Development, validation and clinical utility of short-term adverse-effects of electroconvulsive therapy (SAVE) checklist.

Electroconvulsive therapy (ECT) is one of the most effective treatments in psychiatry. However, it has many cognitive and non-cognitive adverse effects (AEs). There are lacunae in the literature on systematic assessment of non-cognitive AEs. There is a need for a standard, comprehensive and specific clinical tool to evaluate this. Hence, a checklist of short-term AEs of ECT (SAVE) with a 2-phase assessment was developed. Content validation was done using 15 experts' ratings and predefined content validity ratio and index (CVR and CVI) in a two-stage modified Delphi method. The checklist had a good CVR and CVI with a final tool of 39 items. The tool was sensitive and identified the non-cognitive AEs after ECT. Cardiovascular and musculoskeletal systems displayed the highest incidence. Many participants exhibited delayed recovery in orientation, gait, and stance, highlighting a necessity for meticulous monitoring. SAVE is the first standardised tool to assess short-term ECT-related AEs systematically. This checklist likely identifies clinically significant incidences of adverse effects. Its regular use may enhance the safety of ECT and patient comfort by supporting early identification and intervention for AEs. However, given the transient nature of AEs, further studies are needed to determine their predictive validity for long-term consequences.

Solitary neurofibroma of the palate mimicking mucocele: A rare case report.

Neurofibroma is a benign, slow-growing, peripheral nerve neoplasm. It presents as a solitary tumor or component of neurofibromatosis (NF) and von Recklinghausen's disease. The occurrence of solitary neurofibromas in the oral cavity is less frequent, and those in the palate are reported in less numbers. This report describes an unusual case of solitary neurofibroma in the hard palate which clinically mimicked a mucocele.

A potential utilization of end-of-life tyres as recycled carbon black in EPDM rubber.

End-of-life (EOL) tyres and their decomposition present severe environmental concern due to their resistance to moisture, oxygen, natural degradation, etc. Pyrolysis is considered to be the most effective and sustainable process for recycling, due to its eco-friendly process. The current work studied the effect of recycled carbon black (rCB), obtained from the pyrolysis of EOL tyres, on the properties of ethylene propylene diene rubber (EPDM). The rCB was characterized by scanning electron microscopy (SEM), thermogravimetric analysis (TGA) and chemical methods. rCB was incorporated solely, into a conventional EPDM formulation and also in combination with N550 carbon black. The physico-mechanical properties of the EPDM vulcanizates, before and after aging, were succinctly studied by SEM, TGA, Differential Scanning Calorimetry (DSC), tensile tests and cross-link density. The average particle size of rCB was observed to be 8 µm and the ash content was observed to be higher when compared to the conventional N550 carbon black, which was evident, by the TGA and SEM-EDX analyses. The reinforcing effect and the cross-link density of the rCB-filled vulcanizates were found to be marginally inferior in comparison to the conventional carbon black (N550). The morphology of the tensile- and tear-fractured surfaces were studied by SEM and it was observed that the breaking mechanism follows the rubber chain detachment from the surface mode.

Nonbinary associative memory with exponential pattern retrieval capacity and iterative learning.

We consider the problem of neural association for a network of nonbinary neurons. Here, the task is to first memorize a set of patterns using a network of neurons whose states assume values from a finite number of integer levels. Later, the same network should be able to recall the previously memorized patterns from their noisy versions. Prior work in this area consider storing a finite number of purely random patterns, and have shown that the pattern retrieval capacities (maximum number of patterns that can be memorized) scale only linearly with the number of neurons in the network. In our formulation of the problem, we concentrate on exploiting redundancy and internal structure of the patterns to improve the pattern retrieval capacity. Our first result shows that if the given patterns have a suitable linear-algebraic structure, i.e., comprise a subspace of the set of all possible patterns, then the pattern retrieval capacity is exponential in terms of the number of neurons. The second result extends the previous finding to cases where the patterns have weak minor components, i.e., the smallest eigenvalues of the correlation matrix tend toward zero. We will use these minor components (or the basis vectors of the pattern null space) to increase both the pattern retrieval capacity and error correction capabilities. An iterative algorithm is proposed for the learning phase, and two simple algorithms are presented for the recall phase. Using analytical methods and simulations, we show that the proposed methods can tolerate a fair amount of errors in the input while being able to memorize an exponentially large number of patterns.