prepared with 21% PES concentration in NMP as solvent. Moreover, the lowest contact angle of 67° was observed for membrane prepared with 15% of PES concentration in NMP as solvent with coagulation bath temperature of 28°C. Furthermore, the Hansen solubility parameter was used to study the effect on the thermodynamics of membrane formation and found to be in good correlation with experimental observation and approach in the present work

Walking Speed Classification from Marker-Free Video Images in Two-Dimension Using Optimum Data and a Deep Learning Method

Walking speed is considered a reliable assessment tool for any movement-related functional activities of an individual (i.e., patients and healthy controls) by caregivers and clinicians. Traditional video surveillance gait monitoring in clinics and aged care homes may employ modern artificial intelligence techniques to utilize walking speed as a screening indicator of various physical outcomes or accidents in individuals. Specifically, ratio-based body measurements of walking individuals are extracted from marker-free and two-dimensional video images to create a walk pattern suitable for walking speed classification using deep learning based artificial intelligence techniques. However, the development of successful and highly predictive deep learning architecture depends on the optimal use of extracted data because redundant data may overburden the deep learning architecture and hinder the classification performance. The aim of this study was to investigate the optimal combination of ratio-based body measurements needed for presenting potential information to define and predict a walk pattern in terms of speed with high classification accuracy using a deep learning-based walking speed classification model. To this end, the performance of different combinations of five ratio-based body measurements was evaluated through a correlation analysis and a deep learning-based walking speed classification test. The results show that a combination of three ratio-based body measurements can potentially define and predict a walk pattern in terms of speed with classification accuracies greater than 92% using a bidirectional long short-term memory deep learning method