Transmembrane formins as active cargoes of membrane trafficking.

Formins are a large, evolutionarily old family of plant cytoskeletal regulators whose roles include actin capping and nucleation, as well as modulation of microtubule dynamics. The plant class I formin clade is characterized by a unique domain organization, as most of its members are transmembrane proteins with possible cell wall-binding motifs exposed to the extracytoplasmic space - a structure that appears to be a synapomorphy of the plant kingdom. While such transmembrane formins are traditionally considered mainly as plasmalemma-localized proteins contributing to the organization of the cell cortex, we review, from a cell biology perspective, the growing evidence that they can also, at least temporarily, reside (and in some cases also function) in endomembranes including secretory and endocytotic pathway compartments, the endoplasmic reticulum, the nuclear envelope and the tonoplast. Based on this evidence, we propose that class I formins may thus serve as "active cargoes" of membrane trafficking, i.e. membrane-embedded proteins that modulate the fate of endo- or exocytotic compartments while being transported by them.

Post-COVID-19 Cardiovascular Sequelae and Myocarditis.

BACKGROUND AND AIM: Post coronavirus disease 2019 (COVID-19) cardiovascular (CV) pathological changes, myocarditis, and myocardial infarctions (MIs) are major public health issues. This review discusses acute and chronic COVID-19 cardiac manifestations. METHODS: The devastating impact of COVID-19 on global healthcare and economies has likely been one of humanity's deadliest calamities in recent decades, as multiple literature and databases were searched from 2020 to 2022. RESULTS: As of April 2022, we identified 73 articles in various electronic databases that discussed the details of COVID-19 and cardiac manifestations. Cardiometabolic risk factors should now, more than ever, be a top priority for clinicians, as their potent role in exacerbating COVID-19 illness severity has been conclusively demonstrated. CONCLUSION: This review discusses cardiac pathology changes, CV consequences of acute COVID-19, microvascular injury and cardiac complications linked with SARS-CoV2, COVID-19 linked with chronic CV disease, therapeutic drug effects on heart used in COVID-19, and possible investigational approaches and management strategies for post-COVID-19 CV consequences. Highlights Cardiac pathology changes: Effect of COVID-19. Mechanism of development of CV consequences in acute COVID-19: Including autopsy studies. Microvascular injury and cardiac complications: Linked with SARS-CoV2. COVID-19 linked with chronic CV disease. Therapeutic drug effects on heart used in COVID-19. Possible investigational approaches and management strategies for post-COVID-19 CV consequences.

A Novel Baseline Removal Paradigm for Subject-Independent Features in Emotion Classification Using EEG.

Emotion plays a vital role in understanding the affective state of mind of an individual. In recent years, emotion classification using electroencephalogram (EEG) has emerged as a key element of affective computing. Many researchers have prepared datasets, such as DEAP and SEED, containing EEG signals captured by the elicitation of emotion using audio-visual stimuli, and many studies have been conducted to classify emotions using these datasets. However, baseline power removal is still considered one of the trivial aspects of preprocessing in feature extraction. The most common technique that prevails is subtracting the baseline power from the trial EEG power. In this paper, a novel method called InvBase method is proposed for removing baseline power before extracting features that remain invariant irrespective of the subject. The features extracted from the baseline removed EEG data are then used for classification of two classes of emotion, i.e., valence and arousal. The proposed scheme is compared with subtractive and no-baseline-correction methods. In terms of classification accuracy, it outperforms the existing state-of-art methods in both valence and arousal classification. The InvBase method plus multilayer perceptron shows an improvement of 29% over the no-baseline-correction method and 15% over the subtractive method.

Computational assessment of growth of connective tissues around textured hip stem subjected to daily activities after THA.

Longer-term stability of uncemented femoral stem depends on ossification at bone-implant interface. Although attempts have been made to assess the amount of bone growth using finite element (FE) analysis in combination with a mechanoregulatory algorithm, there has been little research on tissue differentiation patterns on hip stems with proximal macro-textures. The primary goal of this investigation is to qualitatively compare the formation of connective tissues around a femoral implant with/without macro-textures on its proximal surfaces. This study also predicts formation of different tissue phenotypes and their spatio-temporal distribution around a macro-textured femoral stem under routine activities. Results from the study show that non-textured implants (80 to 94%) encourage fibroplasia compared to that in textured implants (71 to 85.38%) under similar routine activity, which might trigger aseptic loosening of implant. Formation of bone was more on medio-lateral sides and towards proximal regions of Gruen zones 2 and 6, which was found to be in line with clinical observations. Fibroplasia was higher under stair climbing (85 to 91%) compared to that under normal walking (71 to 85.38%). This study suggests that stair climbing, although falls under recommended activity, might be detrimental to patient compared to normal walking in the initial rehabilitation period.

Application of finite element analysis to tissue differentiation and bone remodelling approaches and their use in design optimization of orthopaedic implants: A review.

Post-operative bone growth and long-term bone adaptation around the orthopaedic implants are simulated using the mechanoregulation based tissue-differentiation and adaptive bone remodelling algorithms, respectively. The primary objective of these algorithms was to assess biomechanical feasibility and reliability of orthopaedic implants. This article aims to offer a comprehensive review of the developments in mathematical models of tissue-differentiation and bone adaptation and their applications in studies involving design optimization of orthopaedic implants over three decades. Despite the different mechanoregulatory models developed, existing literature confirm that none of the models can be highly regarded or completely disregarded over each other. Not much development in mathematical formulations has been observed from the current state of knowledge due to the lack of in vivo studies involving clinically relevant animal models, which further retarded the development of such models to use in translational research at a fast pace. Future investigations involving artificial intelligence (AI), soft-computing techniques and combined tissue-differentiation and bone-adaptation studies involving animal subjects for model verification are needed to formulate more sophisticated mathematical models to enhance the accuracy of pre-clinical testing of orthopaedic implants.

Automatic Muscle Artifacts Identification and Removal from Single-Channel EEG Using Wavelet Transform with Meta-Heuristically Optimized Non-Local Means Filter.

Electroencephalogram (EEG) signals may get easily contaminated by muscle artifacts, which may lead to wrong interpretation in the brain-computer interface (BCI) system as well as in various medical diagnoses. The main objective of this paper is to remove muscle artifacts without distorting the information contained in the EEG. A novel multi-stage EEG denoising method is proposed for the first time in which wavelet packet decomposition (WPD) is combined with a modified non-local means (NLM) algorithm. At first, the artifact EEG signal is identified through a pre-trained classifier. Next, the identified EEG signal is decomposed into wavelet coefficients and corrected through a modified NLM filter. Finally, the artifact-free EEG is reconstructed from corrected wavelet coefficients through inverse WPD. To optimize the filter parameters, two meta-heuristic algorithms are used in this paper for the first time. The proposed system is first validated on simulated EEG data and then tested on real EEG data. The proposed approach achieved average mutual information (MI) as 2.9684 ± 0.7045 on real EEG data. The result reveals that the proposed system outperforms recently developed denoising techniques with higher average MI, which indicates that the proposed approach is better in terms of quality of reconstruction and is fully automatic.

Mucormycosis: A new threat to Coronavirus disease 2019 with special emphasis on India.

The main reason for the growth of mucormycosis in people with Coronavirus disease-2019 (COVID-19) is mainly produced by Rhizopus spp. The infective mechanisms and issues recognized in Rhizopus spp. are the cell wall, germination proteins, and enzymes assisted to iron sequestration, CotH protein, and positive regulation of the GRP78 cell receptor. Mucormycosis is mainly caused by the Rhizopus spp. such as R. oryzae, R. microsporus, R. arrhizus, R. homothallicus, etc. that are gifted to numerous host defense mechanisms and attribute to the endothelium via specific receptors, GRP78 simplifying their endocytosis and angio-invasion. Factors such as hyperglycemia, elevated iron concentrations, and ketoacidosis have been shown to contribute to the pathogenesis in the tentative situation. The analytical data of 'black fungus disease' or 'mucormycosis', specify India reported for about 42.3% of published cases, followed by the USA about 16.9%, Iraq, Bangladesh, Iran, Paraguay, and 1 case each from Brazil, Mexico, Italy, UK, China, France, Uruguay, Turkey, and Austria. The COVID-19 infection is maybe a predisposing factor for mucormycosis and is related to a high mortality rate. Early recognition and restriction of hyperglycemia, liposomal amphotericin B, and surgical debridement are the bases in the successful managing of mucormycosis.

SAM 40: Dataset of 40 subject EEG recordings to monitor the induced-stress while performing Stroop color-word test, arithmetic task, and mirror image recognition task.

This paper presents a collection of electroencephalogram (EEG) data recorded from 40 subjects (female: 14, male: 26, mean age: 21.5 years). The dataset was recorded from the subjects while performing various tasks such as Stroop color-word test, solving arithmetic questions, identification of symmetric mirror images, and a state of relaxation. The experiment was primarily conducted to monitor the short-term stress elicited in an individual while performing the aforementioned cognitive tasks. The individual tasks were carried out for 25 s and were repeated to record three trials. The EEG was recorded using a 32-channel Emotiv Epoc Flex gel kit. The EEG data were then segmented into non-overlapping epochs of 25 s depending on the various tasks performed by the subjects. The EEG data were further processed to remove the baseline drifts by subtracting the average trend obtained using the Savitzky-Golay filter. Furthermore, the artifacts were also removed from the EEG data by applying wavelet thresholding. The dataset proposed in this paper can aid and support the research activities in the field of brain-computer interface and can also be used in the identification of patterns in the EEG data elicited due to stress.

Influence of sequential opening/closing of interface gaps and texture density on bone growth over macro-textured implant surfaces using FE based mechanoregulatory algorithm.

Intramedullary implant fixation is achieved through a press-fit between the implant and the host bone. A stronger press-fit between the bone and the prosthesis often introduces damage to the bone canal creating micro-gaps. The aim of the present investigation is to study the influences of simultaneous opening/closing of gaps on bone growth over macro-textured implant surfaces. Models based on textures available on CORAIL and SP-CL hip stems have been considered and 3D finite element (FE) analysis has been carried out in conjunction with mechanoregulation based tissue differentiation algorithm. Additionally, using a full-factorial approach, different combinations (between 5 µm to 15 µm) of sliding and gap distances at the bone-implant interface were considered to understand their combined influences on bone growth. All designs show an elevated fibrous tissue formation (10.96% at 5 µm to 29.38% at 40 µm for CORAIL based textured model; 11.45% at 5 µm to 32.25% at 40 µm for SP-CL based textured model) and inhibition of soft cartilaginous tissue (75.64% at 5 µm to 53.94% at 40 µm for CORAIL based model; 76.02% at 5 µm to 53.60% at 40 µm SP-CL based model) at progressively higher levels of normal micromotion, leading to a fragile bone-implant interface. These results highlight the importance of minimizing both sliding and gap distances simultaneously to enhance bone growth and implant stability. Further, results from the studies with differential texture density over CORAIL based implant reveal a non-linear complex relationship between tissue growth and texture density which might be investigated in a machine learning framework.

Qualitative predictions of bone growth over optimally designed macro-textured implant surfaces obtained using NN-GA based machine learning framework.

The surface features on implant surface can improve biologic fixation of the implant with the host bone leading to improved secondary (biological) implant stability. Application of finite element (FE) based mechanoregulatory schemes to estimate the amount of bone growth for a wide range of implant surface features is either manually intensive or computationally expensive. This study adopts an integrated approach combining FE, back-propagation neural network (BPNN) and genetic algorithm (GA) based search to evaluate optimum surface macro-textures from three representative implant models so as to enhance bone growth. Initial surface textures chosen for the implant models were based on an earlier investigation. Based on FE predicted dataset, a BPNN was formulated for faster prediction of bone growth. Using the BPNN predicted output, a GA-based search was carried out to maximize bone growth subject to clinically admissible micromotion at the bone-implant interface. The results from FE analysis and bone growth predictions from the BPNN were found to have strong correlation. The optimal osseointegration-maximized-textures (OMTs) obtained were found to offer enhanced biological fixation, as compared to that offered by the textures in the initial models. Results from the present study reveal that certain reduction in the dimension of ribs/grooves promotes bone growth. However, periodic patterns of ribs with higher and lower rib dimensions provide uniform stress environment at the interface thus promoting osseointegration.

Automatic Eyeblink Artifact Removal From EEG Signal Using Wavelet Transform With Heuristically Optimized Threshold.

This paper proposes an automatic eyeblink artifacts removal method from corrupted-EEG signals using discrete wavelet transform (DWT) and meta-heuristically optimized threshold. The novel idea of thresholding approximation-coefficients (ACs) instead of detail-coefficients (DCs) of DWT of EEG in a backward manner is proposed for the first time for the removal of eyeblink artifacts. EEG is very sensitive and easily gets affected by eyeblink artifacts. First, the eyeblink corrupted EEG signals are identified using support vector machine (SVM) as a classifier. Then the corrupted EEG signal is decomposed using DWT up to the sixth level. Both the mother wavelet and the level of decomposition are selected using appropriate techniques. Then the ACs are thresholded in backward manner using the optimum threshold values followed by inverse DWT operation to reconstruct the original EEG signal. The AC at level 6 is thresholded and is used in IDWT with DC to get back the AC at level 5. Likewise, the backward thresholding of the ACs followed by IDWT is continued till the artifact free EEG signal is reconstructed at level 1. The optimum values of the thresholds of the ACs at different levels are optimized using two meta-heuristic algorithms, particle swarm optimization (PSO) and grey wolf optimization (GWO) for comparison. The results reveal that the proposed methodology is superior to the recently reported methods in terms of average correlation coefficient (CC) which states that the proposed method is better in terms of the quality of reconstruction in addition to being fully automatic.

The influence of macro-textural designs over implant surface on bone on-growth: A computational mechanobiology based study.

The longerterm secondary stability of an uncemented implant depends primarily on the quality and extent of bone in-growth or on-growth at the bone-implant interface. Investigations are warranted to predict the influences of implant macro-textures on bone on-growth pattern. Mechanoregulatory tissue differentiation algorithms can predict such patterns effectively. There is, however, a dearth of volumetric in silico study to assess the influence of macro-textures on bone growth. The present study investigated the influence of macro-textural grooves/ribs on changes in tissue formation at the bone-implant interface by carrying out a 3D finite element (FE) analysis. Three distinct macro-textures, loosely based on commercially viable hip stem models, were comparatively assessed for varying levels of interfacial micromotion. The study predicted elevated fibrogenesis and chondrogenesis, followed by a suppressed osteogenesis for higher levels of micromotion (60 µm and 100 µm), resulting in weak bone-implant interface strength. However, small judicious modifications in implant surface texture may enhance bone growth to a considerable extent. The numerical scheme can further be used as a template for more rigorous parametric and multi-scale studies.