DeepLeish: a deep learning based support system for the detection of Leishmaniasis parasite from Giemsa-stained microscope images.

BACKGROUND: Leishmaniasis is a vector-born neglected parasitic disease belonging to the genus Leishmania. Out of the 30 Leishmania species, 21 species cause human infection that affect the skin and the internal organs. Around, 700,000 to 1,000,000 of the newly infected cases and 26,000 to 65,000 deaths are reported worldwide annually. The disease exhibits three clinical presentations, namely, the cutaneous, muco-cutaneous and visceral Leishmaniasis which affects the skin, mucosal membrane and the internal organs, respectively. The relapsing behavior of the disease limits its diagnosis and treatment efficiency. The common diagnostic approaches follow subjective, error-prone, repetitive processes. Despite, an ever pressing need for an accurate detection of Leishmaniasis, the research conducted so far is scarce. In this regard, the main aim of the current research is to develop an artificial intelligence based detection tool for the Leishmaniasis from the Geimsa-stained microscopic images using deep learning method. METHODS: Stained microscopic images were acquired locally and labeled by experts. The images were augmented using different methods to prevent overfitting and improve the generalizability of the system. Fine-tuned Faster RCNN, SSD, and YOLOV5 models were used for object detection. Mean average precision (MAP), precision, and Recall were calculated to evaluate and compare the performance of the models. RESULTS: The fine-tuned YOLOV5 outperformed the other models such as Faster RCNN and SSD, with the MAP scores, of 73%, 54% and 57%, respectively. CONCLUSION: The currently developed YOLOV5 model can be tested in the clinics to assist the laboratorists in diagnosing Leishmaniasis from the microscopic images. Particularly, in low-resourced healthcare facilities, with fewer qualified medical professionals or hematologists, our AI support system can assist in reducing the diagnosing time, workload, and misdiagnosis. Furthermore, the dataset collected by us will be shared with other researchers who seek to improve upon the detection system of the parasite. The current model detects the parasites even in the presence of the monocyte cells, but sometimes, the accuracy decreases due to the differences in the sizes of the parasite cells alongside the blood cells. The incorporation of cascaded networks in future and the quantification of the parasite load, shall overcome the limitations of the currently developed system.

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.

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.

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.

Rational design of a structural framework with potential use to develop chemical reagents that target and modulate multiple facets of Alzheimer's disease.

Alzheimer's disease (AD) is characterized by multiple, intertwined pathological features, including amyloid-ß (Aß) aggregation, metal ion dyshomeostasis, and oxidative stress. We report a novel compound (ML) prototype of a rationally designed molecule obtained by integrating structural elements for Aß aggregation control, metal chelation, reactive oxygen species (ROS) regulation, and antioxidant activity within a single molecule. Chemical, biochemical, ion mobility mass spectrometric, and NMR studies indicate that the compound ML targets metal-free and metal-bound Aß (metal-Aß) species, suppresses Aß aggregation in vitro, and diminishes toxicity induced by Aß and metal-treated Aß in living cells. Comparison of ML to its structural moieties (i.e., 4-(dimethylamino)phenol (DAP) and (8-aminoquinolin-2-yl)methanol (1)) for reactivity with Aß and metal-Aß suggests the synergy of incorporating structural components for both metal chelation and Aß interaction. Moreover, ML is water-soluble and potentially brain permeable, as well as regulates the formation and presence of free radicals. Overall, we demonstrate that a rational structure-based design strategy can generate a small molecule that can target and modulate multiple factors, providing a new tool to uncover and address AD complexity.

Potent γ-secretase inhibitors/modulators interact with amyloid-ß fibrils but do not inhibit fibrillation: a high-resolution NMR study.

Recently, γ-secretase modulators (GSM) have been shown to interact directly with the amyloid precursor protein (APP) and simultaneously inhibit the activity of the Presenilin domain of γ-secretase. A clear understanding of the molecular recognition pathways by which GSM can target both γ-secretase and Aß precursor protein can lead to the development of more effective inhibitors. To examine whether this direct interaction with APP affects the downstream Aß fibril formation, we chose to investigate three different molecules in this study: Sulindac sulfide, Semagacestat and E2012 from the class of generation I GSMs, γ-secretase inhibitors (GSI), and generation II GSM molecules, respectively. Firstly, through NMR based ligand titration, we identified that Sulindac sulfide and Semagacestat interact strongly with Aß40 monomers, whereas E2012 does not. Secondly, using saturation transfer difference (STD) NMR experiments, we found that all three molecules bind equally well with Aß40 fibrils. To determine if these interactions with the monomer/fibril lead to a viable inhibition of the fibrillation process, we designed an NMR based time-dependent assay and accurately distinguished the inhibitors from the non-inhibitors within a short period of 12h. Based on this pre-seeded fibril assay, we conclude that none of these molecules inhibit the ongoing fibrillation, rather ligands such as Semagacestat and E2012 accelerated the rate of aggregation.

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.

Lipid composition-dependent membrane fragmentation and pore-forming mechanisms of membrane disruption by pexiganan (MSI-78).

The potency and selectivity of many antimicrobial peptides (AMPs) are correlated with their ability to interact with and disrupt the bacterial cell membrane. In vitro experiments using model membranes have been used to determine the mechanism of membrane disruption of AMPs. Because the mechanism of action of an AMP depends on the ability of the model membrane to accurately mimic the cell membrane, it is important to understand the effect of membrane composition. Anionic lipids that are present in the outer membrane of prokaryotes but are less common in eukaryotic membranes are usually thought to be key for the bacterial selectivity of AMPs. We show by fluorescence measurements of peptide-induced membrane permeabilization that the presence of anionic lipids at high concentrations can actually inhibit membrane disruption by the AMP MSI-78 (pexiganan), a representative of a large class of highly cationic AMPs. Paramagnetic quenching studies suggest MSI-78 is in a surface-associated inactive mode in anionic sodium dodecyl sulfate micelles but is in a deeply buried and presumably more active mode in zwitterionic dodecylphosphocholine micelles. Furthermore, a switch in mechanism occurs with lipid composition. Membrane fragmentation with MSI-78 can be observed in mixed vesicles containing both anionic and zwitterionic lipids but not in vesicles composed of a single lipid of either type. These findings suggest membrane affinity and membrane permeabilization are not always correlated, and additional effects that may be more reflective of the actual cellular environment can be seen as the complexity of the model membranes is increased.

Resolution of oligomeric species during the aggregation of Aß1-40 using (19)F NMR.

In the commonly used nucleation-dependent model of protein aggregation, aggregation proceeds only after a lag phase in which the concentration of energetically unfavorable nuclei reaches a critical value. The formation of oligomeric species prior to aggregation can be difficult to detect by current spectroscopic techniques. By using real-time (19)F NMR along with other techniques, we are able to show that multiple oligomeric species can be detected during the lag phase of Aß1-40 fiber formation, consistent with a complex mechanism of aggregation. At least six types of oligomers can be detected by (19)F NMR. These include the reversible formation of large ß-sheet oligomer immediately after solubilization at high peptide concentration, a small oligomer that forms transiently during the early stages of the lag phase, and four spectroscopically distinct forms of oligomers with molecular weights between ∼30 and 100 kDa that appear during the later stages of aggregation. The ability to resolve individual oligomers and track their formation in real-time should prove fruitful in understanding the aggregation of amyloidogenic proteins and in isolating potentially toxic nonamyloid oligomers.

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.

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.

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.

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.

Open-ITC: an alternate computational approach to analyze the isothermal titration calorimetry data of complex binding mechanisms.

Isothermal titration calorimetry (ITC) is an important technique used in quantitatively analyzing the global mechanism of protein-protein or protein-ligand interactions through thermodynamic measurements. Among different binding mechanisms, the parallel and ligand induced protein oligomerization mechanisms are technically difficult to analyze compared with a sequential binding mechanism. Here, we present a methodology implemented as a program "Open-ITC" that eliminates the need for exact analytical expressions for free ligand concentrations [L] and mole fractions of bound ligand θ that are required for the thermogram analysis. Adopting a genetic algorithm-based optimization, the thermodynamic parameters are determined, and its standard error is evaluated at the global minimum by calculating the Jacobian matrix. This approach yielded a statistically consistent result for a single-site and a two-site binding protein-ligand system. Further, a comparative simulation of a two-step sequential, a parallel, and a ligand induced oligomerization model revealed that their mechanistic differences are discernable in ITC thermograms, only if the first binding step is weaker compared with the second binding step (K(1)

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.].

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.

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.

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.