Search

We describe a natural orifice technique for simultaneous endoluminal lateral suspension of apical vaginal wall and rectal prolapse fixation with ultrasound and fluoroscopic assistance.The technique is minimally invasive, can be performed under regional anaesthesia, and avoids laparotomy or use of a mesh in addition to preserving the uterus.This technique involves suprapubic transvaginal ventral suture colposuspension, fixation of the anterior rectal wall to the undersurface of the anterior abdominal wall and tack fixation of the posterior rectal wall to the underlying sacral promontory through a submucosal tunnel performed endoscopically with fluoroscopic and ultrasound assistance.Seven patients with a mean age of 63 years were followed between 3 to 11 months.This is a novel minimally invasive transluminal procedure that repairs concomitant rectal and vaginal prolapse in the same sitting.Improvement in the instrument design and incorporation of endoluminal robotic systems will enhance the technical ease. The study needs validation in larger series of patients with longer follow-up.

During last few years, extensive research had been carried out to enhance the lifetime of wireless sensor network. However, most of the approaches centred on energy efficiency to prolong the lifetime of the network but kept quiet about normalization of conditional parameters which is equally significant to minimize the energy consumption of the overall network. In this paper, we propose efficient cluster head selection as well as proper cluster formation and energy efficient and energy balance routing which is premised with normalized condition parameters. For selecting the cluster head efficiently, we introduce an energy efficient algorithm using normalized conditional parameters. Subsequently, an efficient load balanced clustering algorithm using normalized conditional parameters has been proposed to increase the lifespan of sensor network. Finally, we propose the energy efficient and energy balanced routing algorithm considering normalized conditional parameters. We also propose the LP formulation for the clustering and routing problem. Proposed algorithm has been simulated and the results are compared in terms of relevant metrics such as network lifetime, time for first node die, dead sensor nodes etc. with some of the existing algorithms to show the effectiveness of the proposed work.

Soft electronic skin (soft-e-skin) capable of sensing touch and pressure similar to human skin is essential in many applications, including robotics, healthcare, and augmented reality. However, most of the research effort on soft-e-skin was confined to the lab-scale demonstration. Several hurdles remain challenging, such as highly complex and expensive fabrication processes, instability in long-term use, and difficulty producing large areas and mass production. Here, we present a robust 3D printable large-area electronic skin made of a soft and resilient polymer capable of detecting touch and load, and bending with extreme sensitivity (up to 150 kPa

Background and aims: Body fluids such as saliva, tears and urine from patients with hepatitis B virus (HBV) infection are well known to be infectious. However, the infectivity of internal body fluids like ascitic fluid from patients with HBV infection has not been established. So, we conducted this study to know the infectivity of ascitic fluid for hepatitis B in decompensated cirrhosis by detecting HBV DNA in it.Methods: Patients with HBV related cirrhosis with ascites were enrolled. The levels of HBV DNA in the ascitic fluid from these patients were quantified by real-time PCR, and compared with HBV DNA levels in serum. Clinical and laboratory parameters to predict HBV DNA positivity in ascitic fluid were also assessed.Results : Twenty one patients (mean age 45.43±13 years) with HBV related cirrhosis with ascites were enrolled. HBV DNA in ascitic fluid was detected in 4/21 (19 %) patients. The ascitic fluid HBV DNA levels ranged from 4.8 to 6.4 log copies/mL (mean ± SD = 5.27 ± 0.55). High levels of serum HBV DNA was significantly associated with HBV DNA detectability in ascitic fluid (p=0.001). Patients with HBV DNA detected in ascitic fluid had significantly higher serum protein levels as compared to those having undetectable HBV DNA in ascitic fluid (6.83 ± 0.33 versus 5.70 ± 0.77 g/dl, p=0.011). Conclusions : HBV DNA is detectable in ascitic fluid in about one fifth of HBV related cirrhosis patients with ascites so it may not be considered an important source for HBV transmission. High serum HBV DNA and high serum protein levels were positively associated with HBV DNA detectability in ascitic fluid.

Although the electric vehicle market is witnessing an unprecedented evolution, the fast adoption of these vehicles requires a more thorough status analysis of the battery performance's functionality and reliability. Due to their rechargeable nature, Lithium-ion batteries (LIBs) operation is subject to different irreversible processes during their charging and discharging cycles and causing capacity fade due to various degradation mechanisms. These processes generally result in battery capacity degradation, which usually results in battery failure, with consequences ranging from loss of operation, reduced capability, downtime, and catastrophic malfunctions. To address the issues mentioned above, numerous studies have been dedicated to proposing proper degradation model mechanisms for improving the reliability and availability of LIBs. However, due to accuracy and computational complexity challenges, most existing remaining useful life (RUL) and health prediction models focus on special degradation effects and ignore the integrated deterioration mechanisms, which generally involve batteries' capacity fade associated with the inadequacy of current health estimation tools. Thus, these shortcomings ultimately echo the need to devise novel tools to identify the dominant criteria negatively affecting battery performance and accurately predict the system's failure. The above challenges eventually necessitate a robust and reliable predictive or prognostic capability for prognostics and health monitoring (PHM) under a complexly hostile working environment. In this context, this investigation aims at proposing a novel data-driven approach called data-driven prognosis (DDP) that estimates the relevant constitutive parameters in situ and captures deviations from the expected degradation dynamics of the LIBs in addition to precise modeling of the degradation and capacity models. This talk will present a new data-driven approach using statistical pattern recognition and machine learning tools to detect batteries' anomalies and failures.