Integrating Wearable Textiles Sensors and IoT for Continuous sEMG Monitoring.

Surface electromyography is a technique used to measure the electrical activity of muscles. sEMG can be used to assess muscle function in various settings, including clinical, academic/industrial research, and sports medicine. The aim of this study is to develop a wearable textile sensor for continuous sEMG monitoring. Here, we have developed an integrated biomedical monitoring system that records sEMG signals through a textile electrode embroidered within a smart sleeve bandage for telemetric assessment of muscle activities and fatigue. We have taken an "Internet of Things"-based approach to acquire the sEMG, using a Myoware sensor and transmit the signal wirelessly through a WiFi-enabled microcontroller unit (NodeMCU; ESP8266). Using a wireless router as an access point, the data transmitted from ESP8266 was received and routed to the webserver-cum-database (Xampp local server) installed on a mobile phone or PC for processing and visualization. The textile electrode integrated with IoT enabled us to measure sEMG, whose quality is similar to that of conventional methods. To verify the performance of our developed prototype, we compared the sEMG signal recorded from the biceps, triceps, and tibialis muscles, using both the smart textile electrode and the gelled electrode. The root mean square and average rectified values of the sEMG measured using our prototype for the three muscle types were within the range of 1.001 ± 0.091 mV to 1.025 ± 0.060 mV and 0.291 ± 0.00 mV to 0.65 ± 0.09 mV, respectively. Further, we also performed the principal component analysis for a total of 18 features (15 time domain and 3 frequency domain) for the same muscle position signals. On the basis on the hierarchical clustering analysis of the PCA's score, as well as the one-way MANOVA of the 18 features, we conclude that the differences observed in the data for the different muscle types as well as the electrode types are statistically insignificant.

Spatiotemporal distribution of climate-sensitive disease incidences in ethiopia: a longitudinal retrospective analysis of Malaria, Meningitis, Cholera, Dysentery, Leishmaniasis and Dengue fever between 2010 and 2022/2023.

BACKGROUND: Understanding the temporal and geographic distribution of disease incidences is crucial for effective public health planning and intervention strategies. This study presents a comprehensive analysis of the spatiotemporal distribution of disease incidences in Ethiopia, focusing on six major diseases: Malaria, Meningitis, Cholera and Dysentery, over the period from 2010 to 2022, whereas Dengue Fever and Leishmaniasis from 2018 to 2023. METHODS: Using data from Ethiopian public health institute: public health emergency management (PHEM), and Ministry of Health, we examined the occurrence and spread of each disease across different regions of Ethiopia. Spatial mapping and time series analysis were employed to identify hotspots, trends, and seasonal variations in disease incidence. RESULTS: The findings reveal distinct patterns for each disease, with varying cases and temporal dynamics. Monthly wise, Malaria exhibits a cyclical pattern with a peak during the rainy and humid season, while Dysentery, Meningitis and Cholera displays intermittent incidences. Dysentery cases show a consistent presence throughout the years, while Meningitis remains relatively low in frequency but poses a potential threat due to its severity. Dengue fever predominantly occurs in the eastern parts of Ethiopia. A significant surge in reported incident cases occurred during the years 2010 to 2013, primarily concentrated in the Amhara, Sidama, Oromia, Dire Dawa, and Benishangul-Gumuz regions. CONCLUSIONS: This study helps to a better understanding of disease epidemiology in Ethiopia and can serve as a foundation for evidence-based decision-making in disease prevention and control. By recognizing the patterns and seasonal changes associated with each disease, health authorities can implement proactive measures to mitigate the impact of outbreaks and safeguard public health in the region.

Application of singular value decomposition analysis: Insights into the complex mechanisms of amyloidogenesis.

Amyloidogenesis, with its multifaceted nature spanning from peptide self-assembly to membrane-mediated structural transitions, presents a significant challenge for the interdisciplinary scientific community. Here, we emphasize on how Singular Value Decomposition (SVD) can be employed to reveal hidden patterns and dominant modes of interaction that govern the complex process of amyloidogenesis. We first utilize SVD analysis on Circular Dichroism (CD) spectral datasets to identify the intermediate structural species emerging during peptide-membrane interactions and to determine binding constants more precisely than conventional methods. We investigate the monomer loss kinetics associated with peptide self-assembly using Nuclear Magnetic Resonance (NMR) dataset and determine the global kinetic parameters through SVD. Furthermore, we explore the seeded growth of amyloid fibrils by analyzing a time-dependent NMR dataset, shedding light on the kinetic intricacies of this process. Our analysis uncovers two distinct states in the aggregation of Aß40 and pinpoints key residues responsible for this seeded growth. To strengthen our findings and enhance their robustness, we validate those using simulated data, thereby highlighting the physical interpretations derived from SVD. Overall, SVD analysis offers a model-free, global kinetic perspective, enabling the selection of optimal kinetic models. This study not only contributes valuable insights into the dynamics but also highlights the versatility of SVD in unravelling complex processes of amyloidogenesis.

Tryp: a dataset of microscopy images of unstained thick blood smears for trypanosome detection.

Trypanosomiasis, a neglected tropical disease (NTD), challenges communities in sub-Saharan Africa and Latin America. The World Health Organization underscores the need for practical, field-adaptable diagnostics and rapid screening tools to address the negative impact of NTDs. While artificial intelligence has shown promising results in disease screening, the lack of curated datasets impedes progress. In response to this challenge, we developed the Tryp dataset, comprising microscopy images of unstained thick blood smears containing the Trypanosoma brucei brucei parasite. The Tryp dataset provides bounding box annotations for tightly enclosed regions containing the parasite for 3,085 positive images, and 93 images collected from negative blood samples. The Tryp dataset represents the largest of its kind. Furthermore, we provide a benchmark on three leading deep learning-based object detection techniques that demonstrate the feasibility of AI for this task. Overall, the availability of the Tryp dataset is expected to facilitate research advancements in diagnostic screening for this disease, which may lead to improved healthcare outcomes for the communities impacted.

Network analysis of chromophore binding site in LOV domain.

Photoreceptor proteins are versatile toolbox for developing biosensors for optogenetic applications. These molecular tools get activated upon illumination of blue light, which in turn offers a non-invasive method for gaining high spatiotemporal resolution and precise control of cellular signal transduction. The Light-Oxygen-Voltage (LOV) domain family of proteins is a well-recognized system for constructing optogenetic devices. Translation of these proteins into efficient cellular sensors is possible by tuning their photochemistry lifetime. However, the bottleneck is the need for more understanding of the relationship between the protein environment and photocycle kinetics. Significantly, the effect of the local environment also modulates the electronic structure of chromophore, which perturbs the electrostatic and hydrophobic interaction within the binding site. This work highlights the critical factors hidden in the protein networks, linking with their experimental photocycle kinetics. It presents an opportunity to quantitatively examine the alternation in chromophore's equilibrium geometry and identify details which have substantial implications in designing synthetic LOV constructs with desirable photocycle efficiency.

Evaluation of Novel Embroidered Textile-Electrodes Made from Hybrid Polyamide Conductive Threads for Surface EMG Sensing.

Recently, there has been an increase in the number of reports on textile-based dry electrodes that can detect biopotentials without the need for electrolytic gels. However, these textile electrodes have a higher electrode skin interface impedance due to the improper contact between the skin and the electrode, diminishing the reliability and repeatability of the sensor. To facilitate improved skin-electrode contact, the effects of load and holding contact pressure were monitored for an embroidered textile electrode composed of multifilament hybrid thread for its application as a surface electromyography (sEMG) sensor. The effect of the textile's inter-electrode distance and double layering of embroidery that increases the density of the conductive threads were studied. Electrodes embroidered onto an elastic strap were wrapped around the forearm with a hook and loop fastener and tested for their performance. Time domain features such as the Root Mean Square (RMS), Average Rectified Value (ARV), and Signal to Noise Ratio (SNR) were quantitatively monitored in relation to the contact pressure and load. Experiments were performed in triplicates, and the sEMG signal characteristics were observed for various loads (0, 2, 4, and 6 kg) and holding contact pressures (5, 10, and 20 mmHg). sEMG signals recorded with textile electrodes were comparable in amplitude to those recorded using typical Ag/AgCl electrodes (28.45 dB recorded), while the signal-to-noise ratios were, 11.77, 19.60, 19.91, and 20.93 dB for the different loads, and 21.33, 23.34, and 17.45 dB for different holding pressures. The signal quality increased as the elastic strap was tightened further, but a pressure higher than 20 mmHg is not recommended because of the discomfort experienced by the subjects during data collection.

The role of hydrophobic patches of de novo designed MSI-78 and VG16KRKP antimicrobial peptides on fragmenting model bilayer membranes.

Antimicrobial peptides (AMPs) with cell membrane lysing capability are considered potential candidates for the development of the next generation of antibiotics. Designing novel AMPs requires an in-depth understanding of the mechanism of action of the peptides. In this work, we used various biophysical techniques including 31P solid-state NMR to examine the interaction of model membranes with amphipathic de novo-designed peptides. Two such peptides, MSI-78 and VG16KRKP, were designed with different hydrophobicity and positive charges. The model lipid membranes were constituted by mixing lipids of varying degrees of 'area per lipid' (APL), which directly affected the packing properties of the membrane. The observed emergence of the isotropic peak in 31P NMR spectra as a function of time is a consequence of the fragmentation of the membrane mediated by the peptide interaction. The factors such as the charges, overall hydrophilicity of the AMPs, as well as lipid membrane packing, contributed to the kinetics of membrane fragmentation. Furthermore, we anticipate the designed AMPs follow the carpet and toroidal pore mechanisms when lysing the cell membrane. This study highlights the significance of the effect of the overall charges and the hydrophobicity of the novel AMPs designed for antimicrobial activity.

De-Speckling Breast Cancer Ultrasound Images Using a Rotationally Invariant Block Matching Based Non-Local Means (RIBM-NLM) Method.

The ultrasonic technique is an indispensable imaging modality for diagnosis of breast cancer in young women due to its ability in efficiently capturing the tissue properties, and decreasing nega-tive recognition rate thereby avoiding non-essential biopsies. Despite the advantages, ultrasound images are affected by speckle noise, generating fine-false structures that decrease the contrast of the images and diminish the actual boundaries of tissues on ultrasound image. Moreover, speckle noise negatively impacts the subsequent stages in image processing pipeline, such as edge detec-tion, segmentation, feature extraction, and classification. Previous studies have formulated vari-ous speckle reduction methods in ultrasound images; however, these methods suffer from being unable to retain finer edge details and require more processing time. In this study, we propose a breast ultrasound de-speckling method based on rotational invariant block matching non-local means (RIBM-NLM) filtering. The effectiveness of our method has been demonstrated by com-paring our results with three established de-speckling techniques, the switching bilateral filter (SBF), the non-local means filter (NLMF), and the optimized non-local means filter (ONLMF) on 250 images from public dataset and 6 images from private dataset. Evaluation metrics, including Self-Similarity Index Measure (SSIM), Peak Signal to Noise Ratio (PSNR), and Mean Square Error (MSE) were utilized to measure performance. With the proposed method, we were able to record average SSIM of 0.8915, PSNR of 65.97, MSE of 0.014, RMSE of 0.119, and computational speed of 82 seconds at noise variance of 20dB using the public dataset, all with p-value of less than 0.001 compared against NLMF, ONLMF, and SBF. Similarly, the proposed method achieved av-erage SSIM of 0.83, PSNR of 66.26, MSE of 0.015, RMSE of 0.124, and computational speed of 83 seconds at noise variance of 20dB using the private dataset, all with p-value of less than 0.001 compared against NLMF, ONLMF, and SBF.

Accurate Machine-Learning-Based classification of Leukemia from Blood Smear Images.

BACKGROUND: Conventional identification of blood disorders based on visual inspection of blood smears through microscope is time consuming, error-prone and is limited by hematologist's physical acuity. Therefore, an automated optical image processing system is required to support the clinical decision-making. MATERIALS AND METHODS: Blood smear slides (n = 250) were prepared from clinical samples, imaged and analyzed in Jimma Medical Center, Hematology department. Samples were collected, analyzed and preserved from out and in-patients. The system was able to categorize four common types of leukemia's such as acute and chronic myeloid leukemia; and acute and chronic lymphoblastic leukemia, through a robust image segmentation protocol, followed by classification using the support vector machine. RESULTS: The system was able to classify leukemia types with an accuracy, sensitivity, specificity of 97.69%, 97.86% and 100%, respectively for the test datasets, and 97.5%, 98.55% and 100%, respectively, for the validation datasets. In addition, the system also showed an accuracy of 94.75% for the WBC counts that include both lymphocytes and monocytes. The computer-assisted diagnosis system took less than one minute for processing and assigning the leukemia types, compared to an average period of 30 minutes by unassisted manual approaches. Moreover, the automated system complements the healthcare workers' in their efforts, by improving the accuracy rates in diagnosis from ∼70% to over 97%. CONCLUSION: Importantly, our module is designed to assist the healthcare facilities in the rural areas of sub-Saharan Africa, equipped with fewer experienced medical experts, especially in screening patients for blood associated diseases including leukemia.

Corrigendum to "Isothermal Titration Calorimetry and Surface Plasmon Resonance Analysis Using the Dynamic Approach".

[This corrects the article DOI: 10.1016/j.bbrep.2019.100712.].

Corrigendum "Isothermal titration calorimetry and surface plasmon resonance analysis using the dynamic approach" [Biochem. Biophys. Rep. 21 (2020) 100712].

[This corrects the article DOI: 10.1016/j.bbrep.2019.100712.].

Comparison of Synthetic Neuronal Model Membrane Mimics in Amyloid Aggregation at Atomic Resolution.

Alzheimer's disease (AD) is a severe neurodegenerative disorder caused by abnormal accumulation of toxic amyloid plaques of the amyloid-beta (Aß) or the tau proteins in the brain. The plaque deposition leading to the collapse of the cellular integrity is responsible for a myriad of surface phenomena acting at the neuronal lipid interface. Recent years have witnessed dysfunction of the blood-brain barriers (BBB) associated with AD. Several studies support the idea that BBB acts as a platform for the formation of misfolded Aß peptide, promoting oligomerization and fibrillation, compromising the overall integrity of the central nervous system. While the amyloid plaque deposition has been known to be responsible for the collapse of the BBB membrane integrity, the causal effect relationship between BBB and Aß amyloidogenesis remains unclear. In this study, we have used physiologically relevant synthetic model membrane systems to gain atomic insight into the functional aspects of the lipid interface. Here, we have used a minimalist BBB mimic, POPC/POPG/cholesterol/GM1, to compare with the native BBB (total lipid brain extract (TLBE)), to understand the molecular events occurring in the membrane-induced Aß40 amyloid aggregation. Our study showed that the two membrane models accelerated the Aß40 aggregation kinetics with differential secondary structural transitions of the peptide. The observed structural transitions are defined by the lipid compositions, which in turn undermines the differences in lipid surface phenomena, leading to peptide induced cellular toxicity in the neuronal membrane.

Isothermal titration calorimetry and surface plasmon resonance analysis using the dynamic approach.

Biophysical techniques such as isothermal titration calorimetry (ITC) and surface plasmon resonance (SPR) are routinely used to ascertain the global binding mechanisms of protein-protein or protein-ligand interaction. Recently, Dumas etal, have explicitly modelled the instrument response of the ligand dilution and analysed the ITC thermogram to obtain kinetic rate constants. Adopting a similar approach, we have integrated the dynamic instrument response with the binding mechanism to simulate the ITC profiles of equivalent and independent binding sites, equivalent and sequential binding sites and aggregating systems. The results were benchmarked against the standard commercial software Origin-ITC. Further, the experimental ITC chromatograms of 2'-CMP + RNASE and BH3I-1 + hBCLXL interactions were analysed and shown to be comparable with that of the conventional analysis. Dynamic approach was applied to simulate the SPR profiles of a two-state model, and could reproduce the experimental profile accurately.

Probing transient non-native states in amyloid beta fiber elongation by NMR.

Using NMR to probe transient binding of Aß1-40 monomers to fibers, we find partially bound conformations with the highest degree of interaction near F19-K28 and a lesser degree of interaction near the C-terminus (L34-G37). This represents a shift away from the KLVFFA recognition sequence (residues 16-21) currently used for inhibitor design.

Lipopolysaccharide from Gut Microbiota Modulates α-Synuclein Aggregation and Alters Its Biological Function.

Altered intestinal permeability has been correlated with Parkinson's pathophysiology in the enteric nervous system, before manifestations in the central nervous system (CNS). The inflammatory endotoxin or lipopolysaccharide (LPS) released by gut bacteria is known to modulate α-synuclein amyloidogenesis through the formation of intermediate nucleating species. Here, biophysical techniques in conjunction with microscopic images revealed the molecular interaction between lipopolysaccharide and α-synuclein that induce rapid nucleation events. This heteromolecular interaction stabilizes the α-helical intermediates in the α-synuclein aggregation pathway. Multitude NMR studies probed the residues involved in the LPS-binding structural motif that modulates the nucleating forms, affecting the cellular internalization and associated cytotoxicity. Collectively, our data characterizes this heteromolecular interaction associated with an alternative pathway in Parkinson's disease progression.

Log D analysis using dynamic approach.

Log D the logarithm ( log10 ) of the distribution coefficient ( D ), is one of the important parameters used in Lipinski's rule to assess the druggability of a molecule in pharmaceutical formulations. The distribution of a molecule between a hydrophobic organic phase and an aqueous buffer phase is influenced by the pH of the buffer system. In this work, we used both the conventional algebraic method and the generalized 'dynamic' approach to model the distribution coefficient of amphoteric, diamino-monoprotic molecule and monoprotic acid in the presence of salt or co-solvent. We have shown the equivalence of these methods by analysing the recently reported experimental data of amphoteric molecules such as nalidixic acid, mebendazole, benazepril and telmisartan.

Binding Moiety Mapping by Saturation Transfer Difference NMR.

Saturation transfer difference (STD) NMR has emerged as one of the key technologies in lead optimization during drug design. Unlike most biophysical assays which report only on the binding affinity, STD NMR reports simultaneously on both the binding affinity and the structure of the binding ligand/protein complex. The STD experiment drives magnetization from a protein to a bound small molecule ligand which carries away the memory of the saturation signal when it dissociates. Since the transfer of saturation is distance dependent, STD NMR can be used to map the specific atoms on the ligand in contact with a protein receptor allowing the impact of any structural change in the binding site to be mapped directly on to the individual functional groups responsible when a suitable compound library is screened. Because the signal is detected from the free ligand and not the bound complex, it can be used on a much wider range of systems than protein-detected NMR and has the advantage of more directly reporting on distances than changes in chemical shifts alone. The STD experiment, while deceptively simple, is very sensitive to both sample conditions and acquisition parameters. We present a general protocol for setting up and STD NMR experiment with a particular focus on how choices in sample conditions and acquisition parameters affect the outcome of the experiment.

Inhibition and Degradation of Amyloid Beta (Aß40) Fibrillation by Designed Small Peptide: A Combined Spectroscopy, Microscopy, and Cell Toxicity Study.

A designed nontoxic, nonhemolytic 11-residue peptide, NF11 (NAVRWSLMRPF), not only inhibits the aggregation of amyloid beta (Aß40) protein but also disaggregates the preformed oligomers and mature Aß fibrils, thereby reducing associated-toxicity. NMR experiments provide evidence of NF11's ability to inhibit fibril formation, primarily through interaction with the N-terminus region as well as the central hydrophobic cluster of Aß40. NF11 has micromolar binding affinity toward both monomeric and aggregated species for efficient clearance of toxic aggregates. From these in vitro results, the future development of a next generation peptidomimetic therapeutic agent for amyloid disease may be possible.

High-resolution NMR characterization of low abundance oligomers of amyloid-ß without purification.

Alzheimer's disease is characterized by the misfolding and self-assembly of the amyloidogenic protein amyloid-ß (Aß). The aggregation of Aß leads to diverse oligomeric states, each of which may be potential targets for intervention. Obtaining insight into Aß oligomers at the atomic level has been a major challenge to most techniques. Here, we use magic angle spinning recoupling (1)H-(1)H NMR experiments to overcome many of these limitations. Using (1)H-(1)H dipolar couplings as a NMR spectral filter to remove both high and low molecular weight species, we provide atomic-level characterization of a non-fibrillar aggregation product of the Aß1-40 peptide using non-frozen samples without isotopic labeling. Importantly, this spectral filter allows the detection of the specific oligomer signal without a separate purification procedure. In comparison to other solid-state NMR techniques, the experiment is extraordinarily selective and sensitive. A resolved 2D spectra could be acquired of a small population of oligomers (6 micrograms, 7% of the total) amongst a much larger population of monomers and fibers (93% of the total). By coupling real-time (1)H-(1)H NMR experiments with other biophysical measurements, we show that a stable, primarily disordered Aß1-40 oligomer 5-15 nm in diameter can form and coexist in parallel with the well-known cross-ß-sheet fibrils.

Probing the sources of the apparent irreproducibility of amyloid formation: drastic changes in kinetics and a switch in mechanism due to micellelike oligomer formation at critical concentrations of IAPP.

The aggregation of amyloidogenic proteins is infamous for being highly chaotic, with small variations in conditions sometimes leading to large changes in aggregation rates. Using the amyloidogenic protein IAPP (islet amyloid polypeptide protein, also known as amylin) as an example, we show that a part of this phenomenon may be related to the formation of micellelike oligomers at specific critical concentrations and temperatures. We show that pyrene fluorescence can sensitively detect micellelike oligomer formation by IAPP and discriminate between micellelike oligomers from fibers and monomers, making pyrene one of the few chemical probes specific to a prefibrillar oligomer. We further show that oligomers of this type reversibly form at critical concentrations in the low micromolar range and at specific critical temperatures. Micellelike oligomer formation has several consequences for amyloid formation by IAPP. First, the kinetics of fiber formation increase substantially as the critical concentration is approached but are nearly independent of concentration below it, suggesting a direct role for the oligomers in fiber formation. Second, the critical concentration is strongly correlated with the propensity to form amyloid: higher critical concentrations are observed for both IAPP variants with lower amyloidogenicity and for native IAPP at acidic pH in which aggregation is greatly slowed. Furthermore, using the DEST NMR technique, we show that the pathway of amyloid formation switches as the critical point is approached, with self-interactions primarily near the N-terminus below the critical temperature and near the central region above the critical temperature, reconciling two apparently conflicting views of the initiation of IAPP aggregation.