Search

Fibromyalgia is a chronic pain syndrome associated with sleep disturbances, which may manifest as altered electroencephalography and electrocardiography (ECG) signal alterations during sleep. We aimed to develop a lightweight machine learning model for diagnosing fibromyalgia using single-lead ECG signals recorded during sleep. We analyzed 139 single-lead ECGs recorded during Stage 2 and Sleep Stage 3 of 16 patients with fibromyalgia and 16 age and sex matched controls. ECG records were divided into 15-second segments: 3308 and 1783 in healthy vs fibromyalgia classes, respectively. Our model comprised (1) feature extraction that combined an 8-wavelet filter and 4-level multiple filters-based multilevel discrete wavelet transform signal decomposition with a novel local binary pattern (LBP)-like function, 3LBP, that generated multiple patterns (analogous to quantum superposition) for feature map value extraction (the optimal input-specific pattern was dynamically selected using a novel forward-forward algorithm); (2) feature selection using neighborhood component analysis and Chi-square functions; (3) classification with k-nearest neighbors and support vector machine classifiers using leave-one-record-out cross-validation; and (4) mode function-based iterative majority voting to generate voted results, from which the best model result was derived. Our model attained binary classification accuracies of 93.87% and 92.02% for Sleep Stage 2 and Sleep Stage 3, respectively. The observed outcomes and empirical evidence unequivocally demonstrate the efficacy of our proposed methodology in differentiating the electrocardiographic signatures of fibromyalgia patients from control subjects. The model exhibited self-organizational properties and computational efficiency, rendering it amenable to facile clinical integration.

Most emotion classification schemes to date have concentrated on individual inputs rather than crowd-level signals. In addressing this gap, we introduce Sound-based Community Emotion Recognition (SCED) as a fresh challenge in the machine learning domain. In this pursuit, we crafted the FF-BTP-based feature engineering model inspired by deep learning principles, specifically designed for discerning crowd sentiments. Our unique dataset was derived from 187 YouTube videos, summing up to 2733 segments each of 3 seconds (sampled at 44.1 KHz). These segments, capturing overlapping speech, ambient sounds, and more, were meticulously categorized into negative, neutral, and positive emotional content. Our architectural design fuses the BTP, a textural feature extractor, and an innovative handcrafted feature selector inspired by Hinton's FF algorithm. This combination identifies the most salient feature vector using calculated mean square error. Further enhancements include the incorporation of a multilevel discrete wavelet transform for spatial and frequency domain feature extraction, and a sophisticated iterative neighborhood component analysis for feature selection, eventually employing a support vector machine for classification. On testing, our FF-BTP model showcased an impressive 97.22% classification accuracy across three categories using the SCED dataset. This handcrafted approach, although inspired by deep learning's feature analysis depth, requires significantly lower computational resources and still delivers outstanding results. It holds promise for future SCED-centric applications.

Gram-negative bacteria cause the majority of highly drug-resistant bacterial infections. To cross the outer membrane of the complex Gram-negative cell envelope, antibiotics permeate through porins, trimeric channel proteins that enable the exchange of small polar molecules. Mutations in porins contribute to the development of drug-resistant phenotypes. In this work, we show that a single point mutation in the porin PorB from Neisseria meningitidis, the causative agent of bacterial meningitis, can strongly affect the binding and permeation of beta-lactam antibiotics. Using X-ray crystallography, high-resolution electrophysiology, atomistic biomolecular simulation, and liposome swelling experiments, we demonstrate differences in drug binding affinity, ion selectivity and drug permeability of PorB. Our work further reveals distinct interactions between the transversal electric field in the porin eyelet and the zwitterionic drugs, which manifest themselves under applied electric fields in electrophysiology and are altered by the mutation. These observations may apply more broadly to drug-porin interactions in other channels. Our results improve the molecular understanding of porin-based drug-resistance in Gram-negative bacteria.