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The widespread administration of COVID-19 vaccines has prompted a need to understand their safety profile. This investigation focuses on the safety of inactivated and mRNA-based COVID-19 vaccines, particularly concerning potential cardiovascular and haematological adverse events. A retrospective cohort study was conducted for 1.3 million individuals residing in Abu Dhabi, United Arab Emirates, who received 1.8 million doses of the inactivated BBIBP CorV (by SinoPharm) and mRNA-based BNT162b2 (Pfizer-BioNTech) vaccines between June 1, 2021, and June 30, 2022. The study's primary outcome was to assess the occurrence of selected cardiovascular and haematological events leading to hospitalization or emergency room visits within 21 days post-vaccination. Results showed no significant increase in the incidence rates of these events compared to the subsequent 22 to 42 days following vaccination. Analysis revealed no elevated risk for adverse outcomes following first (IRR 1·03; 95% CI 0·82-1·31), second (IRR 0·92; 95% CI 0·72-1·16) and third (IRR 0·82; 95% CI 0·66-1·00) doses of either vaccine. This study found no substantial link between receiving either mRNA and inactivated COVID-19 vaccines and a higher likelihood of cardiovascular or haematological events within 21 days after vaccination., (© 2024. The Author(s).)

Energy efficiency is important for underwater sensor networks. Designing such networks is challenging due to underwater environmental traits that hinder network lifespan extension. Unlike terrestrial protocols, underwater settings require novel protocols due to slower signal propagation. To enhance energy efficiency in underwater sensor networks, ongoing research concentrates on developing innovative solutions. Thus, in this paper, an intelligent bio-inspired autonomous surveillance system using underwater sensor networks is proposed as an efficient method for data communication. The tunicate swarm algorithm is used for the election of the cluster heads by considering different parameters such as energy, distance, and density. Each layer has several clusters, each of which is led by a cluster head that continuously rotates in response to the fitness values of the SNs using the tunicate swarm algorithm. The performance of the proposed protocol is compared with existing methods such as EE-LHCR, EE-DBR, and DBR, and results show the network's lifespan is improved by the proposed work. Due to the effective fitness parameters during cluster head elections, our suggested protocol may more effectively achieve energy balance, resulting in a longer network lifespan., Competing Interests: The authors declare no conflict of interest.

Nanostructured Lipid Carriers (NLC) are nano-sized colloidal drug delivery system that contains a lipid mixture consisting of both solid and liquid lipids in their core. This Lipid-Based Nanosystem is introduced as a biocompatible, non-toxic, and safe nano-drug delivery system as compared to polymeric or metallic nanoparticles. Due to its safety, stability, and high drug loading capacity compared to other lipid-based nanocarriers, NLC gained the attention of researchers to formulate safe and effective drug carriers. The ability to increase drug solubility and permeability while encapsulating the drug in a lipidic shell makes them an ideal carrier for drug delivery through difficult-to-achieve routes. Surface modification of NLC and the use of various additives result in drug targeting and increased residence time. With such qualities, NLCs can be used to treat a variety of diseases such as cancer, infections, neurodegenerative diseases, hypertension, diabetes, and pain management. This review focuses on the recent developments being made to deliver the drugs and genes through different routes via these nanocarriers. Here, we also discuss about historical background, structure, types of NLC and commonly employed techniques for manufacturing lipid-based nanocarriers., Competing Interests: None., (©2023 The Authors.)

In today's real-world, estimation of the level of difficulty of the musical is part of very meaningful musical learning. A musical learner cannot learn without a defined precise estimation. This problem is not very basic but it is complicated up to some extent because of the subjectivity of the contents and the scarcity of the data. In this paper, a lightweight model that generates original music content using deep learning along with generating music based on a specific genre is proposed. The paper discusses a lightweight deep learning-based approach for jazz music generation in MIDI format. In this work, the genre of music chosen is Jazz, and the songs selected are classical numbers composed by various artists. All the songs are in MIDI format and there might be differences in the pace or tone of the music. It is prudential to make sure that the chosen datasets that do not have these kinds of differences and are similar to the final output as desired. A model is trained to take in a part of a music file as input and should produce its continuation. The result generated should be similar to the dataset given as the input. Moreover, the proposed model also generates music using a particular instrument., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2022 Prasant Singh Yadav et al.)

A timely replacement of the rather expensive indium-doped tin oxide with aluminum-doped zinc oxide is hindered by the poor uniformity of electronic properties when deposited by magnetron sputtering. Recent results demonstrated the ability to improve the uniformity and to decrease the resistivity of aluminum-doped zinc oxide thin films by decreasing the energy of the oxygen-negative ions assisting in thin film growth by using a tuning electrode. In this context, a comparative study was designed to elucidate if the same phenomenology holds for gallium-doped zinc oxide and indium-doped tin oxide as well. The metal oxide thin films have been deposited in the same setup for similar discharge parameters, and their properties were measured with high spatial resolution and correlated with the erosion track on the target's surface. Furthermore, the films were also subject to post annealing and degradation tests by wet etching. While the tuning electrode was able to reduce the self-bias for all three materials, only the doped zinc oxide films exhibited properties correlating with the erosion track.

In today's scenario, sepsis is impacting millions of patients in the intensive care unit due to the fact that the mortality rate is increased exponentially and has become a major challenge in the field of healthcare. Such peoples require determinant care which increases the cost of the treatment by using a large number of resources because of the nonavailability of the resources. The treatment of sepsis is available in the early state, but treatment is not started at the right time, and then it converts to the advanced level of sepsis and increases the fatalities. Thus, an intensive analysis is required to detect and identify sepsis at the early stage. There are some models available that work based on the manual score and based on only the biomark features, but these are not fully automated. Some machine learning-based models are also available, which can reduce the mortality rate, but accuracy is not up to date. This paper proposes a machine learning model for early detecting and predicting sepsis in intensive care unit patients. Various models, random forest (RF), linear regression (LR), support vector machine (SVM), naive Bayes (NB), ensemble (of SVM, RF, NB, and LR), XGBoost, and proposed ensemble (of SVM, RF, NB, LR, and XGBoost), are simulated by using the collected data from intensive care unit patient's database that is based on the clinical laboratory values and vital signs. The performance of the models is evaluated by considering the same datasets. The balanced accuracy of RF, LR, SVM, NB, ensemble (of SVM, RF, NB, and LR), XGBoost, and proposed ensemble (of SVM, RF, NB, LR, and XGBoost) is 0.90, 0.73, 0.93, 0.74, 0.94, 0.95, and 0.96, respectively. It is also evident from the experimental results that the proposed ensemble model performs well as compared to the other models., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2022 Yash Veer Singh et al.)

In today's environment, electronics technology is growing rapidly because of the availability of the numerous and latest devices which can be deployed for monitoring and controlling the various healthcare systems. Due to the limitations of such devices, there is a dire need to optimize the utilization of the devices. In healthcare systems, Internet of things (IoT) based biosensors networking has minimal energy during transmission and collecting data. This paper proposes an optimized artificial intelligence system using IoT biosensors networking for healthcare problems for efficient data collection from the deployed sensor nodes. Here, an optimized tunicate swarm algorithm is used for optimizing the route for data collection and transmission among the patient and doctor. The fitness function of the optimized tunicate swarm algorithm used the distance, proximity, residual, and average energy of nodes parameters. The proposed method is attributed to the optimal CH chosen under TSA operation having a lower energy consumption. The performance of the proposed method is compared to the existing methods in terms of various metrics like stability period, lifetime, throughput, and clusters per round., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2022 Shadab Khan et al.)

Population averaged diffusion atlases can be utilized to characterize complex microstructural changes with less bias than data from individual subjects. In this study, a fetal diffusion tensor imaging (DTI) atlas was used to investigate tract-based changes in anisotropy and diffusivity in vivo from 23 to 38 weeks of gestational age (GA). Healthy pregnant volunteers with typically developing fetuses were imaged at 3 T. Acquisition included structural images processed with a super-resolution algorithm and DTI images processed with a motion-tracked slice-to-volume registration algorithm. The DTI from individual subjects were used to generate 16 templates, each specific to a week of GA; this was accomplished by means of a tensor-to-tensor diffeomorphic deformable registration method integrated with kernel regression in age. Deterministic tractography was performed to outline the forceps major, forceps minor, bilateral corticospinal tracts (CST), bilateral inferior fronto-occipital fasciculus (IFOF), bilateral inferior longitudinal fasciculus (ILF), and bilateral uncinate fasciculus (UF). The mean fractional anisotropy (FA) and mean diffusivity (MD) was recorded for all tracts. For a subset of tracts (forceps major, CST, and IFOF) we manually divided the tractograms into anatomy conforming segments to evaluate within-tract changes. We found tract-specific, nonlinear, age related changes in FA and MD. Early in gestation, these trends appear to be dominated by cytoarchitectonic changes in the transient white matter fetal zones while later in gestation, trends conforming to the progression of myelination were observed. We also observed significant (local) heterogeneity in within-tract developmental trajectories for the CST, IFOF, and forceps major., (© 2021 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

Diffusion-weighted magnetic resonance imaging (DW-MRI) of fetal brain is challenged by frequent fetal motion and signal to noise ratio that is much lower than non-fetal imaging. As a result, accurate and robust parameter estimation in fetal DW-MRI remains an open problem. Recently, deep learning techniques have been successfully used for DW-MRI parameter estimation in non-fetal subjects. However, none of those prior works has addressed the fetal brain because obtaining reliable fetal training data is challenging. To address this problem, in this work we propose a novel methodology that utilizes fetal scans as well as scans from prematurely-born infants. High-quality newborn scans are used to estimate accurate maps of the parameter of interest. These parameter maps are then used to generate DW-MRI data that match the measurement scheme and noise distribution that are characteristic of fetal data. In order to demonstrate the effectiveness and reliability of the proposed data generation pipeline, we used the generated data to train a convolutional neural network (CNN) to estimate color fractional anisotropy (CFA). We evaluated the trained CNN on independent sets of fetal data in terms of reconstruction accuracy, precision, and expert assessment of reconstruction quality. Results showed significantly lower reconstruction error (n=100,p<0.001) and higher reconstruction precision (n=20,p<0.001) for the proposed machine learning pipeline compared with standard estimation methods. Expert assessments on 20 fetal test scans showed significantly better overall reconstruction quality (p<0.001) and more accurate reconstruction of 11 regions of interest (p<0.001) with the proposed method., (Copyright © 2021. Published by Elsevier Inc.)

Accurate modeling of diffusion-weighted magnetic resonance imaging measurements is necessary for accurate brain connectivity analysis. Existing methods for estimating the number and orientations of fascicles in an imaging voxel either depend on non-convex optimization techniques that are sensitive to initialization and measurement noise, or are prone to predicting spurious fascicles. In this paper, we propose a machine learning-based technique that can accurately estimate the number and orientations of fascicles in a voxel. Our method can be trained with either simulated or real diffusion-weighted imaging data. Our method estimates the angle to the closest fascicle for each direction in a set of discrete directions uniformly spread on the unit sphere. This information is then processed to extract the number and orientations of fascicles in a voxel. On realistic simulated phantom data with known ground truth, our method predicts the number and orientations of crossing fascicles more accurately than several classical and machine learning methods. It also leads to more accurate tractography. On real data, our method is better than or compares favorably with other methods in terms of robustness to measurement down-sampling and also in terms of expert quality assessment of tractography results., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier B.V. All rights reserved.)

The present study evaluates the water quality status of 6-km-long Kali River stretch that passes through the Aligarh district in Uttar Pradesh, India, by utilizing high-resolution IRS P6 LISS IV imagery. In situ river water samples collected at 40 random locations were analyzed for seven physicochemical and four heavy metal concentrations, and the water quality index (WQI) was computed for each sampling location. A set of 11 spectral reflectance band combinations were formulated to identify the most significant band combination that is related to the observed WQI at each sampling location. Three approaches, namely multiple linear regression (MLR), backpropagation neural network (BPNN) and gene expression programming (GEP), were employed to relate WQI as a function of most significant band combination. Comparative assessment among the three utilized approaches was performed via quantitative indicators such as R 2 , RMSE and MAE. Results revealed that WQI estimates ranged between 203.7 and 262.33 and rated as " very poor " status. Results further indicated that GEP performed better than BPNN and MLR approaches and predicted WQI estimates with high R 2 values (i.e., 0.94 for calibration and 0.91 for validation data), low RMSE and MAE values (i.e., 2.49 and 2.16 for calibration and 4.45 and 3.53 for validation data). Moreover, both GEP and BPNN depicted superiority over MLR approach that yielded WQI with R 2  ~ 0.81 and 0.67 for calibration and validation data, respectively. WQI maps generated from the three approaches corroborate the existing pollution levels along the river stretch. In order to examine the significant differences among WQI estimates from the three approaches, one-way ANOVA test was performed, and the results in terms of F -statistic ( F  = 0.01) and p -value ( p  = 0.994 > 0.05) revealed WQI estimates as " not significant ," reasoned to the small water sample size (i.e., N  = 40). The study therefore recommends GEP as more rational and a better alternative for precise water quality monitoring of surface water bodies by producing simplified mathematical expressions., (© The Author(s), under exclusive licence to Springer Nature B.V. 2021.)

Competing Interests: Conflict of Interest Disclosures not related to this work: Ron C. Gaba receives research support from Guerbet USA LLC, Janssen Research & Development LLC, the United States Department of Defense, and the United States National Institutes of Health. He serves as a consultant for Sus Clinicals, Inc.

The third trimester of pregnancy is a period of rapid development of fiber bundles in the fetal white matter. Using a recently developed motion-tracked slice-to-volume registration (MT-SVR) method, we aimed to quantify tract-specific developmental changes in apparent diffusion coefficient (ADC), fractional anisotropy (FA), and volume in third trimester healthy fetuses. To this end, we reconstructed diffusion tensor images from motion corrected fetal diffusion magnetic resonance imaging data. With an approved protocol, fetal MRI exams were performed on healthy pregnant women at 3 Tesla and included multiple (2-8) diffusion scans of the fetal head (1-2 b = 0 s/mm 2 images and 12 diffusion-sensitized images at b = 500 s/mm 2 ). Diffusion data from 32 fetuses (13 females) with median gestational age (GA) of 33 weeks 4 days were processed with MT-SVR and deterministic tractography seeded by regions of interest corresponding to 12 major fiber tracts. Multivariable regression analysis was used to evaluate the association of GA with volume, FA, and ADC for each tract. For all tracts, the volume and FA increased, and the ADC decreased with GA. Associations reached statistical significance for: FA and ADC of the forceps major; volume and ADC for the forceps minor; FA, ADC, and volume for the cingulum; ADC, FA, and volume for the uncinate fasciculi; ADC of the inferior fronto-occipital fasciculi, ADC of the inferior longitudinal fasciculi; and FA and ADC for the corticospinal tracts. These quantitative results demonstrate the complex pattern and rates of tract-specific, GA-related microstructural changes of the developing white matter in human fetal brain., (© 2020 The Authors. Human Brain Mapping published by Wiley Periodicals, Inc.)

Background: Naphthalene is widely used in households as an insect repellent, but its poisoning is rare, especially in adults. Naphthalene is a strong oxidant with a pungent smell., Case Report: We report a case of a 16-year-old female who ingested three naphthalene mothballs 3 days prior to admission and presented with history of recurrent vomiting, severe pallor, jaundice, and hemoglobinuria. Investigation found severe hemolytic anemia, indirect hyperbilirubinemia, acute kidney injury, and evidence of intravascular hemolysis. Her urine output was normal throughout the course of illness. She was managed conservatively with i.v. fluid, oral ascorbic acid, and blood transfusion. With treatment our patient improved completely and was discharged in hemodynamically stable condition. She is doing fine after further follow-up. WHY SHOULD AN EMERGENCY PHYSICIAN BE AWARE OF THIS?: Emergency physician should keep the possibility of poisoning by an oxidizing agent, such as naphthalene, when a patient presents to the emergency department with rapid onset pallor, jaundice, and hemoglobinuria., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Aim: To do a randomized prospective interventional study for comparing the effects of a single subconjunctival triamcinolone acetonide (SCTA) injection to tapering topical loteprednol in patients undergoing phacoemulsification surgery under topical anesthesia., Methods: A total of 400 patients were randomized into 2 groups; Group A (200 patients) received 5 mg SCTA at the end of surgery and topical ketorolac tromethamine (0.5%) with ofloxacin (0.3%) combination for 3wk. Group B (200 patients) received tapering topical loteprednol etabonate (0.5%) along with ofloxacin (0.3%) and ketorolac tromethamine (0.5%) for 3wk. Outcomes evaluated were intraocular pressure (IOP), anterior chamber cells/flare and macular oedema postoperatively at 1, 6 and 12wk., Results: Baseline parameters were almost similar in both the groups. No statistical difference was seen between the preoperative and postoperative IOP values for Group A ( P =0.82) and Group B ( P =0.61) and postoperative IOP values in between both groups ( P =0.14) at 1wk. Incidence of cells/flare postoperative was statistically not significant ( P =0.82) in both groups at all follow up visits. Postoperative macular oedema was not observed at any follow up visit., Conclusion: SCTA appears to be an effective alternative to prolong postoperative topical steroid use.

Purpose: To achieve motion-robust diffusion compartment imaging (DCI) in near continuously moving subjects based on simultaneous multi-slice, diffusion-weighted brain MRI., Methods: Simultaneous multi-slice (SMS) acquisition enables fast and dense sampling of k- and q-space. We propose to achieve motion-robust DCI via slice-level motion correction by exploiting the rigid coupling between simultaneously acquired slices. This coupling provides 3D coverage of the anatomy that substantially constraints the slice-to-volume alignment problem. This is incorporated into an explicit model of motion dynamics that handles continuous and large subject motion in robust DCI reconstruction., Results: We applied the proposed technique, called Motion Tracking based on Simultanous Multislice Registration (MT-SMR) to multi b-value SMS diffusion-weighted brain MRI of healthy volunteers and motion-corrupted scans of 20 pediatric subjects. Quantitative and qualitative evaluation based on fractional anisotropy in unidirectional fiber regions, and DCI in crossing-fiber regions show robust reconstruction in the presence of motion., Conclusion: The proposed approach has the potential to extend routine use of SMS DCI in very challenging populations, such as young children, newborns, and non-cooperative patients., (© 2018 International Society for Magnetic Resonance in Medicine.)

With an aim to increase the capture range and accelerate the performance of state-of-the-art inter-subject and subject-to-template 3-D rigid registration, we propose deep learning-based methods that are trained to find the 3-D position of arbitrarily-oriented subjects or anatomy in a canonical space based on slices or volumes of medical images. For this, we propose regression convolutional neural networks (CNNs) that learn to predict the angle-axis representation of 3-D rotations and translations using image features. We use and compare mean square error and geodesic loss to train regression CNNs for 3-D pose estimation used in two different scenarios: slice-to-volume registration and volume-to-volume registration. As an exemplary application, we applied the proposed methods to register arbitrarily oriented reconstructed images of fetuses scanned in-utero at a wide gestational age range to a standard atlas space. Our results show that in such registration applications that are amendable to learning, the proposed deep learning methods with geodesic loss minimization achieved 3-D pose estimation with a wide capture range in real-time (<100ms). We also tested the generalization capability of the trained CNNs on an expanded age range and on images of newborn subjects with similar and different MR image contrasts. We trained our models on T2-weighted fetal brain MRI scans and used them to predict the 3-D pose of newborn brains based on T1-weighted MRI scans. We showed that the trained models generalized well for the new domain when we performed image contrast transfer through a conditional generative adversarial network. This indicates that the domain of application of the trained deep regression CNNs can be further expanded to image modalities and contrasts other than those used in training. A combination of our proposed methods with accelerated optimization-based registration algorithms can dramatically enhance the performance of automatic imaging devices and image processing methods of the future.

Altered structural fetal brain development has been linked to neuro-developmental disorders. These structural alterations can be potentially detected in utero using diffusion tensor imaging (DTI). However, acquisition and reconstruction of in utero fetal brain DTI remains challenging. Until now, motion-robust DTI methods have been employed for reconstruction of in utero fetal DTIs. However, due to the unconstrained fetal motion and permissible in utero acquisition times, these methods yielded limited success and have typically resulted in noisy DTIs. Consequently, atlases and methods that could enable groupwise studies, multi-modality imaging, and computer-aided diagnosis from in utero DTIs have not yet been developed. This paper presents the first DTI atlas of the fetal brain computed from in utero diffusion-weighted images. For this purpose an algorithm for computing an unbiased spatiotemporal DTI atlas, which integrates kernel-regression in age with a diffeomorphic tensor-to-tensor registration of motion-corrected and reconstructed individual fetal brain DTIs, was developed. Our new algorithm was applied to a set of 67 fetal DTI scans acquired from healthy fetuses each scanned at a gestational age between 21 and 39 weeks. The neurodevelopmental trends in the fetal brain, characterized by the atlas, were qualitatively and quantitatively compared with the observations reported in prior ex vivo and in utero studies, and with results from imaging gestational-age equivalent preterm infants. Our major findings revealed early presence of limbic fiber bundles, followed by the appearance and maturation of projection pathways (characterized by an age related increase in FA) during late 2nd and early 3rd trimesters. During the 3rd trimester association fiber bundles become evident. In parallel with the appearance and maturation of fiber bundles, from 21 to 39 gestational weeks gradual disappearance of the radial coherence of the telencephalic wall was qualitatively identified. These results and analyses show that our DTI atlas of the fetal brain is useful for reliable detection of major neuronal fiber bundle pathways and for characterization of the fetal brain reorganization that occurs in utero. The atlas can also serve as a useful resource for detection of normal and abnormal fetal brain development in utero., (Copyright © 2018 Elsevier Inc. All rights reserved.)

Purpose: To generate virtual models using different hinge locations and to investigate the advantages of a temporal hinge to a superior in LASIK., Methods: The study design was cross sectional. An image processing software was used to construct virtual LASIK models of 56 slit lamp photos of eyes. For a given hinge location and width, the largest flap diameter and maximum treatable stromal bed area were calculated, comparing temporal hinge to superior., Results: Temporal hinge allowed a greater flap diameter than superior and thus a greater treatment zone area for a given hinge width. A temporal hinge allowed a greater hinge width than a superior hinge for a given treatment zone area., Conclusion: We provide a rational basis for a mechanically stable temporal hinge. Moreover, we provide a method to pre-operative model flap size and hinge location, using a slit lamp camera and an image editing software.

Diffusion tensor imaging (DTI) based group analysis has helped uncover the impact of white matter injuries in a wide range of studies involving subjects from preterm neonates to adults. The application of these methods to fetal cohorts, however, has been hampered by the challenging nature of in utero fetal DTI caused by unconstrained fetal motion, limited scan times, and limited signal-to-noise ratio. We present a framework that addresses these issues to systematically evaluate group differences in fetal cohorts. A motion-robust DTI computation approach with a new unbiased DTI template construction method is unified with kernel-regression in age and tensor-specific registration to normalize DTI volumes in an unbiased space. A robust statistical approach is used to map region-specific group differences to the medial representation of the tracts of interest. The proposed approach was applied and showed, for the first time, differences in local white matter fractional anisotropy based on in utero DTI of fetuses with congenital heart disease and age-matched healthy controls. This paper suggests the need for fetal-specific pipelines to be used for DTI-based group analysis involving fetal cohorts.

Objectives: This study aimed to determine the change in anatomical location of appendix in full-term pregnancy., Study Design: This was a descriptive cross-sectional study., Place and Duration of Study: Liaquat National University Hospital, Karachi, Pakistan, Department of General Surgery, January 01 to July 31, 2010., Patients and Methods: Full-term pregnant women undergoing caesarean section were enrolled. The anatomical position of the appendix was noted by visual inspection with reference to the transtubercular plane (TTP). SPSS-10 was used for analysis., Results: Seventy-seven full-term pregnant female patients who underwent caesarean section were included in the study. Their mean age was 29 years, the mean height was 5.3 feet, and mean gestational age was 38 weeks. Appendix was found at the normal anatomical location in 63 out of 77 patients (81.8%), while it was located above the TTP in 14 patients (18.2%)., Conclusion: Appendix does not migrate up with increasing gestational age in the majority of pregnant women. In most full-term pregnant female patients, appendix is located at the normal anatomical position., Competing Interests: Disclosure The authors report no conflicts of interest in this work.

Ankyloglossia or "tongue-tie" is a rare congenital anomaly characterized by an abnormally short, thick, fibrosed lingual frenulum which may cause restriction in function of tongue including limitation in tongue movement. The lingual frenulum may be fibrous or muscular, and the tie may be complete or partial. Most often, ankyloglossia is seen as an isolated condition in an otherwise normal individual. This article reports the surgical management of a 20-year-old patient having ankyloglossia associated with restricted movement of tongue and difficulty in speech. The treatment involved is surgical removal of the lingual frenum followed by tongue training exercise and speech therapy to functionally rehabilitate the tongue. Six months postoperatively, the patient showed uneventful healing and was satisfied with the procedure.

Objective: Currently no efficient and reliable technique exists to routinely assess surgical margins during a radical prostatectomy. Electrical impedance spectroscopy (EIS) has been reported as a potential technique to provide surgeons with real-time intraoperative margin assessment. In addition to providing a quantified measure of margin status, a co-registered electrical impedance tomography (EIT) image presented on a surgeon's workstation could add value to the margin assessment process., Approach: To investigate this, we conducted a comparative study between EIS and EIT to evaluate the potential these technologies might have for margin assessment. EIS and EIT data was acquired from ex vivo human prostates using a multi-electrode endoscopic impedance acquisition probe., Main Results: EIS and EIT show good predictive performance with a 0.76 and 0.80 area-under-curve (AUC), respectively, when considering discrete frequencies only. A machine learning (ML) algorithm is implemented to combine features, which improves the AUCs of EIS and EIT to 0.84 and 0.85, respectively. Single-step EIT takes significantly less time to reconstruct than multi-step EIT, yet provides similarly accurate classification results, making the single-step approach a potential candidate for real-time margin assessment. While the ML-based approach clearly exhibits benefits as compared to the single feature assessment, the decision to use EIS versus EIT is unclear since each approach performs better for different subsets of tissue classifications., Significance: The results presented in this paper corroborate our previous studies and present the strongest evidence yet that an intraoperative-capable impedance probe can be used to distinguish benign from malignant prostate tissues. An in vivo study with a large cohort will be necessary to definitively determine the preferred approach and to show the clinical effectiveness of using this technology for margin assessment.

Prostate cancer (PCa) recurrences are often predicted by assessing the status of surgical margins (SM)- positive surgical margins (PSM) increase the chances of biochemical recurrence by 2-4 times which may lead to PCa recurrence. To this end, an electrical impedance acquisition system with a microendoscopic probe was employed in an ex-vivo study of human prostates. This system measures the tissue bioimpedance over a range of frequencies (1 kHz to 1MHz), and computes a number of Composite Impedance Metrics (CIM). A classifier trained using CIM data can be used to classify tissue as benign or cancerous. The system was used to collect the impedance spectra from 14 excised prostates, which were obtained from men undergoing radical prostatectomy, for a total of 23 cancerous and 53 benign measurements. The data revealed statistically significant (p < 0.05) differences in the impedance properties of the benign and tumorous tissues, and among the measurements taken on the apical, base, and lateral surface of the prostate. Further, in the leave-one-patient-out cross validation, a maximum predictive accuracy of 90.79% was achieved by combining high frequency CIM phase data to train a support vector machine classifier with a radial basis function kernel. The observations are consistent with the physiology and morphology of benign and malignant prostate tissue. CIMs were found to be an effective tool in distinguishing benign from cancerous tissues.

Here we report a simple, rapid, environment friendly approach for the synthesis of gold nanoparticles using neem (Azadirachta indica A. Juss.) fungal endophyte, which based upon morphological and cultural characteristics was eventually identified as Fusarium oxysporum. The aqueous precursor (HAuCl4) solution when reacted with endophytic fungus resulted in the biosynthesis of abundant amounts of well dispersed gold nanoparticles of 10-40 nm with an average size of 22nm. These biosynthesized gold nanoparticles were then characterized by standard analytical techniques such as UV-Visible spectroscopy, X-ray diffraction, Transmission Electron Microscopy and Fourier Transform Infrared Spectroscopy. Cytotoxic activity of these nanoparticles was checked against three different cell types including breast cancer (ZR-75-1), Daudi (Human Burkitt's lymphoma cancer) and normal human peripheral blood mononuclear cells (PBMC), where it was found that our gold nanoparticles are anti-proliferative against cancer cells but completely safe toward normal cells. In addition to this, assessment of toxicity toward human RBC revealed less than 0.1 % hemolysis as compared to Triton X-100 suggesting safe nature of our biosynthesized gold nanoparticles on human cells. Also, our nanoparticles exhibited no anti-fungal (against Aspergillus niger) or anti-bacterial [against Gram positive (Bacillus subtilis & Staphylococcus aureus) and Gram negative (Escherichia coli & Pseudomonas aeruginosa) bacteria] activity thus suggesting their non-toxic, biocompatible nature. The present investigation opens up avenues for ecofriendly, biocompatible nanomaterials to be used in a wide variety of application such as drug delivery, therapeutics, theranostics and so on.

Radially configured microendoscopic electrical impedance probes intended for intraoperative surgical margin assessment during robot-assisted laparoscopic prostatectomy (RALP) were examined through simulation, bench-top experimentation, and ex vivo tissue studies. Three probe designs with 8, 9, and 17 electrodes, respectively, were analyzed through finite element method based simulations. One mm diameter spherical inclusions ( σinclusion = 1 S/m) are positioned at various locations within a hemispherical background ( σbackground = 0.1 S/m) of radius 5 mm. An 8-electrode configuration is not able to localize the inclusion at these positions while 9 and 17-electrode configurations are able to accurately reconstruct the inclusion at maximum depth of 1 mm and 3 mm, respectively. All three probe designs were constructed and evaluated using saline phantoms and ex vivo porcine and human prostate tissues. The 17-electrode probe performed best in saline phantom studies, accurately reconstructing high contrast, 1-mm-diameter metal cylindrical inclusions in a saline bath ( σsaline = 0.1 S/m) with a position and area error of 0.46 mm and 0.84 mm2, respectively. Additionally, the 17-electrode probe was able to adequately distinguish cancerous from benign tissues in three ex vivo human prostates. Simulations, bench-top saline experiments, and ex vivo tissue sampling suggest that for intraoperative surgical margin assessment during RALP, the 17-electrode probe (as compared to an 8 and 9 electrode probe) will be necessary to provide sufficient accuracy and sensitivity.

Electrical impedance tomography (EIT) is used to image the electrical property distribution of a tissue under test. An EIT system comprises complex hardware and software modules, which are typically designed for a specific application. Upgrading these modules is a time-consuming process, and requires rigorous testing to ensure proper functioning of new modules with the existing ones. To this end, we developed a modular and reconfigurable data acquisition (DAQ) system using National Instruments' (NI) hardware and software modules, which offer inherent compatibility over generations of hardware and software revisions. The system can be configured to use up to 32-channels. This EIT system can be used to interchangeably apply current or voltage signal, and measure the tissue response in a semi-parallel fashion. A novel signal averaging algorithm, and 512-point fast Fourier transform (FFT) computation block was implemented on the FPGA. FFT output bins were classified as signal or noise. Signal bins constitute a tissue's response to a pure or mixed tone signal. Signal bins' data can be used for traditional applications, as well as synchronous frequency-difference imaging. Noise bins were used to compute noise power on the FPGA. Noise power represents a metric of signal quality, and can be used to ensure proper tissue-electrode contact. Allocation of these computationally expensive tasks to the FPGA reduced the required bandwidth between PC, and the FPGA for high frame rate EIT. In 16-channel configuration, with a signal-averaging factor of 8, the DAQ frame rate at 100 kHz exceeded 110 frames s (-1), and signal-to-noise ratio exceeded 90 dB across the spectrum. Reciprocity error was found to be for frequencies up to 1 MHz. Static imaging experiments were performed on a high-conductivity inclusion placed in a saline filled tank; the inclusion was clearly localized in the reconstructions obtained for both absolute current and voltage mode data.

In this paper, we for the first time show the ability of the mesophilic fungus Fusarium oxysporum in the bioleaching of waste material such as Fly-ash for the extracellular production of highly crystalline and highly stable, protein capped, fluorescent and water soluble silica nanoparticles at ambient conditions. When the fungus Fusarium oxysporum is exposed to Fly-ash, it is capable of selectively leaching out silica nanoparticles of quasi-spherical morphology within 24 h of reaction. These silica nanoparticles have been completely characterized by UV-vis spectroscopy, Photoluminescence (PL), Transmission electron microscopy (TEM), X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR) and Energy dispersive analysis of X-rays (EDAX).

As a part of our programme to develop nanobioconjugates for the treatment of cancer, we first synthesized extracellular, protein-capped, highly stable and well-dispersed gadolinium oxide (Gd2O3) nanoparticles by using thermophilic fungus Humicola sp. The biodistribution of the nanoparticles in rats was checked by radiolabelling with Tc-99m. Finally, these nanoparticles were bioconjugated with the chemically modified anticancer drug taxol with the aim of characterizing the role of this bioconjugate in the treatment of cancer. The biosynthesized Gd2O3 nanoparticles were characterized by UV-vis spectroscopy, transmission electron microscopy (TEM), X-ray diffraction (XRD) and X-ray photoemission spectroscopy (XPS). The Gd2O3-taxol bioconjugate was confirmed by UV-vis spectroscopy and fluorescence microscopy and was purified by using high performance liquid chromatography (HPLC).

Purpose: Cervical spine injuries following major trauma result in significant associated morbidity and mortality. Devastating neurological injury, including complete and incomplete tetraplegia, are common sequelae of cervical spine trauma and cause profound and life-altering medical, financial, and social consequences. Most cervical spine injuries follow motor vehicle accidents, falls, and violence. The proliferation of multidetector computed tomography allows for fast and accurate screening for potential bony and vascular injuries. Magnetic resonance imaging is useful for evaluation of the supporting ligaments and the spinal cord after the patient has been stabilised., Conclusion: Cervical spine injuries are approached with much caution by emergency room clinicians. Thus, it is essential that radiologists be able to differentiate between a stable and unstable injury on MDCT, as this information ultimately helps determine the management of such injuries., Teaching Points: MDCT and MRI are complementary and both may be needed to define injuries and determine management. MDCT rapidly evaluates the bones, and MRI is superior for detecting ligament and cord injuries. Injury to one of the three spinal columns may be stable, and injuries to more than one are unstable. Instability may cause abnormal interspinous and interpedicular distances, or cervical malalignment. Fractures of the foramen transversarium are associated with vertebral arterial dissection.

The ductal epithelium plays a key role in physiological secretion of pancreatic enzymes into the digestive system. Loss of barrier properties of the pancreatic duct may contribute to the development of pancreatitis and metastatic dissemination of pancreatic tumors. Proinflammatory cytokines are essential mediators of pancreatic inflammation and tumor progression; however, their effects on the integrity and barrier properties of the ductal epithelium have not been previously addressed. In the present study, we investigate mechanisms of cytokine-induced disassembly of tight junctions (TJs) and adherens junctions (AJs) in a model pancreatic epithelium. Exposure of HPAF-II human pancreatic epithelial cell monolayers to interferon (IFN)γ disrupted integrity and function of apical junctions as manifested by increased epithelial permeability and cytosolic translocation of AJ and TJ proteins. Tumor necrosis factor (TNF)α potentiated the effects of IFNγ on pancreatic epithelial junctions. The cytokine-induced increase in epithelial permeability and AJ/TJ disassembly was attenuated by pharmacological inhibition of Janus kinase (JAK) and protein kinase D (PKD). Loss of apical junctions in IFNγ/TNFα-treated HPAF-II cells was accompanied by JAK and PKD dependent decrease in expression of AJ (E-cadherin, p120 catenin) and TJ (occludin, ZO-1) proteins. Depletion of E-cadherin or p120 catenin recapitulated the effects of cytokines on HPAF-II cell permeability and junctions. Our data suggests that proinflammatory cytokines disrupt pancreatic epithelial barrier via expressional downregulation of key structural components of AJs and TJs. This mechanism is likely to be important for pancreatic inflammatory injury and tumorigenesis.

Automatic Karyotyping is the process of classifying chromosomes from an unordered karyogram into their respective classes to create an ordered karyogram. Automatic karyotyping algorithms typically perform geometrical correction of deformed chromosomes for feature extraction; these features are used by classifier algorithms for classifying the chromosomes. Karyograms of bone marrow cells are known to have poor image quality. An example of such karyograms is the Lisbon-K(1) (LK(1)) dataset that is used in our work. Thus, to correct the geometrical deformation of chromosomes from LK(1), a robust method to obtain the medial axis of the chromosome was necessary. To address this problem, we developed an algorithm that uses the seed points to make a primary prediction. Subsequently, the algorithm computes the distance of boundary from the predicted point, and the gradients at algorithm-specified points on the boundary to compute two auxiliary predictions. Primary prediction is then corrected using auxiliary predictions, and a final prediction is obtained to be included in the seed region. A medial axis is obtained this way, which is further used for geometrical correction of the chromosomes. This algorithm was found capable of correcting geometrical deformations in even highly distorted chromosomes with forked ends.

A typical characteristic of images of bone marrow cells taken during mitosis is poor quality. This renders the task of extraction of accurate band profile, representative of intensity distribution over each chromosome, more challenging. A robust method is hence required to tackle this problem. An algorithm was thus developed, which estimates a single-line medial axis, the basis for computation of band profile. Medial axis was generated by computing a final prediction, using primary and secondary predictions obtained by a nonparametric machine learning algorithm trained with data from chromosome's skeleton, and geometrical properties of medial axis, respectively. Experiments were performed using the LK(1) dataset. The algorithm was found capable of estimating a satisfactory single-line medial axis. Band profile obtained was found to be a good representation of intensity levels in different regions of chromosomes. Additionally, this algorithm is robust in terms of growing a very small seed region into desired medial axis and also handling highly irregular chromosomes.

Setting: The study was carried out at Delhi State, New Delhi., Objectives: 1. To assess the effect of social stigma in TB patient's treatment, their personality, emotions, feelings, changes in their thinking process and behaviour of their family members, friends. 2. To study the relationship of gender and to what extent social stigma affects their lives., Design: It was a prospective study. A total of 1977 newly diagnosed and registered cases under Revised National TB Control Programme for treatment during the period of March 2009 to May 2009 were included in the study. Out of a total population of 170 lacs, a proportion of 31 lacs of Delhi, distributed among five chest clinics of Delhi, comprised the study population. All the patients were interviewed according to a pre-designed & pre-tested questionnaire after taking informed consent of the patients. The data was collected and analysed after processing into MS excel sheets for statistical analysis., Results: There was an immense stigma observed at society level with 60% of the patients hiding their disease (p<0.05) from friends and neighbours. Stigma was observed more among middle and upper middle class when compared to lower middle class and lower class (p<0.05). Gender-wise further it was observed that stigma was more among females (p<0.05) than in males., Conclusion: The study has demonstrated that despite good performance of Revised National TB Control Programme the stigma in tuberculosis still remains a problem and we need to supplement the efforts in advocacy, communication and social mobalization for reducing the stigma problem among TB patients in effective control of tuberculosis.

Brucella abortus, a facultative intracellular pathogen, is of tremendous zoonotic importance because of its ability to induce spontaneous abortion in cattle and other livestock. It is also known to cause persistent undulant fever, endocarditis, arthritis, osteomyelitis and meningitis in humans. The available vaccines against this dreadful infection suffer from limitations like short-term immunity, increased risk of hypersensitivity and low prophylactic index in the recipients. In the present study, we have demonstrated that liposomal form of a recombinant ribosomal L7/L12 protein, a B-T cell antigen of B. abortus, activates strong immune response in the host. In contrast, free antigen generates moderate immune response in the immunised animals. The liposomisation of rL7/L12 protein causes tremendous increase in cell-mediated immune response in terms of delayed type hypersensitivity, T-cell proliferation and up-regulation in type I cytokine expression, etc. Moreover, the liposome encapsulated antigen elicited stronger humoral immune response as compared to standard vaccine (S-19) or IFA-L7/L12 combination in the immunised animals. The effectiveness of liposome-based vaccine was also substantiated by better systemic clearance of bacterial load after challenging the animals with B. abortus 544 pathogen. The results of the present study suggest the potential of liposome-based rL7/L12 antigen as prospective and efficient candidate vaccine capable of eliciting both cell mediated as well as humoral immune responses against experimental murine brucellosis.