A potential function for MicroRNA-124 in normal and pathological bone conditions

Cells produce short single-stranded non-coding RNAs (ncRNAs) called microRNAs (miRNAs), which actively regulate gene expression at the posttranscriptional level. Several miRNAs have been observed to exert significant impacts on bone health and bone-related disorders. One of these, miR-124, is observed in bone microenvironments and is conserved across species. It affects bone cell growth and differentiation by activating different transcription factors and signaling pathways. In-depth functional analyses of miR-124 have revealed several physiological and pathological roles exerted through interactions with other ncRNAs. Deciphering these RNA-mediated signaling networks and pathways is essential for understanding the potential impacts of dysregulated miRNA functions on bone biology. In this review, we aim to provide a comprehensive analysis of miR-124's involvement in bone physiology and pathology. We highlight the importance of miR-124 in controlling transcription factors and signaling pathways that promote bone growth. This review reveals therapeutic implications for the treatment of bone-related diseases.

Neural harmony: revolutionizing thyroid nodule diagnosis with hybrid networks and genetic algorithms

In the contemporary world, thyroid disease poses a prevalent health issue, particularly affecting women's well-being. Recognizing the significance of maternal thyroid (MT) hormones in fetal neurodevelopment during the first half of pregnancy, this study introduces the HNN-GSO model. This groundbreaking hybrid approach, utilizing the MT dataset, integrates ResNet-50 and Artificial Neural Network (ANN) within a Glow-worm Swarm Optimization (GSO) framework for optimal parameter tuning. With a comprehensive methodology involving dataset preprocessing and Genetic Algorithm (GA) for feature selection, our model leverages ResNet-50 for feature extraction and ANN for classification tasks. Implemented in Python, the HNN-GSO model outperforms existing models, including K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), ResNet, GoogleNet, and ANN, achieving an impressive accuracy rate of 98%. This success underscores the effectiveness of our approach in complex classification tasks within machine learning (ML) and pattern recognition, specifically tailored to the Thyroid Ultrasound Images (TUI) Dataset. To provide a comprehensive understanding of performance, additional statistical measures such as precision, recall, and F1 score were considered. The HNN-GSO model consistently outperformed competitors across these metrics, showcasing its superiority in MT classification. The HNN-GSO model seamlessly combines ResNet-50's feature extraction, ANN's classification robustness, and GSO's optimization for unparalleled performance. This research offers a promising framework for advancing ML methodologies, enhancing accuracy, and efficiency in classification tasks related to MT health.

Histological characterization and development of mesial surface sulci in the human brain at 13-15 gestational weeks through high-resolution histology

Cellular-level anatomical data from early fetal brain are sparse yet critical to the understanding of neurodevelopmental disorders. We characterize the organization of the human cerebral cortex between 13 and 15 gestational weeks using high-resolution whole-brain histological data sets complimented with multimodal imaging. We observed the heretofore underrecognized, reproducible presence of infolds on the mesial surface of the cerebral hemispheres. Of note at this stage, when most of the cerebrum is occupied by lateral ventricles and the corpus callosum is incompletely developed, we postulate that these mesial infolds represent the primordial stage of cingulate, callosal, and calcarine sulci, features of mesial cortical development. Our observations are based on the multimodal approach and further include histological three-dimensional reconstruction that highlights the importance of the plane of sectioning. We describe the laminar organization of the developing cortical mantle, including these infolds from the marginal to ventricular zone, with Nissl, hematoxylin and eosin, and glial fibrillary acidic protein (GFAP) immunohistochemistry. Despite the absence of major sulci on the dorsal surface, the boundaries among the orbital, frontal, parietal, and occipital cortex were very well demarcated, primarily by the cytoarchitecture differences in the organization of the subplate (SP) and intermediate zone (IZ) in these locations. The parietal region has the thickest cortical plate (CP), SP, and IZ, whereas the orbital region shows the thinnest CP and reveals an extra cell-sparse layer above the bilaminar SP. The subcortical structures show intensely GFAP-immunolabeled soma, absent in the cerebral mantle. Our findings establish a normative neurodevelopment baseline at the early stage.

Chitosan nanocarriers for non-coding RNA therapeutics: A review

Non-coding RNA (ncRNA)-based therapies entail delivering ncRNAs to cells to regulate gene expression and produce proteins that combat infections, cancer, neurological diseases, and bone abnormalities. Nevertheless, the therapeutic potential of these ncRNAs has been limited due to the difficulties in delivering them to specific cellular targets within the body. Chitosan (CS), a biocompatible cationic polymer, interacts with negatively charged RNA molecules to form stable complexes. It is a promising biomaterial to develop nanocarriers for ncRNA delivery, overcoming several disadvantages of traditional delivery systems. CS-based nanocarriers can protect ncRNAs from degradation and target-specific delivery by surface modifications and intracellular release profiles over an extended period. This review briefly summarizes the recent developments in CS nanocarriers' synthesis and design considerations and their applications in ncRNA therapeutics for treating various diseases. We also discuss the challenges and limitations of CS-based nanocarriers for ncRNA therapeutics and potential strategies for overcoming these challenges.

Wide field block face imaging using deep ultraviolet induced autofluorescence of the human brain

BACKGROUND

Imaging large volume human brains at cellular resolution involve histological methods that cause structural changes. A reference point prior to sectioning is needed to quantify these changes and is achieved by serial block face imaging (BFI) methods that have been applied to small volume tissue (∼1 cm3).

NEW METHOD

We have developed a BFI uniquely designed for large volume tissues (∼1300 cm3) with a very large field of view (20 × 20 cm) at a resolution of 70 µm/pixel under deep ultraviolet (UV-C) illumination which highlights key features.

RESULTS

The UV-C imaging ensures high contrast imaging of the brain tissue and highlights salient features of the brain. The system is designed to provide uniform and stable illumination across the entire surface area of the tissue and to work at low temperatures, which are required during cryosectioning. Most importantly, it has been designed to maintain its optical focus over the large depth of tissue and over long periods of time, without readjustments. The BFI was installed within a cryomacrotome, and was used to image a large cryoblock of an adult human cerebellum and brainstem (∼6 cm depth resulting in 2995 serial images) with precise optical focus and no loss during continuous serial acquisition.

COMPARISON WITH EXISTING METHOD(S)

The deep UV-C induced BFI highlights several large fibre tracts within the brain including the cerebellar peduncles, and the corticospinal tract providing important advantage over white light BFI.

CONCLUSIONS

The 3D reconstructed serial BFI images can assist in the registration and alignment of the microscopic high-resolution histological tissue sections.

Face-Free Chest Detection Using Convolutional Neural Networks for Non-Contact Respiration Monitoring

Non-contact methods for monitoring respiration face limitations when it comes to selecting the chest region of interest. The semi-automatic method, which requires the user to select the chest region in the first frame, is not suitable for real-time applications. The automatic method, which tracks the face first and then detects the chest region based on the face's position, can be inaccurate if the face is not visible or is rotated. Moreover, using the face region to track the chest region can under-utilize camera pixels since the face is not essential for monitoring respiration. This approach may adversely affect the quality of the respiration signal being measured. To address these issues, we propose a face-free chest detection model based on Convolutional Neural Networks. Our model enhances the measured non-contact respiration signal quality and utilizes more pixels for the chest region alone. In our quantitative study, we demonstrate that our method outperforms traditional methods that require the presence of the face. This approach offers potential benefits for real-time, non-contact respiration monitoring applicationsClinical relevance- This work enhances the performance of non-contact respiration monitoring techniques by precisely detecting the chest region without the need of face in it through a CNN-based model. The use of the CNN-based chest detection model also enhances the real-time monitoring capabilities of non-contact respiration monitoring techniques.

An effective ontology-based query response model for risk assessment in urban flood disaster management

The drastic advancements in the field of Information Technology make it possible to analyze, manage and handle large-scale environment data and spatial information acquired from diverse sources. Nevertheless, this process is a more challenging task where the data accessibility has been performed in an unstructured, varied, and incomplete manner. The appropriate extraction of information from diverse data sources is crucial for evaluating natural disaster management. Therefore, an effective framework is required to acquire essential information in a structured and accessible manner. This research concentrates on modeling an efficient ontology-based evaluation framework to facilitate the queries based on the flood disaster location. It offers a reasoning framework with spatial and feature patterns to respond to the generated query. To be specific, the data is acquired from the urban flood disaster environmental condition to perform data analysis hierarchically and semantically. Finally, data evaluation can be accomplished by data visualization and correlation patterns to respond to higher-level queries. The proposed ontology-based evaluation framework has been simulated using the MATLAB environment. The result exposes that the proposed framework obtains superior significance over the existing frameworks with a lesser average query response time of 7 seconds.

A novel approach to improve network validity using various soft computing techniques

Mobile Adhoc Networks (MANET) in modern research have many optimal energy conservation mechanisms that can be deployed easily and in a faster manner. The routing approaches associated with energy consumption play a dominant role in routing the data packets between the mobile sensor nodes within the range of optimization. However, major challenges associated with energy consumption in MANETs include reduced lifetime of sensor nodes, poor coverage, and throughput. Most methods tend to reduce the interference of data while traversing between the sensor nodes and increase the capacity of the network. This results in delays while transmitting the packets across the network, and this may result in failure of packets being transmitted. To resolve this issue, in this paper, we propose an ant colony optimization combined with a flower pollination algorithm for minimal energy consumption and throughput maximisation in MANETs. This hybrid meta-heuristic model resolves the issues, including delays, poor coverage, and reduced network lifetime. This hybrid model uses the estimation of neighbourhood distance among the nodes for optimal placement of nodes for effective location. The estimation of location is found using a flower pollination algorithm with a levy flight mechanism. The estimation is carried out in a hyper sphere model that helps in finding the coverage area of the sensor nodes. Depending upon the estimation of neighbourhood distance among the sensor nodes, the consumption of energy among the sensor nodes in MANETs is reduced. The simulation was conducted between the proposed hybrid approach and conventional soft computing heuristics, where the results show that the proposed model achieves a higher rate of energy conservation and reduces delay than other methods.

Deep learning based non-contact physiological monitoring in Neonatal Intensive Care Unit

Preterm babies in the Neonatal Intensive Care Unit (NICU) have to undergo continuous monitoring of their cardiac health. Conventional monitoring approaches are contact-based, making the neonates prone to various nosocomial infections. Video-based monitoring approaches have opened up potential avenues for contactless measurement. This work presents a pipeline for remote estimation of cardiopulmonary signals from videos in NICU setup. We have proposed an end-to-end deep learning (DL) model that integrates a non-learning-based approach to generate surrogate ground truth (SGT) labels for supervision, thus refraining from direct dependency on true ground truth labels. We have performed an extended qualitative and quantitative analysis to examine the efficacy of our proposed DL-based pipeline and achieved an overall average mean absolute error of 4.6 beats per minute (bpm) and root mean square error of 6.2 bpm in the estimated heart rate.

Camera fusion for real-time temperature monitoring of neonates using deep learning

Abstract

The continuous monitoring of vital signs is a crucial aspect of medical care in neonatal intensive care units. Since cable-based sensors pose a potential risk for the immature skin of preterm infants, unobtrusive monitoring techniques using camera systems are increasingly investigated. The combination of deep learning–based algorithms and camera modalities such as RGB and infrared thermography can improve the development of cable-free methods for the extraction of vital parameters. In this study, a real-time approach for local extraction of temperatures on the body surface of neonates using a multi-modal clinical dataset was implemented. Therefore, a trained deep learning–based keypoint detector was used for body landmark prediction in RGB. Image registration was conducted to transfer the RGB points to the corresponding thermographic recordings. These landmarks were used to extract the body surface temperature in various regions to determine the central-peripheral temperature difference. A validation of the keypoint detector showed a mean average precision of 0.82. The registration resulted in mean absolute errors of 16.4 px (8.2 mm) for x and 22.4 px (11.2 mm) for y. The evaluation of the temperature extraction revealed a mean absolute error of 0.55 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^{\circ }$$\end{document}∘C. A final performance of 31 fps was observed on the NVIDIA Jetson Xavier NX module, which proves real-time capability on an embedded GPU system. As a result, the approach can perform real-time temperature extraction on a low-cost GPU module.

Graphical abstract

Using patching asymmetric regions to assess ischemic stroke lesion in neuro imaging

Ischemic stroke is a universal ailment that endangers the life of patients and makes them bedridden until death. Over a decade, doctors and radiologists have been dissecting patient status straightforwardly from the printouts of the slice images delivered by different diagnostic imaging modalities. Computed Tomography (CT) is a frequently used imaging strategy for therapeutic analysis and neuroanatomical investigations. The main objective of the paper is to develop a simple technique with less architectural complication and power consumption. The proposed work is to section the ischemic stroke lesion more efficiently from multi-succession CT images using patching the asymmetric region. The Hough transform segment and extracts the features from the asymmetric region of the CT image and finally, the random forest is implemented to classify the unusual tissues from the CT image dependent on their pathological properties. RF classifier has been trained for different parts of the cerebrum for fragmenting the stroke lesion. The acquired outcomes produce better segmentation accuracy when compared with different strategies. The overall efficiency of the proposed method determines the Ischemic stroke with an accuracy of 95% with an RF classifier. Hence this method can be used in the segmentation process of stroke lesions.

Enhanced energy efficient routing scheme based ant colony optimization

Mobile Ad hoc Networks (MANETs) are independent of central administration or any infrastructure, hence it is flexible in nature. Though, the network is self organizing, the mobile nodes have some resource constraints. There is always a requirement forefficient routing protocol to manage the energy consumption and reduce the energy wastages in MANETWith that in mind, this article proposes Ant Colony Optimization for Enhanced Energy Efficient Routing (EEER-ACO). Furthermore, the network design balances the Transition Probability Standard (TPS)offset Coefficient to maximize navigation processing effectiveness and decrease path finding packets. Furthermore, the Surviving route lifespan is calculated based upon that node’s position and speed rate. Through incorporating the Residual Node Lifetime (RNL) and the Residual Link Lifetime (RLL), the Residual Node Lifetime (RNL), the ACO based pheromone has been designed. Further, the algorithm involves in choosing an optimal quality route for assuring continuous and efficient data packet transmission over the defined MANET. The investigation took into account the energy consumption of nodes as well as associated motility. Furthermore, the results indicate that the EEER-ACO algorithm improves network durability by reducing end-to-end delay, data packet loss, and path discovery rate. In comparison to previous algorithms, the proposed study has demonstrated that it achieves a 35 percent better performance than traditional protocols.

Using a recurrent neural network with S2 characteristics, efficient identification of localised cortical dysplasia

Focal cortical dysplasia (FCD) is an inborn anomaly in brain growth and morphological deformation in lesions of the brain which induces focal seizures. Neurosurgical therapies were performed for the detection of FCD. Furthermore, it can be overcome through the presurgical evaluation of epilepsy. The surgical result is attained basically through the output of the presurgical output. In preprocessing the process of increasing true positives with the decrease in false negatives occurs which results in an effective outcome. MRI (Magnetic Resonance Imaging) outputs are efficient to predict the FCD lesions through T1- MPRAGE and T2- FLAIR efficient output can be obtained. In our proposed work we extract the S2 features through the testing of T1, T2 images. Using RNN-LSTM (Recurrent neural network-Long short-term memory) test images were trained and the FCD lesions were segmented. The output of our work is compared with the proposed work yields better results compared to the existing system such as artificial neural network (ANN), support vector machine (SVM), and convolution neural network (CNN). This approach obtained an accuracy rate of 0.195% (ANN), 0.20% (SVM), 0.14% (CNN), specificity rate of 0.23% (ANN), 0.15% (SVM), 0.13% (CNN) and sensitivity rate of 0.22% (ANN), 0.14% (SVM), 0.08% (CNN) respectively in comparison with RNN-LSTM.

Ultra-Low Power and High Sensitivity of Joint Clock Gating Based Dual Feedback Edge Triggered Flip Flop for Biomedical Imaging Applications

To adapt to varied working situations, the latest biomedical imaging applications require low energy consumption, high performance, and extensive energy-performance scalability. State-of-the-art electronics with higher sensitivity, higher counting rate, and finer time resolution are required to create higher precision, higher temporal resolution, and maximum contrast biomedical images. In recent days, the system’s power consumption is important critically in modern VLSI circuits particularly for the low power application. In order to decrease the power, a power optimization technique must be used at various design levels. The low power use of logic cells is a proficient technique for decreasing the circuit level power. Dual Feedback edge triggered Flip Flop (DFETFF) is considered for biomedical imaging applications in the proposed system. Initially, the high dynamic range voltage is given as input signal. The comparator output is then retried at the comparator end. The integration capacitor is employed for storing remaining voltage signal. The comparator voltage is then given to the capacitor reset block. In the proposed work, a capacitor-reset block that employs clock signal takes up a dual-feedbackedge-triggered Flip-flop as an alternative of a conventional type for reducing the final output signals errors. Dual feedback loops assure that feedback loops do not tri-state at the time of SET restoration, a scheme that could lead to SEUs in latches if a single delay component and a single feedback loop are used. In digital system, Clock gating is a competent method of lessening the overall consumption of power along with deactivating the clock signal selectively and is useful for controlling the usage of clock signal asynchronously in reference to input-signal current. The integration-control (Vint) signal is employed in controlling the integration time. On the termination of integration, the signal level phase is kept, also similar one is send to arrangement all through read period. As a result, the simulation was carried out after the design layout and the estimations of performance were made and are compared with traditional approaches to prove the proposed mechanism effectiveness for future biomedical applications.

Efficient Detection Using Soft Computing Approach of Modified Fuzzy C-Means Based Outlier Detection in Electronics Patient Records Systems

Outliers are the data objects or data elements which are deviated from the observation or population in the dataset. Normally outliers are considered as noise data but in recent days outliers are taken vital role in the applications like healthcare systems to energetically check for any malignant, irregular, or abnormal behaviour. Accuracy of the outlier detection is purely based upon the efficiency of outlier detection methods and application where the outlier detection is involved. In this paper we design hybrid three phase modified fuzzy c-means and diverse distance based outlier detection method for distributed dataset to detect the unusual usage of data and illegitimate approach in large-scale integral networks, especially in healthcare sectors (EPR systems). The proposed algorithm combining the features of modified C-Means Fuzzification, Z-score and Manhattan distance in outlier identification. The proposed algorithm provides efficiency in outlier detection on univariate EPRdata. This paper also mainly focuses the application intrusion detection in healthcare data. The algorithm is tested on real world dataset of machine learning database repository (UCIML).

Interpretation of Gait Supervising Mechanism Using Sensor Integrated Makeshift and Analysing Pattern by K-Means Clustering Algorithm

Human mobility or walking pattern(gait) is described as the interpreter movements of the rotatory body to achieve extensive range of locomotion. Gait analysis is foremost widely used technique for identifying abnormalities in the lower extremities and gait characteristics essentially support HAT (Head, Arm & Trunk). The act of walking is unconscious when there are no dysfunctions, but for ambulated the continuous monitoring is required. The existing clinical analysis method couldn’t achieve the daily walking routine within the confinement of a room.The proposed method focuses on developing an ambulatory system on daily routines by incorporating feasible techniques for achieving the gait pattern which is not confined to a room atmosphere where all possibilities of walking pattern can’t be reached.This system has expounded an ideology, to interpret the gait parameters using an insole type shoe integrated sensor system. Here, a wearable gait system which is incorporated with force resistive sensors, piezo sensors, inertial sensors and IR sensors are interfaced to the ESP 32. The corresponding sensors extract the data of kinematic angles, kinetics, foot pressure, step count and foot stride investigations.The system proved to be efficient in finding the phases and orientation of the individual by interpreting values from the device. Acquired data can be clustered together to find the abnormal and normal values by applying K-Means clustering algorithm, later the values are utilized in biomechanics for rectifying posture or movement related problems.The device will have several applications in sports, rehabilitation medicine and post-surgery treatment.

Role of seminal MDA, ROS, and antioxidants in cryopreservation and their kinetics under the influence of ejaculatory abstinence in bovine semen

Despite many cryopreservation techniques in bovine semen, various stressors' detrimental effects remain a significant issue. The present study targeted to assess the role of semen quality parameters, sperm function tests, lipid peroxidation, reactive oxygen species (ROS), and different antioxidants in the cryopreservation of bovine semen. Further, the kinetics of lipid peroxidation, ROS, and antioxidants on repeated semen collection under short ejaculatory abstinence were studied. We designed a comparative study where bulls were grouped into good and low freezable semen groups (Freeze-groups) based on their post-thaw motility. All the bulls included had similar initial motility and qualified minimum standards for initial semen parameters viz. semen volume and sperm concentration. The present study detected a higher lipid peroxidation and ROS viz. superoxide anions (•O2-) and a lower total antioxidant capacity (TAC) in the low freeze-group compared to the good freeze-group. The ROS and antioxidants showed unique kinetics on repeated semen collection at short intervals, and no significant change was detected in semen volume, sperm motility, and sperm concentration. This study detected higher head abnormalities and poor acrosome integrity in the low freeze-groups. The present study results indicated that the sperm head might be the most vulnerable part of the sperm to cryopreservation stress. The present study finds significantly higher lipid peroxidation and ROS levels and reduced antioxidant capacity as the primary reasons for low cryopreservability. Further, repeated semen collection with a shorter or lack of abstinence does not impose any significant change in the semen volume and sperm concentration; moreover, it could be beneficial for higher antioxidant levels and lower lipid peroxidation levels. As seminal plasma has both inhibitory and stimulatory roles in sperm function and cryopreservation, identifying the critical role players of seminal plasma and identifying sperm related changes in cryopreservation could predict the cryopreservability potential of semen.

Pathological and immunological protection induced by inactivated reverse genetics-based H3N8 equine influenza vaccine candidate in murine model

Equine influenza (EI) is an important viral respiratory disease of equines caused by influenza A virus (IAV). The antigenic drift in IAVs necessitates regular updating and harmonization of vaccine strain with the circulating virus. The reverse genetics-based recombinant viruses could be easy instrument in generating vaccine against circulating virus in a quick and effective manner. Present study has been envisaged to evaluate the immunogenicity and protective efficacy of inactivated recombinant equine influenza virus (rgEIV) vaccine candidate having six segments from H1N1 virus (A/WSN/33/H1N1) and HA (hemaglutinin) and NA (neuraminidase) segments from H3N8 equine influenza virus [(A/eq/Jammu-Katra/06/08) of clade 2 of Florida sublineage] generated through reverse genetic engineering. BALB/c mice were immunized with inactivated rgEIV adjuvanted with aluminium hydroxide gel and challenged with H3N8 virus (A/eq/Jammu-Katra/06/08). The protective efficacy was evaluated through serology, cytokine profiling, clinical signs, gross and histopathological changes, immunohistochemistry and residual virus quantification. Immunizations induced robust humoral immune response as estimated through hemagglutination inhibition assay (HAI). The antibodies were isotyped and the predominant subclass was IgG1. The vaccine candidate produced mixed Th1 and Th2 responses through stimulation of IFN-γ, IL-2, IL-4 and IL-6 expression. Immunization protected mice against challenge as reflected through reduction in clinical signs and body weight loss, early recovery, mild pathological changes (gross and histopathological lesions) as evident through scoring of lesions, low residual virus in nasopharynx and lungs quantified through egg titration and quantitative reverse transcriptase PCR (qRT-PCR). The study demonstrates that inactivated recombinant EIV generated through reverse genetic approach provides equivalent protection to that observed with inactivated whole H3N8 EIV vaccine. Keywords: equine influenza; reverse genetics; vaccine; pathology; murine model.

Fast body part segmentation and tracking of neonatal video data using deep learning

Photoplethysmography imaging (PPGI) for non-contact monitoring of preterm infants in the neonatal intensive care unit (NICU) is a promising technology, as it could reduce medical adhesive-related skin injuries and associated complications. For practical implementations of PPGI, a region of interest has to be detected automatically in real time. As the neonates' body proportions differ significantly from adults, existing approaches may not be used in a straightforward way, and color-based skin detection requires RGB data, thus prohibiting the use of less-intrusive near-infrared (NIR) acquisition. In this paper, we present a deep learning-based method for segmentation of neonatal video data. We augmented an existing encoder-decoder semantic segmentation method with a modified version of the ResNet-50 encoder. This reduced the computational time by a factor of 7.5, so that 30 frames per second can be processed at 960 × 576 pixels. The method was developed and optimized on publicly available databases with segmentation data from adults. For evaluation, a comprehensive dataset consisting of RGB and NIR video recordings from 29 neonates with various skin tones recorded in two NICUs in Germany and India was used. From all recordings, 643 frames were manually segmented. After pre-training the model on the public adult data, parts of the neonatal data were used for additional learning and left-out neonates are used for cross-validated evaluation. On the RGB data, the head is segmented well (82% intersection over union, 88% accuracy), and performance is comparable with those achieved on large, public, non-neonatal datasets. On the other hand, performance on the NIR data was inferior. By employing data augmentation to generate additional virtual NIR data for training, results could be improved and the head could be segmented with 62% intersection over union and 65% accuracy. The method is in theory capable of performing segmentation in real time and thus it may provide a useful tool for future PPGI applications. Graphical Abstract This work presents the development of a customized, real-time capable Deep Learning architecture for segmenting of neonatal videos recorded in the intensive care unit. In addition to hand-annotated data, transfer learning is exploited to improve performance.

Plasticity, ligand conformation and enzyme action of Mycobacterium smegmatis MutT1

Mycobacterium smegmatis MutT1 (MsMutT1) is a sanitation enzyme made up of an N-terminal Nudix hydrolase domain and a C-terminal domain resembling a histidine phosphatase. It has been established that the action of MutT1 on 8-oxo-dGTP, 8-oxo-GTP and diadenosine polyphosphates is modulated by intermolecular interactions. In order to further explore this and to elucidate the structural basis of its differential action on 8-oxo-NTPs and unsubstituted NTPs, the crystal structures of complexes of MsMutT1 with 8-oxo-dGTP, GMPPNP and GMPPCP have been determined. Replacement soaking was used in order to ensure that the complexes were isomorphous to one another. Analysis of the structural data led to the elucidation of a relationship between the arrangements of molecules observed in the crystals, molecular plasticity and the action of the enzyme on nucleotides. The dominant mode of arrangement involving a head-to-tail sequence predominantly leads to the generation of NDPs. The other mode of packing arrangement appears to preferentially generate NMPs. This work also provides interesting insights into the dependence of enzyme action on the conformation of the ligand. The possibility of modulating the enzyme action through differences in intermolecular interactions and ligand conformations makes MsMutT1 a versatile enzyme.

Effect of sertraline in patients with newly diagnosed depression and type 2 diabetes mellitus or hypertension: An observational study from south India

BACKGROUND AND AIMS

Patients with either type II diabetes mellitus (T2DM) or hypertension (HTN) are more prone to develop depression when compared with the general population. Along with comorbid of either T2DM or HTN, treatment of depression may become an even bigger challenge. The present study aimed to observe the effect of sertraline in newly diagnosed depression patients with T2DM or HTN.

METHODS

A prospective, observational study was conducted in the T2DM or HTN patients who were newly diagnosed as depression patients. Parameters such as, glycated hemoglobin (HbA_1c), systolic blood pressure (SBP), diastolic blood pressure (DBP) and Hamilton Depression Rating Scale (HAM-D) score for depression were measured before and after initiating sertraline therapy (50 mg, twice daily). Paired t-test was used to find out the statistical significance.

RESULT

Of the 546 enrolled patients, 291 patients were in T2DM and 255 patients with HTN. No statistically significant difference (p > 0.05) was observed between baseline and 12th week of comparison with respect to HbA_1c, SBP and DBP values. While at the end of treatment, the number of patients suffered from depression in severe state were improved to moderate to a mild state of depression. Statistically significant difference (p < 0.001) was observed in both T2DM and HTN groups when comparing the total score of HAM-D before and after sertraline treatment.

CONCLUSION

Sertraline is effective in the management of newly diagnosed depression among T2DM or HTN patients. However, the results should be confirmed by a double blinded placebo studies.

Non-contact sensing of neonatal pulse rate using camera-based imaging: a clinical feasibility study

OBJECTIVE

Neonates and infants are patients who would benefit from less invasive vital sign sensing, especially from fewer cables and the avoidance of adhesive electrodes. Photoplethysmography imaging (PPGI) has been studied for medical applications in recent years: it is possible to assess various vital signs remotely, non-invasively, and without contact by using video cameras and light. However, studies on infants and especially on neonates in clinical settings are still rare. Hence, we conducted a single-center study to assess heart activity by estimating the pulse rate (PR) of 19 neonates.

APPROACH

Time series were generated from tracked regions of interest (ROIs) and PR was estimated via a joint time-frequency analysis using a short-time Fourier transform. Artifacts, for example, induced by movement, were detected and flagged by applying a signal quality index in the frequency domain.

MAIN RESULTS

The feasibility of PR estimation was demonstrated using visible light and near-infrared light at 850 nm and 940 nm, respectively: the estimated PR was as close as 3 heartbeats per minute in artifact-free time segments. Furthermore, an improvement could be shown when selecting the best performing ROI compared to the ROI containing the whole body. The main challenges are artifacts from motion, light sources, medical devices, and the detection and tracking of suitable regions for signal retrieval. Nonetheless, the PR extracted was found to be comparable to the contact-based photoplethysmography reference and is, therefore, a viable replacement if robust signal retrieval is ensured.

SIGNIFICANCE

Neonates are seldom measured by PPGI and studies reporting measurements on darker skin tones are rare. In this work, not only a single camera was used, but a synchronized camera setup using multiple wavelengths. Various ROIs were used for signal extraction to examine the capabilities of PPGI. In addition, qualitative observations regarding camera parameters and noise sources were reported and discussed.

A sensitive refining of in vitro and in vivo toxicological behavior of green synthesized ZnO nanoparticles from the shells of Jatropha curcas for multifunctional biomaterials development

ZnO nanoparticles (NPs) possess a wide range of biological functions in pharmaceutical and cosmetic applications due to their excellent antimicrobial, optical and UV protective properties. This study first reports the toxicological assessment of ZnO NPs green synthesized from Jatropha curcas shells for multifunctional biomedical applications. The hot water extract of J.curcas shells is utilized as a chelating agent for the reduction of zinc acetate and then, the prepared ZnO NPs are broadly characterized using X-ray spectroscopic and electron microscopic observations. The prepared ZnO NPs acquire high purity (100%) wurtzite crystal with hexagonal structure with the average particle size of 53 nm. In vitro and in vivo toxicity evaluation against human tumor cell lines and zebrafish embryos have ascertained the purpose of ZnO NPs in clinical research. Toxic effects of ZnO NPs were observed by a dose-dependent reduction of bacterial growth at ≥1   μg ml^-1, by teratogenicity and genotoxicity in zebrafish embryos (from 3 to 90 μg ml^-1) and by a significant nanoparticle uptake (0.5 ng μl^-1) by a fish serum. In contrast, ZnO NPs fail to reduce the proliferation of human bladder tumor cells (UC6) and cell viability of A549 cells in vitro up to 500 μg ml^-1. All these observations limit the unobstructed application of ZnO NPs at higher concentrations. Thus, abundantly used metal oxide nanoparticles like ZnO NPs examined in our present study in different animal models under in vitro and in vivo conditions will be the significant screening strategy to determine the nanotoxicity.

Bullous pemphigoid associated with dipeptidyl peptidase-4 inhibitor - A case report

Dipeptidyl peptidase-4 inhibitors (DPP-4i) are one of the mainstay drugs in the management of type 2 diabetes mellitus. It has been well-documented that these class of drugs cause allergic reactions. Bullous pemphigoid (BP) is a blistering skin condition commonly associated with many drugs. Here, we report a case of probable DPP-4i-induced BP in an elderly man, which resolved on discontinuation of the drug. Although this adverse drug reaction has been documented in Western world and Japanese ethnicity, this seems to be the first case report of such occurrence in Indian ethnicity.

Radiological-pathological correlation of the British Thyroid Association ultrasound classification of thyroid nodules: a real-world validation study

AIM

To evaluate the real-world performance of the British Thyroid Association (BTA) U classification, specifically focusing on radiology-pathology correlation and to glean learning points.

METHODS AND MATERIALS

Adults undergoing a neck ultrasound for thyroid nodules were reviewed over a period of 1-year. Data including demographics, nodule characteristics, BTA grading, and cytology/histopathology were retrieved with a minimum 24-month follow-up.

RESULTS

Of 1,225 graded nodules in 964 patients, cytology and/or histology were available for 300 (24%). 57 cancers were detected. Of 24 (2%) U5 nodules, 14 were malignant, of 51 (4%) U4, 22 were malignant, of 256 (21%) U3, 20 were malignant, and from 894 (73%) U2 nodules, one cancer was discovered. BTA U grading with fine-needle aspiration (FNA)/core biopsy achieved 96.5% sensitivity, 93.7% specificity, and 93.9% accuracy compared to excision. There was no association between nodule size and rate of malignancy.

CONCLUSION

This is the first study to validate the use of the BTA U-grading system in UK clinical practice. The BTA U-grading system is a robust and reliable method of evaluating the risk of malignancy in thyroid nodules with a high negative predictive value. Key learning points gleaned from the study were accurate assessment of nodule echogenicity, careful evaluation of solid-cystic nodules, optimising ultrasound technique, and the low-risk nature of U3 nodules.

Crystal structures of disordered Z-type helices

The X-ray crystal structures of a decamer sequence d(CGCGTACGCG)₂ and a tetradecamer sequence d(CGCGCGTACGCGCG)₂ are presented here. Both sequences are alternating pyrimidine-purine repeat sequences and they form disordered, pseudo-continuous left handed Z-type helices. They demonstrate interesting variants of the 'bundles of columns of helices' mode of packing.

A facile method for high level dual expression of recombinant and congener protein in a single expression system

Protein engineering is an emerging field for developing novel therapeutic proteins and commercial enzymes, along with a major impact on the global market. In recent decades, advanced methods employing protein modification through expansion of the genetic code have led to the development of proteins with new biochemical and physical properties. These techniques have produced engineered proteins with improved attribute comprising substrate relaxation, protein drug conjugation and high stability under extreme conditions of high temperatures, pH and organic solvents. Furthermore, residue specific incorporation is the simplest method for the global incorporation of non-canonical amino acid (NCAA) for protein modification; however it has the major drawbacks of high production cost and manpower requirement. In the present study, we developed a method for the incorporation of single NCAA in two different proteins by using Escherichia coli (E. coli) expression system. For that, the dual protein expressing Escherichia coli JW2581 strain was constructed by transforming pQE80L and pD881-PpiBT vectors with different promoters, selectable markers and AnnexinV, GFPHS gene. To modify the protein, the 3,4 dihydroxy phenyl alanine (DOPA) was globally incorporated into the GFPHS and Annexin V protein using dual protein expression system. The incorporation efficiency during the dual protein expression was achieved through optimized concentrations of amino acids, carbohydrate and inducers in minimal medium. This method for the incorporation of single NCAA into two different proteins using a single expression host system saves the production cost, manpower and time substantially.

Measurement of carotid blood pressure and local pulse wave velocity changes during cuff induced hyperemia

We present a prototype design of dual element photoplethysmograph (PPG) probe along with associated measurement system for carotid local pulse wave velocity (PWV) evaluation in a non-invasive and continuous manner. The PPG probe consists of two identical sensing modules placed 23 mm apart. Simultaneously measured blood pulse waveforms from these arterial sites were processed and the pulse transit time delay was resolved using the developed application-specific software. The ability of developed PPG probe and associated measurement system to detect acute changes in carotid local PWV due to blood pressure (BP) variations was experimentally validated by an in-vivo study. Intra-subject carotid BP elevation was achieved by an upper arm cuff based occlusion, which offered a controlled way of local PWV escalation. The elevated carotid BP values were also recorded by a calibrated pressure tonometer prior to the study, and was used as a reference. A significant increment (1.0 - 2.6 m/s) in local PWV was observed and was proportional to the BP increment induced by the occlusive reactive hyperemia. Study results demonstrated the feasibility of real-time signal acquisition and reliable local PWV evaluation under normal and elevated BP conditions using the developed measurement system.

Comparison of X-ray crystal structures of a tetradecamer sequence d(CCCGGGTACCCGGG)2 at 1.7 Å resolution

We present here a comparison of three different X-ray crystal structures of DNA tetradecamer sequence d(CCCGGGTACCCGGG)₂ all at about 1.7 Å resolution. The sequence was designed as an attempt to form a DNA four-way junction with A-type helical arms. However, in the presence of zinc, magnesium, and in the absence of any metal ion, it does not take up the junction structure, but forms an A-type double helix. This allowed us to study possible conformational changes in the double helix due to the presence of metal ions. Upon addition of the zinc ion, there is a change in the space group from P4₁2₁2 to P4₁. The overall conformation of the duplex remains the same. There are small changes in the interaction of the metal ions with the DNA. In the zinc-bound structure, there are two zinc ions that show direct interaction with the N7 atoms of terminal G₁₃ bases at either end of the molecule. There are small changes in the interhelical contacts. The consequence of these differences is to break some of the symmetry and change the space group.

Alcohol-related cue-reactivity predicts abstinence duration in individuals with severe alcohol-use disorders

Introduction

Alcohol use disorder (AUD) is an important global public health problem with complex aetiology and relapsing remitting course. Clinical measures of alcohol dependence severity and alcohol-craving, are largely unreliable in identifying individuals at high-risk for relapse. Functional human neuroimaging methods that employ symptom provocation paradigms have shown promise in identifying critical brain regions with cue-elicited alcohol-craving response.

Objective

The present study aimed at examining the utility of fMRI cue-reactivity (CR) in predicting relapse risk.

Methods

The study was conducted on inpatients of a tertiary care neuropsychiatric hospital. Thirty-two treatment-seeking right-handed men were recruited for the study after informed consent. Following detoxification and 3-day drug-washout period, they underwent a task-based fMRI while viewing images of alcohol-related and control cues presented to them using a previously validated fMRI paradigm. All patients received anti-craving medications (baclofen: 60–80 mg/d, n = 16; naltrexone: 50–100 mg/d, n = 16) and were prospectively followed-up till their first alcohol lapse.

Results

Random-effect analysis using one-sample test revealed significant CR to alcohol-related cues (relative to implicit baseline) with activation in salience-reward related regions [insula, cingulate, dorsal striatum (DS)], visual-attention regions [occipito-temporal] and deactivation of default-mode regions [posterior cingulate (PCC)] (all significant at PFWE < 0.05, whole-brain corrected). Cox-proportional hazard regressions revealed that greater CR in Insula (Chi2 = 10.33; P = 0.001; HR = 3.1; 95% CI = 1.5–6.3) and DS (Chi2 = 10.87; P = 0.001; HR = 2.8; 95% CI = 1.5–5.2) predicts faster subsequent time to first drink after accounting for the role of clinical measures.

Conclusion

These findings indicate that CR can serve as potential marker to identify individuals at high-risk for relapse. Further examination of intervention-related CR change may aid in personalizing treatment of AUD.

Disclosure of interest

The authors have not supplied their declaration of competing interest.

Coumarin polycaprolactone polymeric nanoparticles: light and tumor microenvironment activated cocktail drug delivery

Highly sensitive hypoxia (H₂O₂)-activated photoresponsive polymeric nanoparticles for cocktail delivery of anticancer drugs doxorubicin (Dox) and chlorambucil (Cbl) were developed.

Photocaging of Single and Dual (Similar or Different) Carboxylic and Amino Acids by Acetyl Carbazole and its Application as Dual Drug Delivery in Cancer Therapy

A new fluorescent photoremovable protecting group (FPRPG) based on acetylcarbazole framework has been explored for the first time release of single and dual (similar or different) substrates from single chromophore. Mechanistic studies of the photorelease process revealed that photorelease of two (similar or different) substrates from acetyl carbazole proceeds via a stepwise pathway. Further, we constructed photoresponsive dual drug delivery system (DDS) to release two different anticancer drugs (caffeic acid and chlorambucil, 1 equiv each). In vitro study reveals that our DDS exhibit excellent properties like biocompatibility, cellular uptake, and photoregulated dual drug release.