H-Deep-Net: A deep hybrid network with stationary wavelet packet transforms for Retinal detachment classification through fundus images

Nowadays, automated disease diagnosis has become a vital role in the medical field due to the significant population expansion. An automated disease diagnostic approach assists clinicians in the diagnosis of disease by giving exact, consistent, and prompt results, along with minimizing the mortality rate. Retinal detachment has recently emerged as one of the most severe and acute ocular illnesses, spreading worldwide. Therefore, an automated and quickest diagnostic model should be implemented to diagnose retinal detachment at an early stage. This paper introduces a new hybrid approach of best basis stationary wavelet packet transform and modified VGG19-Bidirectional long short-term memory to detect retinal detachment using retinal fundus images automatically. In this paper, the best basis stationary wavelet packet transform is utilized for image analysis, modified VGG19-Bidirectional long short-term memory is employed as the deep feature extractors, and then obtained features are classified through the Adaptive boosting technique. The experimental outcomes demonstrate that our proposed method obtained 99.67% sensitivity, 95.95% specificity, 98.21% accuracy, 97.43% precision, 98.54% F1-score, and 0.9985 AUC. The model obtained the intended results on the presently accessible database, which may be enhanced further when additional RD images become accessible. The proposed approach aids ophthalmologists in identifying and easily treating RD patients.

Conv-eRVFL: Convolutional Neural Network Based Ensemble RVFL Classifier for Alzheimer's Disease Diagnosis

As per the latest statistics, Alzheimer's disease (AD) has become a global burden over the following decades. Identifying AD at the intermediate stage became challenging, with mild cognitive impairment (MCI) utilizing credible biomarkers and robust learning approaches. Neuroimaging techniques like magnetic resonance imaging (MRI) and positron emission tomography (PET) are practical research approaches that provide structural atrophies and metabolic variations. With the help of MRI and PET scans, metabolic and structural changes in AD patients can be visible even ten years before the disease's onset. This paper proposes a novel wavelet packet transform-based structural and metabolic image fusion approach using MRI and PET scans. An eight-layer trained CNN extracts features from multiple layers and these features are fed to an ensemble of non-iterative random vector functional link (RVFL) models. The RVFL network incorporates the s-membership fuzzy function as an activation function that helps overcome outliers. Lastly, outputs of all the customized RVFL classifiers are averaged and fed to the RVFL classifier to make the final decision. Experiments are performed over Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, and classification is made over CN vs. AD vs. MCI. The model performance obtained is decent enough to prove the effectiveness of the fusion-based ensemble approach.

A multi-class deep learning model for early lung cancer and chronic kidney disease detection using computed tomography images

Lung cancer is a fatal disease caused by an abnormal proliferation of cells in the lungs. Similarly, chronic kidney disorders affect people worldwide and can lead to renal failure and impaired kidney function. Cyst development, kidney stones, and tumors are frequent diseases impairing kidney function. Since these conditions are generally asymptomatic, early, and accurate identification of lung cancer and renal conditions is necessary to prevent serious complications. Artificial Intelligence plays a vital role in the early detection of lethal diseases. In this paper, we proposed a modified Xception deep neural network-based computer-aided diagnosis model, consisting of transfer learning based image net weights of Xception model and a fine-tuned network for automatic lung and kidney computed tomography multi-class image classification. The proposed model obtained 99.39% accuracy, 99.33% precision, 98% recall, and 98.67% F1-score for lung cancer multi-class classification. Whereas, it attained 100% accuracy, F1 score, recall and precision for kidney disease multi-class classification. Also, the proposed modified Xception model outperformed the original Xception model and the existing methods. Hence, it can serve as a support tool to the radiologists and nephrologists for early detection of lung cancer and chronic kidney disease, respectively.

234U/238U disequilibrium and 235U/238U ratios measured using MC-ICP-MS in natural high background radiation area soils to understand the fate of uranium

The Chhatrapur-Gopalpur coastal area in Odisha, India is a well-known natural high background radiation (HBRA) area due to the abundance of monazite (a thorium bearing radioactive mineral) in beach sands and soils. Recent studies on Chhatrapur-Gopalpur HBRA groundwater have reported high concentrations of uranium and its decay products. Therefore, the soils of the Chhatrapur-Gopalpur HBRA are reasonably suspected as the sources of these high uranium concentrations in groundwater. In this report, first the uranium concentrations in soil samples were measured using inductively coupled plasma mass spectrometry (ICP-MS) and they were found to range from 0.61 ± 0.01 to 38.59 ± 0.16 mg kg^-１. Next, the ²³⁴U/²³⁸U and ²³⁵U/²³⁸U isotope ratios were measured to establish a baseline for the first time in Chhatrapur-Gopalpur HBRA soil. Multi-collector inductively coupled plasma mass spectrometry (MC-ICP-MS) was used for measurement of these isotope ratios. The ²³⁵U/²³⁸U ratio was observed to be the normal terrestrial value. The ²³⁴U/²³⁸U activity ratio, was calculated to understand the secular equilibrium between ²³⁴U and ²³⁸U in soil and it varied from 0.959 to 1.070. To understand the dynamics of uranium in HBRA soil, physico-chemical characteristics of soil were correlated with uranium isotope ratios and this correlation of ²³⁴U/²³⁸U activity ratio indicated the leaching of ²³⁴U from Odisha HBRA soil.

ACTIVITY RATIO OF URANIUM IN FUKUSHIMA SOIL SAMPLES USING MULTI-COLLECTOR INDUCTIVELY COUPLED PLASMA MASS SPECTROMETRY

The Fukushima soils have been collected from Namie and Futaba areas for the radiocaesium and uranium isotope ratio studies. The 137Cs activity concentration of soil samples ranged from 6 ± 1 to 756 ± 14 kBq/kg. The uranium isotope ratios are measured using multi collector inductively coupled plasma mass spectrometry. The activity ratio (234U/238U) of the Fukushima soils is calculated from the measured 234U/238U isotope ratio. Activity ratio varied from 0.98 to 1.02 which indicates that 234U and 238U are in secular equilibrium. The 235U/238U atomic ratio of the Fukushima soils did not show any heterogeneity compared with the natural terrestrial ratio even with high level of 137Cs in soils.

Ada-GridRF: A Fast and Automated Adaptive Boost Based Grid Search Optimized Random Forest Ensemble model for Lung Cancer Detection

Lung cancer is considered one of the leading causes of death all across the world. Various radiology-related fields increasingly have used Computer-aided diagnosis (CAD) systems. It just has already become a part of clinical work for lung cancer detection. In this article, we proposed an Adaptive Boost-based Grid Search Optimized Random Forest (Ada-GridRF) classifier that best optimized the hyperparameters of the base random forest model to identify the malignant and non-malignant nodules from the trained CT images. Improved performance speed and reduced computational complexity were the advantages of the proposed method. The proposed methodology was compared with other hyperparameter optimization techniques and also with different conventional approaches. It even outperformed the popular state-of-the-art deep learning techniques such as transfer learning and convolutional neural network. The experimental results proved that the proposed method yielded the best performance metrics of 97.97% accuracy, 100% sensitivity, 96% specificity, 96.08% precision, 98% F1-score, 4% False positives rate, and 99.8% Area under the ROC curve (AUC). It took only 8 msec to train the model. Thus, the proposed Ada-GridRF model can aid radiologists in fast lung cancer detection.

Lattice model on the rate of DNA hybridization

We develop a lattice model on the rate of hybridization of the complementary single-stranded DNAs (c-ssDNAs). Upon translational diffusion mediated collisions, c-ssDNAs interpenetrate each other to form correct (cc), incorrect (icc), and trap correct contacts (tcc) inside the reaction volume. Correct contacts are those with exact registry matches, which leads to nucleation and zipping. Incorrect contacts are the mismatch contacts which are less stable compared to tcc, which can occur in the repetitive c-ssDNAs. Although tcc possess registry match within the repeating sequences, they are incorrect contacts in the view of the whole c-ssDNAs. The nucleation rate (k_{N}) is directly proportional to the collision rate and the average number of correct contacts (〈n_{cc}〉) formed when both c-ssDNAs interpenetrate each other. Detailed lattice model simulations suggest that 〈n_{cc}〉∝L/V where L is the length of c-ssDNAs and V is the reaction volume. Further numerical analysis revealed the scaling for the average radius of gyration of c-ssDNAs (R_{g}) with their length as R_{g}∝sqrt[L]. Since the reaction space will be approximately a sphere with radius equals to 2R_{g} and V∝L^{3/2}, one obtains k_{N}∝1/sqrt[L]. When c-ssDNAs are nonrepetitive, the overall renaturation rate becomes as k_{R}∝k_{N}L, and one finally obtains k_{R}∝sqrt[L] in line with the experimental observations. When c-ssDNAs are repetitive with a complexity of c, earlier models suggested the scaling k_{R}∝sqrt[L]/c, which breaks down at c=L. This clearly suggests the existence of at least two different pathways of renaturation in the case of repetitive c-ssDNAs, viz., via incorrect contacts and trap correct contacts. The trap correct contacts can lead to the formation of partial duplexes which can keep the complementary strands in the close proximity for a prolonged timescale. This is essential for the extended 1D slithering, inchworm movements, and internal displacement mechanisms which can accelerate the searching for the correct contacts. Clearly, the extent of slithering dynamics will be inversely proportional to the complexity. When the complexity is close to the length of c-ssDNAs, the pathway via incorrect contacts will dominate. When the complexity is much less than the length of c-ssDNA, pathway via trap correct contacts would be the dominating one.

Fast and Robust Exudate Detection in Retinal Fundus Images Using Extreme Learning Machine Autoencoders and Modified KAZE Features

Diabetic retinopathy(DR) is a health condition that affects the retinal blood vessels(BV) and arises in over half of people living with diabetes. Exudates(EX) are significant indications of DR. Early detection and treatment can prevent vision loss in many cases. EX detection is a challenging problem for ophthalmologists due to its different sizes and elevations as retinal fundus images frequently have irregular illumination and are poorly contrasting. Manual detection of EX is a time-consuming process to diagnose a mass number of diabetic patients. In the domain of signal processing, both SIFT (scale-invariant feature transform) and SURF (speed-up robust feature) methods are predominant in scale-invariant location retrieval and have shown a range of advantages. But, when extended to medical images with corresponding weak contrast between reference features and neighboring areas, these methods cannot differentiate significant features. Considering these, in this paper, a novel method is proposed based on modified KAZE features, which is an emerging technique to extract feature points and extreme learning machine autoencoders(ELMAE) for robust and fast localization of the EX in fundus images. The main stages of the proposed method are pre-processing, OD localization, dimensionality reduction using ELMAE, and EX localization. The proposed method is evaluated based on the freely accessible retinal database DIARETDB0, DIARETDB1, e-Ophtha, MESSIDOR, and local retinal database collected from Silchar Medical College and Hospital(SMCH). The sensitivity, specificity, and accuracy obtained by the proposed method are 96.5%, 96.4%, and 97%, respectively, with the processing time of 3.19 seconds per image. The results of this study are satisfactory with state-of-the-art methods. The results indicate that the approach taken can detect EX with less processing time and accurately from the fundus images.

Multiple transcription auto regulatory loops can act as robust oscillators and decision-making motifs

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We have shown that:

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Negative transcription auto regulation can speed up the response time at the cost of reduced steady state protein levels. Under strong binding conditions, one can increase the steady state protein level by increasing the gene copy number without a compromise on the response time.

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Multiple negative transcription autoregulatory motifs can be tuned for both the response time as well as steady state protein levels by varying the gene copy number.

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Multiple negative autoregulatory loops can act as robust genetic oscillators.

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Dual feedback motifs constructed with multiple negative and positive autoregulatory loop components can act as robust oscillators and bistable decision making units within the transcription factor networks.

Response time decides how fast a gene can react against an external signal at the transcription level in a signalling cascade. The steady state protein levels of the responding genes decide the coupling between two consecutive members of a signalling cascade. A negative autoregulatory loop (NARL) present in a transcription factor network can speed up the response time of the regulated gene at the cost of reduced steady state protein level. We present here a multi NARL motif which can be tuned for both the steady state protein level as well as response time in the required direction. Remarkably, there exists an optimum Hill coefficient n_opt≅4 at which the response time of the NARL motif is at minimum. When the Hill coefficient is n < n_opt, then under strong binding conditions, one can raise the steady state protein level by increasing the gene copy number with almost no change in the response time of the multi NARL motif. Using detailed computational analysis, we show that the coupled multi NARL and positive auto regulatory loop (PARL) motifs can act as an oscillator as well as decision making component which are robust against extrinsic fluctuations in the control parameters. We further demonstrate that the period of oscillation of the coupled multi NARL-PARL dual feedback oscillator can also be fine-tuned by the gene copy number apart from the inducer concentration. We finally demonstrate robustness of bistable dual feedback decision making motifs with multi autoregulatory loop component.

ICP-MS Measurement of Trace and Rare Earth Elements in Beach Placer-Deposit Soils of Odisha, East Coast of India, to Estimate Natural Enhancement of Elements in the Environment

Inductively coupled plasma mass spectrometry (ICP-MS) has been used to measure the concentration of trace and rare earth elements (REEs) in soils. Geochemical certified reference materials such as JLk-1, JB-1, and JB-3 were used for the validation of the analytical method. The measured values were in good agreement with the certified values for all the elements and were within 10% analytical error. Beach placer deposits of soils mainly from Odisha, on the east coast of India, have been selected to study selected trace and rare earth elements (REEs), to estimate enrichment factor (EF) and geoaccumulation index (I_geo) in the natural environment. Enrichment factor (EF) and geoaccumulation index (I_geo) results showed that Cr, Mn, Fe, Co, Zn, Y, Zr, Cd and U were significantly enriched, and Th was extremely enriched. The total content of REEs (ƩREEs) ranged from 101.3 to 12,911.3 µg g⁻¹, with an average 2431.1 µg g⁻¹ which was higher than the average crustal value of ΣREEs. A high concentration of Th and light REEs were strongly correlated, which confirmed soil enrichment with monazite minerals. High ratios of light REEs (LREEs)/heavy REEs (HREEs) with a strong negative Eu anomaly revealed a felsic origin. The comparison of the chondrite normalized REE patterns of soil with hinterland rocks such as granite, charnockite, khondalite and migmatite suggested that enhancement of trace and REEs are of natural origin.

Extracorporeal Plating of High-Level Condyle fracture Through Retromandibular Approach - A Technical Note

The complex anatomy of the mandibular condyle makes its fracture management challenging and debatable. Apart from this, the approaches to condyle are also challenging as most of them depend on the surgical expertise. The retromandibular approach which was initially proposed for the vertical sub condylar osteotomies was later popularized for condyle fracture management. It is considered to be a gold standard approach in the management of low condylar fractures. Although it has its own demerits in managing high condylar fracture due to its poor access and visibility, the major complications of temporary facial nerve paresis and sialocele are very less compared to other approaches. However, modified extracorporeal plating combined with retromandibular approach proves to be effective in managing high condylar fracture. In this article, we discuss about a case of bilateral neck of condyle fracture that has been managed with the combined modified extracorporeal plating with retromandibular approach and has been followed with no complications for about 1 year.

WOANet: Whale optimized deep neural network for the classification of COVID-19 from radiography images

Coronavirus Diseases (COVID-19) is a new disease that will be declared a global pandemic in 2020. It is characterized by a constellation of traits like fever, dry cough, dyspnea, fatigue, chest pain, etc. Clinical findings have shown that the human chest Computed Tomography(CT) images can diagnose lung infection in most COVID-19 patients. Visual changes in CT scan due to COVID-19 is subjective and evaluated by radiologists for diagnosis purpose. Deep Learning (DL) can provide an automatic diagnosis tool to relieve radiologists' burden for quantitative analysis of CT scan images in patients. However, DL techniques face different training problems like mode collapse and instability. Deciding on training hyper-parameters to adjust the weight and biases of DL by a given CT image dataset is crucial for achieving the best accuracy. This paper combines the backpropagation algorithm and Whale Optimization Algorithm (WOA) to optimize such DL networks. Experimental results for the diagnosis of COVID-19 patients from a comprehensive COVID-CT scan dataset show the best performance compared to other recent methods. The proposed network architecture results were validated with the existing pre-trained network to prove the efficiency of the network.

Measurement of uranium isotope ratios in Fukushima-accident contaminated soil samples using multi collector inductively coupled plasma mass spectrometry

In the present study, ¹³⁷Cs and ²³⁸U activity concentrations, ²³⁴U/²³⁸U activity ratio, and ²³⁵U/²³⁸U isotope ratio were measured in fifteen soil samples collected from the exclusion zone around the Fukushima Daiichi Nuclear Power Station (FDNPS). The ¹³⁷Cs activity concentrations of Fukushima-accident contaminated soil samples ranged from 29.9 to 4780 kBq kg^-1 with a mean of 2007 kBq kg^-1. On the other hand, the ²³⁸U activity concentrations of these soil samples ranged from 5.2 to 22.4 Bq kg^-1 with a mean of 13.2 Bq kg^-1. The activity ratios of ²³⁴U/²³⁸U ranged from 0.973 to 1.023. The ²³⁵U/²³⁸U isotope ratios of these exclusion zone soil samples varied from 0.007246 to 0.007260, and they were similar to the natural terrestrial ratio confirming the natural origin. Using isotope dilution technique, the ²³⁵U/¹³⁷Cs activity ratio was theoretically estimated for highly ¹³⁷Cs contaminated soil samples from Fukushima exclusion zone ranged from 5.01 × 10^-8 - 6.16 × 10^-7 with a mean value of 2.51 × 10^-7.

Geochemical characterization of monazite sands based on rare earth elements, thorium and uranium from a natural high background radiation area in Tamil Nadu, India

The Kanyakumari coastal area in the southernmost part of Tamil Nadu, India is a well-known natural high background radiation area due to the abundance of monazite in beach placer deposits. In the present study, the concentrations of major oxides, rare earth elements (REEs), Th and U were measured to understand geochemical characteristics of these monazite sands. Based on the ambient dose rate, 23 locations covering an area of about 60 km along the coast were selected for sample collection. The concentrations of U and Th ranged from 1.1 to 737.8 μg g^-1 and 25.2-12250.6 μg g^-1, respectively. The Th/U ratio ranged from 2.2 to 61.6, which clearly indicated that Th was the dominant contributing radionuclide to the enhanced natural radioactivity in this coastal region. The chondrite-normalized REEs pattern of the placer deposits showed enrichment in light REEs and depletion in heavy REEs with a negative Eu anomaly that indicated the monazite sands were derived from granite, charnockite, and granitoid rocks from the Nagercoil and the Trivandrum Blocks of the Southern Granulite Terrain.

622 Potential Application of Electrocardiographs (ECG) to Diagnose Breech Presentations of Fetuses

Breech presentation, i.e. the abnormal lie of a foetus longitudinally with the buttocks closest to the mother’s cervix, is a common issue affecting up to 5% of women during delivery resulting in poor outcomes such as traumatic labour, infection and increased neonatal mortality.

Currently, abdominal palpation is the screening method of choice for breech presentation; however, 15,000 breech presentations remain undiagnosed across England annually. While routine ultrasonography has been suggested, its cost-effectiveness and scalability remain concerning, especially in low/middle-income countries.

Various algorithms have been applied to maternal trans-abdominal ECGs to obtain foetal ECGs, but current applications only allow heart rates, congenital heart defects, hypoxia, and foetal distress to be identified. The use of ECG is proposed to determine foetal position as breech foetuses tend to display QRS complexes in a similar pattern to the maternal trace, whereas cephalic foetuses would demonstrate inverted QRS complexes.

ECGs are cost-effective, safe and do not require highly skilled technicians to operate, which makes it an ideal starting platform for the development of a device with high sensitivity and reasonable diagnostic speeds to identify breech foetuses. Future considerations include the use of artificial intelligence to increase diagnostic accuracy and development of a patient-facing mobile application.

Automatic Screening of COVID-19 Using an Optimized Generative Adversarial Network

The quick spread of coronavirus disease (COVID-19) has resulted in a global pandemic and more than fifteen million confirmed cases. To battle this spread, clinical imaging techniques, for example, computed tomography (CT), can be utilized for diagnosis. Automatic identification software tools are essential for helping to screen COVID-19 using CT images. However, there are few datasets available, making it difficult to train deep learning (DL) networks. To address this issue, a generative adversarial network (GAN) is proposed in this work to generate more CT images. The Whale Optimization Algorithm (WOA) is used to optimize the hyperparameters of GAN's generator. The proposed method is tested and validated with different classification and meta-heuristics algorithms using the SARS-CoV-2 CT-Scan dataset, consisting of COVID-19 and non-COVID-19 images. The performance metrics of the proposed optimized model, including accuracy (99.22%), sensitivity (99.78%), specificity (97.78%), F1-score (98.79%), positive predictive value (97.82%), and negative predictive value (99.77%), as well as its confusion matrix and receiver operating characteristic (ROC) curves, indicate that it performs better than state-of-the-art methods. This proposed model will help in the automatic screening of COVID-19 patients and decrease the burden on medicinal services frameworks.

E-DiCoNet: Extreme learning machine based classifier for diagnosis of COVID-19 using deep convolutional network

The novel coronavirus disease (COVID-19) spread quickly worldwide, changing the everyday lives of billions of individuals. The preliminary diagnosis of COVID-19 empowers health experts and government professionals to break the chain of change and level the epidemic curve. The regular sort of COVID-19 detection test, be that as it may, requires specific hardware and generally has low sensitivity. Chest X-ray images to be used to diagnosis the COVID-19. In this work, a dataset of X-ray images with COVID-19, bacterial pneumonia, and normal was used to diagnose the COVID-19 automatically. This work to assess the execution of best in class Convolutional Neural Network (CNN) models proposed over ongoing years for clinical image classification. In particular, the modified pre-trained CNN-ResNet50 based Extreme Learning Machine classifier (ELM) has proposed for different diagnosis abnormalities such as COVID-19, Pneumonia, and normal. The proposed CNN method has trained and tested with the publicly available COVID-19, pneumonia, and normal datasets. The presented pre-trained ResNet CNN model provides accuracy, sensitivity, specificity, recall, precision, and F1 score values of 94.07, 98.15, 91.48, 85.21, 98.15, and 91.22, respectively, which is the best classification performance than other states of the art methods. This study introduced a computationally productive and exceptionally exact model for multi-class grouping of three diverse contamination types from alongside Normal people. This CNN model can help in the automatic diagnosis of COVID-19 cases and help decrease the burden on medicinal services frameworks.

A lattice model on the rate of in vivo site-specific DNA-protein interactions

We develop a lattice model of site-specific DNA-protein interactions under in vivo conditions where DNA is modelled as a self-avoiding random walk that is embedded in a cubic lattice box resembling the living cell. The protein molecule searches for its cognate site on DNA via a combination of three dimensional (3D) and one dimensional (1D) random walks. Hopping and intersegmental transfers occur depending on the conformational state of DNA. Results show that the search acceleration ratio (= search time in pure 3D route/search time in 3D and 1D routes) asymptotically increases towards a limiting value as the dilution factor of DNA (= volume of the cell/the volume of DNA) tends towards infinity. When the dilution ratio is low, then hopping and intersegmental transfers significantly enhance the search efficiency over pure sliding. At high dilution ratio, hopping does not enhance the search efficiency much since under such situation DNA will be in a relaxed conformation that favors only sliding. In the absence of hopping and intersegmental transfers, there exists an optimum sliding time at which the search acceleration ratio attains a maximum in line with the current theoretical results. However, existence of such optimum sliding length disappears in the presence of hopping. When the DNA is confined in a small volume inside the cell resembling a natural cell system, then there exists an optimum dilution and compression ratios (= total cell volume/volume in which DNA is confined) at which the search acceleration factor attains a maximum especially in the presence of hopping and intersegmental transfers. These optimum values are consistent with the values observed in the Escherichia coli cell system. In the absence of confinement of DNA, position of the specific binding site on the genomic DNA significantly influences the search acceleration. However, such position dependent changes in the search acceleration ratio will be nullified in the presence of hopping and intersegmental transfers especially when the DNA is confined in a small volume that is embedded in an outer cell.

A Microwave Digestion Technique for the Analysis of Rare Earth Elements, Thorium and Uranium in Geochemical Certified Reference Materials and Soils by Inductively Coupled Plasma Mass Spectrometry

Two different digestion methods—microwave digestion (Mw) and Savillex digestion (Sx)—were used to evaluate the best quality control for analysis of the rare earth elements, Th and U in the geochemical certified reference material JSd-2, supplied by the Geological Survey of Japan (GSJ). The analysis of trace elements was carried out using inductively coupled plasma mass spectrometry (ICP-MS). The digestion recovery was > 90% for almost all elements by both methods. Mw-4 (four repeats of the microwave digestion) was found to be more effective and faster than Sx. In order to evaluate the efficiency of Mw-4, three other GSJ certified reference materials, JLk-1, JB-1 and JB-3, as well as five different soil samples from Belarus, Japan, Serbia and Ukraine were also analyzed. The Mw-4 method was seen to be promising for complete digestion and recovery of most of the elements. The U/Th ratio showed some heterogeneity for Ukraine and Serbia soils affected by Chernobyl nuclear power plant accident and depleted uranium contamination, respectively. This method can be successfully applied to any type of soils for elemental analyses.

Precise Measurement of Tellurium Isotope Ratios in Terrestrial Standards Using a Multiple Collector Inductively Coupled Plasma Mass Spectrometry

Precise tellurium (Te) isotope ratio measurement using mass spectrometry is a challenging task for many decades. In this paper, Te isotope ratio measurements using multi-collector inductively coupled plasma mass spectrometry (MC–ICP–MS) in terrestrial Te standards have been reported. Newly developed Faraday cup with 10¹² Ω resistor is used to measure low abundance ¹²⁰Te, whereas the 10¹¹ Ω resistor is used to measure other Te isotopes. The relative standard deviation obtained for Te isotope ratio measurement by Faraday cups of ¹²⁰Te/¹²⁸Te [0.002907(05)], ¹²²Te/¹²⁸Te [0.079646(10)], ¹²³Te/¹²⁸Te [0.027850(07)], ¹²⁵Te/¹²⁸Te [0.221988(09)], ¹²⁶Te/¹²⁸Te [0.592202(20)], and ¹³⁰Te/¹²⁸Te [1.076277(30)] were 0.140%, 0.014%, 0.026%, 0.005%, 0.004%, and 0.004%, respectively. The measured isotope ratio results are compared with previous results obtained by thermal ionization mass spectrometry (TIMS), negative thermal ionization mass spectrometry (N–TIMS), and MC–ICP–MS, showing an improvement in the precision about one order of magnitude for ¹²⁰Te/¹²⁸Te ratio. The present study shows better precision for Te isotope ratios compared to earlier studies.

Influence of Ni Doping in SnO₂ Nanoparticles with Enhanced Visible Light Photocatalytic Activity for Degradation of Methylene Blue Dye

In this article, Nickel doped rutile structure tin oxide (SnO₂) nanoparticles have been prepared by simple chemical co-precipitation method and prepared samples were characterized by Powder X-ray Diffraction, Fourier transform infrared Spectroscopy, Microraman analysis, Photoluminescene Spectroscopy, UV-Visible Spectroscopy, Energy dispersive analysis and Field emission scanning electron microscope. XRD studies revealed the single phase tetragonal rutile structure with space group of P42/mnm. The average crystallite size of the particles was decreased from 27 to 22 nm with increasing Ni doping concentration. FTIR spectra confirmed the presence of various bands such as O-H, C-H, Sn-O-Sn. Raman modes E_g, A_1g and B_2g were assigned at 478, 630 and 740 cm^-1 which confirmed the single phase of pure and Ni doped SnO₂ nanoparticles. The photoluminescence spectra confirmed that the defect related emissions increased with increasing of Ni concentration. The UV absorption spectra showed that the absorption of the particles decreased with increasing Ni concentration and the band gap values decreased from 3.7 to 3.4 eV. EDX spectra confirmed the presence of Sn, Ni, O in pure and doped samples. The photocatalytic activity of the pure and Ni doped SnO₂ nanoparticles were analyzed by using methylene blue dye under visible light irradiation. It is concluded Ni (7%) doped SnO₂ nanoparticles have higher degradation efficiency compared to pure SnO₂ nanoparticles.

Theory of Site-Specific DNA-Protein Interactions in the Presence of Nucleosome Roadblocks

We show that nucleosomes exert a maximal amount of hindrance to the one-dimensional diffusion of transcription factors (TFs) when they are present between TFs and their cognate sites on DNA. The effective one-dimensional diffusion coefficient of TFs (χ_TF) decreases with a rise in the free-energy barrier (μ_NU) of the sliding of nucleosomes as χ_TF∝exp(-μ_NU). The average time (η_L) required by TFs to slide over L sites on DNA increases with μ_NU as η_L∝exp(μ_NU). When TFs move close to nucleosomes, then they exhibit typical subdiffusion. Nucleosomes can enhance the search dynamics of TFs when TFs are present between nucleosomes and TF binding sites. These results suggest that nucleosome-depleted regions around the cognate sites of TFs are mandatory for efficient site-specific binding of TFs. Remarkably, the genome-wide in vivo positioning pattern of TFs shows a maximum at their specific binding sites where the occupancy of nucleosomes shows a minimum. This could be a consequence of an increasing level of breathing dynamics of nucleosome cores and decreasing levels of fluctuations in the DNA binding domains of TFs as they move across TF binding sites. The dynamics of TFs becomes slow as they approach their cognate sites so that TFs form a tight site-specific complex, whereas the dynamics of nucleosomes becomes rapid so that they quickly pass through the cognate sites of TFs. Several in vivo data sets on the genome-wide positioning pattern of nucleosomes and TFs agree well with our arguments. The retarding effects of nucleosomes can be minimized when the degree of condensation of DNA is such that it can permit a jump size associated with the dynamics of TFs beyond ∼160-180 bp.

Comparative study of evaluation of the oral stereognostic ability between diabetic and nondiabetic complete denture wearers with and without denture

BACKGROUND

The diabetic subjects would have impaired oral stereognostic ability (OSA) compared with normal subjects due to diabetic neuropathy and microcirculatory disturbances. This study was conducted to compare the OSA between diabetic and nondiabetic complete denture wearers with and without denture.

MATERIALS AND METHODS

In this in vivo study the present comparative study comprised of seventy edentulous subjects (36 males and 34 females), aged from 35 to 84 rehabilitated with complete dentures (among them 35 were diabetic and 35 subjects were nondiabetic complete denture wearer). The OSA tests were conducted using acrylic test samples of 12 shaped forms, which were placed in patient's mouth for a given period of time for identification and scored according to three-point scale as OSA score and the identification time was also recorded. The data obtained were analyzed using Chi-square test, t-test, and Pearson's correlation coefficient (P < 0.05).

RESULTS

In this study, diabetic complete denture wearers got the mean OSA score of 12.43 ± 3.93 without dentures, which was lower than nondiabetic complete denture wearer group (14.82 ± 4.44). There was a significant difference (P = 0.020*) in the identification of test pieces.

CONCLUSION

Within limitations of this study, diabetic complete denture wearers showed decreased OSA than nondiabetic subjects, particularly it was significant while not wearing dentures. Oral stereognosis may be used as one of the clinical aids in predicting patient's performance to a prosthesis. Based on their response, we can educate the patient about the prognosis.

Effects of patchouli and cinnamon essential oils on biofilm and hyphae formation by Candida species

The prevalence and fatality rates with biofilm-associated candidal infections have remained a challenge to the medical fraternity despite major advances in the field of antifungal therapy. Traditionally, essential oils (EOs) from the aromatic plants have been found to be excellent therapeutic agents to treat fungal ailments. The present study explores the antivirulent and antibiofilm effects of under explored leaf EOs of Indian patchouli EO extracted from Pogostemon heyneanus (PH), Indian cassia from Cinnamomum tamala (CT) and camphor EO from C. camphora (CC) against Candida species. The EOs were investigated for its efficacy to disrupt the young and preformed Candida spp. biofilms and to inhibit the yeast to hyphal transition, a hallmark virulent trait of C. albicans. The ability of these EOs to inhibit metabolically active cells was assessed through XTT assay. Of these three EOs, CT EO showed enhanced biofilm inhibition than others and hence it was further selected to study its biomass inhibition potential and exopolysaccharide layer disruption ability. The CT EO reduced the biomass of the preformed biofilms of all three Candida strains, which was supported by confocal microscopy. It also disrupted the exopolysaccharide layer of the Candida strains as shown by scanning electron microscopy. The present findings validate the effectiveness of EOs against the virulence of Candida spp. and emphasize the pharmaceutical potential of several native but yet unexplored wild aromatic plants in the prospect of therapeutic application.

Implant-based overdenture: A review in patient perspective

A review in affected person's attitude in abstract care of edentulous patients has to be a priority in elderly individuals. The development of complete dentures (CDs) has been the selection of remedy retaining in mind the socioeconomic popularity, age, and nutritional elements. However, most of the patients complain of loss of retention in mandibular implant-supported overdentures (ODs), which compensated the downside of the loss of retention in complete dentures (CDs). Moreover, implant supported over dentures (ISOVDs) supplied accurate exceptional of lifestyles, esthetics, progressed nutritional deficiencies, and provided good patient satisfaction. The place of dental implants and desire of retentive attachments for implant supported mandibular over dentures (ISOVD) are selected on clinician preference and professional opinion. This text offers a fundamental statistics regarding implant placement, mode of treatment to be selected, and patient care. Two implants provide extraordinary long-term achievement and survival with improved oral capabilities. Single midline implant OD is costly, powerful, and may be a promising alternative. In maxilla, 4–6 implants splinted with bar have located to give true results.

Adjunctive aids for the detection of oral premalignancy

Early detection of cancer greatly decreases the morbidity and mortality rates and thereby increases the 5-year survival rates. In developing countries like India where the disease is highly prevalent focus is mainly on decreasing the mortality rates which can be easily achieved by detection at an asymptomatic stage. Visual examination has been the standard screening method for screening oral cancer through several decades, and it is well known that conventional visual examination is limited to subjective interpretation and cannot be easily achieved in certain anatomical sites. As a solution to all these adjunctive techniques have emerged, and it has been widely used. An effort is made through this paper to review the most commonly used adjunctive aids for the detection of premalignancy and cancer.

Theory on the mechanism of site-specific DNA-protein interactions in the presence of traps

The speed of site-specific binding of transcription factor (TFs) proteins with genomic DNA seems to be strongly retarded by the randomly occurring sequence traps. Traps are those DNA sequences sharing significant similarity with the original specific binding sites (SBSs). It is an intriguing question how the naturally occurring TFs and their SBSs are designed to manage the retarding effects of such randomly occurring traps. We develop a simple random walk model on the site-specific binding of TFs with genomic DNA in the presence of sequence traps. Our dynamical model predicts that (a) the retarding effects of traps will be minimum when the traps are arranged around the SBS such that there is a negative correlation between the binding strength of TFs with traps and the distance of traps from the SBS and (b) the retarding effects of sequence traps can be appeased by the condensed conformational state of DNA. Our computational analysis results on the distribution of sequence traps around the putative binding sites of various TFs in mouse and human genome clearly agree well the theoretical predictions. We propose that the distribution of traps can be used as an additional metric to efficiently identify the SBSs of TFs on genomic DNA.

Theory on the Mechanism of DNA Renaturation: Stochastic Nucleation and Zipping

Renaturation of the complementary single strands of DNA is one of the important processes that requires better understanding in the view of molecular biology and biological physics. Here we develop a stochastic dynamical model on the DNA renaturation. According to our model there are at least three steps in the renaturation process viz. nonspecific-contact formation, correct-contact formation and nucleation, and zipping. Most of the earlier two-state models combined nucleation with nonspecific-contact formation step. In our model we suggest that it is considerably meaningful when we combine the nucleation with the zipping since nucleation is the initial step of zipping and nucleated and zipping molecules are indistinguishable. Nonspecific contact formation step is a pure three-dimensional diffusion controlled collision process. Whereas nucleation involves several rounds of one-dimensional slithering and internal displacement dynamics of one single strand of DNA on the other complementary strand in the process of searching for the correct-contact and then initiate nucleation. Upon nucleation, the stochastic zipping follows to generate a fully renatured double stranded DNA. It seems that the square-root dependency of the overall renaturation rate constant on the length of reacting single strands originates mainly from the geometric constraints in the diffusion controlled nonspecific-contact formation step. Further the inverse scaling of the renaturation rate on the viscosity of reaction medium also originates from nonspecific contact formation step. On the other hand the inverse scaling of the renaturation rate with the sequence complexity originates from the stochastic zipping which involves several rounds of crossing over the free-energy barrier at microscopic levels. When the sequence of renaturing single strands of DNA is repetitive with less complexity then the cooperative effects will not be noticeable since the parallel zipping will be a dominant enhancing factor. However for DNA strands with high sequence complexity and length one needs to consider the underlying cooperative effects both at microscopic and macroscopic levels to explain various scaling behaviours of the overall renaturation rate.

Indications and management of mechanical fluid removal in critical illness

BACKGROUND

The Acute Dialysis Quality Initiative (ADQI) dedicated its Twelfth Consensus Conference (2013) to all aspects of fluid therapy, including the management of fluid overload (FO). The aim of the working subgroup 'Mechanical fluid removal' was to review the indications, prescription, and management of mechanical fluid removal within the broad context of fluid management of critically ill patients.

METHODS

The working group developed a list of preliminary questions and objectives and performed a modified Delphi analysis of the existing literature. Relevant studies were identified through a literature search using the MEDLINE database and bibliographies of relevant research and review articles.

RESULTS

After review of the existing literature, the group agreed the following consensus statements: (i) in critically ill patients with FO and with failure of or inadequate response to pharmacological therapy, mechanical fluid removal should be considered as a therapy to optimize fluid balance. (ii) When using mechanical fluid removal or management, targets for rate of fluid removal and net fluid removal should be based upon the overall fluid balance of the patient and also physiological variables, individualized, and reassessed frequently. (iii) More research on the role and practice of mechanical fluid removal in critically ill patients not meeting fluid balance goals (including in children) is necessary.

CONCLUSION

Mechanical fluid removal should be considered as a therapy for FO, but more research is necessary to determine its exact role and clinical application.

Development and validation of a stability indicating RP-HPLC method for simultaneous estimation of Olmesartan Medoxomil and Metoprolol Succinate in pharmaceutical dosage form

AIM AND BACKGROUND

A simple, rapid, precise and isocratic RP-HPLC (Reverse Phase High Performance Liquid Chromatography) method is aimed to develop for the simultaneous estimation of Olmesartan Medoxomil and Metoprolol Succinate in bulk drug and pharmaceutical dosage form.

MATERIALS AND METHODS

The quantification is carried out using YMC-Pack CN (250 × 4.6 mm, 5.0 μm) column and the mobile phase comprises of 0.05% Trifluoro acetic acid (TFA) and Acetonitrile (ACN) (70:30 v/v). The flow rate is 1.0 ml/min. The eluent is monitored at 220 nm. The retention times of Olmesartan Medoxomil and Metoprolol Succinate are 7.9 min and 4.1 min respectively. The method is validated in terms of linearity, precision, accuracy, specificity, limit of detection and limit of quantitation.

RESULTS

Linearity and percentage recoveries of both Olmesartan Medoxomil and Metoprolol Succinate are in the range of 5-35 μg/ml and 100 ± 2%, respectively. The stress testing of both the drugs individually and their mixture is carried out under acidic, alkaline, oxidation, photo-stability and thermal degradation (dry heat and wet heat) conditions and its degradation products are well resolved from the analyte peaks.

CONCLUSION

This method was successfully validated for accuracy, precision, and linearity.

Theory on the coupled stochastic dynamics of transcription and splice-site recognition

Eukaryotic genes are typically split into exons that need to be spliced together to form the mature mRNA. The splicing process depends on the dynamics and interactions among transcription by the RNA polymerase II complex (RNAPII) and the spliceosomal complex consisting of multiple small nuclear ribonucleo proteins (snRNPs). Here we propose a biophysically plausible initial theory of splicing that aims to explain the effects of the stochastic dynamics of snRNPs on the splicing patterns of eukaryotic genes. We consider two different ways to model the dynamics of snRNPs: pure three-dimensional diffusion and a combination of three- and one-dimensional diffusion along the emerging pre-mRNA. Our theoretical analysis shows that there exists an optimum position of the splice sites on the growing pre-mRNA at which the time required for snRNPs to find the 5′ donor site is minimized. The minimization of the overall search time is achieved mainly via the increase in non-specific interactions between the snRNPs and the growing pre-mRNA. The theory further predicts that there exists an optimum transcript length that maximizes the probabilities for exons to interact with the snRNPs. We evaluate these theoretical predictions by considering human and mouse exon microarray data as well as RNAseq data from multiple different tissues. We observe that there is a broad optimum position of splice sites on the growing pre-mRNA and an optimum transcript length, which are roughly consistent with the theoretical predictions. The theoretical and experimental analyses suggest that there is a strong interaction between the dynamics of RNAPII and the stochastic nature of snRNP search for 5′ donor splicing sites.

The DNA encoding most eukaryotic genes is interrupted by long sequences called introns. These introns need to be removed through the process of splicing to produce the mature messenger RNA. The process of splicing plays a critical role in determining the exact aminoacid content of the ensuing protein. Several molecules denominated small nuclear ribonucleo proteins (snRNPs) are involved in finding the appropriate 5′ donor splicing sites for splicing. Transcription and splicing occur simultaneously and the ultimate product depends on the relative speed of transcription and the stochastic dynamics underlying splicing. Here we propose a biophysically plausible theory that describes the ongoing interactions between transcription and splicing. We show that the theoretical predictions are consistent with experimental measurements of the abundance patterns of different exons and transcripts across tissues.