Confronting multidrug-resistant Klebsiella meningitis after mid-clival Cerebrospinal Fluid leak repair: a therapeutic odyssey.

A man in his 40s with type 2 diabetes mellitus had persistent right-sided watery nasal discharge for 6 months following cerebrospinal fluid (CSF) leak repair at another hospital, prompting his visit to us due to recurring symptoms. Imaging revealed a CSF leak from the mid-clivus for which revision endoscopic CSF leak repair was done. Regrettably, he developed postoperative meningitis caused by multidrug-resistant (MDR) Klebsiella pneumoniaeManaging this complex case was a challenging task due to the pathogen's resistance to conventional drugs and the scarcity of scientific evidence. We initiated a culture-guided combination regimen with ceftazidime, avibactam, aztreonam and tigecycline. This decision stemmed from meticulous literature review and observed antibiotic synergy while testing for this organism.After 4 weeks of vigilant treatment, the patient's symptoms improved significantly, and CSF cultures were sterile. We present our approach to effectively confront and manage a challenging instance of postoperative MDR bacterial meningitis.

Combined two-level damage identification strategy using ultrasonic guided waves and physical knowledge assisted machine learning.

Structural Health Monitoring of composite structures is one of the significant challenges faced by the aerospace industry. A combined two-level damage identification viz damage detection and localization is performed in this paper for a composite panel using ultrasonic guided waves. A novel physical knowledge-assisted machine learning technique is proposed in which domain knowledge and expert supervision is utilized to assist the learning process. Two supervised learning-based convolutional neural networks are trained for damage detection (binary classification) and localization (multi-class classification) on an experimental benchmark dataset. The performance of the trained models is evaluated using loss curve, accuracy, confusion matrix, and receiver-operating characteristics curve. It is observed that incorporating physical knowledge helps networks perform better than a direct deep learning approach. In this work, a combined damage identification strategy is proposed for a real-time application. In this strategy, the damage detection model works in an outer-loop and predicts the state of the structure (undamaged or damaged), whereas an inner-loop predicts the location of the damage only if the outer-loop detects damage. It is seen that the proposed technique offers advantages in terms of accuracy (above 99% for both detection and localization), computational time (prediction time per signal in milliseconds), sensor optimization, in-situ monitoring, and robustness towards the noise.

Numerical analysis of lamb wave generation in piezoelectric composite IDT.

An equivalent, single-layer model for Lamb wave generation by interdigital transducer (IDT) on composite host structures is developed. The additional complexities generally encountered while launching the surface acoustic wave (SAW) on composite structure, such as the coupling between the Lamb wave modes, the complicated nature of the electromechanical actuation etc. are considered. The model of infinite IDT is extended to deal with the finite IDT with edge discontinuities. The effect of electromechanical actuation on the wavelength shifts with respect to the passive case is investigated. The problem of electrically driven instability within the IDT is analyzed. Numerical results are reported by considering a model of the IDT integrated with the host structure, which shows that there are significant deviations from the conventional design estimates while launching a targeted mode. The proposed approach may enable one to obtain new designs in material systems and geometry that avoid mode-mixing, or to introduce it by choice.