Optimizing Ni-Cr patterned boron-doped diamond band electrodes: Doping effects on electrochemical efficiency and posaconazole sensing performance.

There is growing interest in developing diamond electrodes with defined geometries such as, for example, micrometer-sized electrode arrays to acquire signals for electroanalysis. For electroanalytical sensing applications, it is essential to achieve precise conductive patterns on the insulating surface. This work provides a novel approach to boron-doped diamond patterning using nichrome masking for selective seeding on an oxidized silicon substrate. The optimized process involves nichrome deposition, sonication, chemical etching, seeding, and tailored chemical vapor deposition of boron-doped diamond with an intrinsic layer to suppress boron diffusion. Through a systematic investigation, it was determined that isolated boron-doped diamond band electrodes can be efficiently produced on non-conductive silica. Additionally, the influence of boron doping on electrochemical performance was studied, with higher doping enhancing the electrochemical response of band electrodes. To demonstrate sensing capabilities, boron-doped diamond bands were used to detect posaconazole, an antifungal drug, exploiting its electroactive behaviour. A linear correlation between posaconazole concentration and oxidation peak current was observed over 1.43 × 10-8 - 5.71 × 10-6 M with a 1.4 × 10-8 M detection limit. The developed boron-doped diamond microbands could significantly impact the field of electroanalysis, facilitating detection of diverse biologically relevant molecules. Overall, this diamond patterning approach overcomes major challenges towards all-diamond electrochemical sensor chips.

Smart Seizure Detection System: Machine Learning Based Model in Healthcare IoT.

AIMS: Epilepsy, the tendency to have recurrent seizures, can have various causes, including brain tumors, genetics, stroke, brain injury, infections, and developmental disorders. Epileptic seizures are usually transient events. They normally leave no trace after the postictal recovery period has passed. BACKGROUND: An electroencephalogram (EEG) can only detect brain activity during the recording. It will be detected if an epileptogenic focus or generalized abnormality is active during the recording. OBJECTIVE: This work demonstrated a smart seizure detection system for Healthcare IoT, which is a challenging problem of EEG data analysis. METHOD: The study suggested an integrated methodology in recognition of the drawbacks of manual identification and the significant negative effects of uncontrollable seizures on patients' lives. RESULT: The research shows remarkable accuracy, up to 100% in some experiments, by combining classifier ensembles like Decision Trees, Logistic Regression, and Support Vector Machine with different signal processing techniques like Discrete Wavelet Transform, Hjorth Parameters, and statistical features. The results were compared using the kNN classifier, compared with other datasets and other state-of-the-art techniques. CONCLUSION: Healthcare IoT is further utilized by the methodology, which takes a comprehensive approach using classifier ensembles and signal processing approaches resulting in real-time data to help them make better decisions. This demonstrates how well the suggested method works for smart seizure detection, which is a crucial development for better patient outcomes.

Post-partum cerebral venous sinus thrombosis: A case report.

INTRODUCTION: Cerebral venous sinus thrombosis (CVST) is a rare and life-threatening condition that may be encountered during pregnancy and puerperium. The diagnosis of CVST is a challenge because of its varied presentation. CASE REPORT: A 28-year-old woman presented with headache, projectile vomiting, and generalized tonic-clonic seizures 10 days after delivery by cesarean section. She had an uneventful antenatal period of 38 weeks of gestation. High clinical suspicion and the availability of magnetic resonance venography helped in making a diagnosis of CVST. She was successfully managed with a low-molecular-weight heparin (LMWH) and anti-epileptic therapy with no residual complications. DISCUSSION: Pregnancy induces several prothrombotic changes in the coagulation system that predispose to CVST. These changes persist for six to eight weeks after birth. Infection and cesarean section are the additional risk factors for CVST during puerperium. The symptoms of CVST depend on the sinuses and veins involved, raised intracranial pressure, and the extent of brain parenchymal injury. CONCLUSION: Greater awareness of the disease and the availability of imaging modalities have contributed to the early diagnosis and favorable outcomes in these cases. LMWH is the main stay of treatment in this disease.

Influence of scanning protocol on the accuracy of complete-arch digital implant scans: An in vitro study.

OBJECTIVE: This in-vitro study assessed the influence of two intraoral scanning (IOS) protocols on the accuracy (trueness and precision) of digital scans performed in edentulous arches. METHODS: Twenty-two abutment-level master casts of edentulous arches with at least four implants were scanned repeatedly five times, each with two different scanning protocols. Protocol A (IOS-A) consisted of scanning the edentulous arch before inserting the implant scan bodies, followed by their insertion and its subsequent digital acquisition. Protocol B (IOS-B) consisted of scanning the edentulous arch with the scan bodies inserted from the outset. A reference scan from each edentulous cast was obtained using a laboratory scanner. Trueness and precision were calculated using the spatial fit analysis, cross-arch distance, and virtual Sheffield test. Statistical analysis was performed using generalized estimating equations (GEEs). Statistical significance was set at α = .05. RESULTS: In the spatial fit test, the precision of average 3D distances was 45 µm (±23 µm) with protocol IOS-A and 25 µm (±10 µm) for IOS-B (p < .001), and the trueness of average 3D distances was 44 µm (±24 µm) with protocol IOS-A and 24 µm (±7 µm) for IOS-B (p < .001). Cross-arch distance precision was 59 µm (±53 µm) for IOS-A and 41 µm (±43 µm) for IOS-B (p = .0035), and trueness was 64 µm (±47 µm) for IOS-A and 50 µm (±40 µm) for IOS-B (p = .0021). Virtual Sheffield precision was 286 µm (±198 µm) for IOS-A and 146 µm (±92 µm) for IOS-B (p < .001), and trueness was 228 µm (±171 µm) for IOS-A and 139 µm (±92 µm) for IOS-B (p < .001). CONCLUSIONS: The IOS-B protocol demonstrated significantly superior accuracy. Placement of scan bodies before scanning the edentulous arch is recommended to improve the accuracy of complete-arch intraoral scanning.

A Novel Detection of Cerebrovascular Disease using Multimodal Medical Image Fusion.

BACKGROUND: Diseases are medical situations that are allied with specific signs and symptoms. A disease may be instigated by internal dysfunction or external factors like pathogens. Cerebrovascular disease can progress from diverse causes, comprising thrombosis, atherosclerosis, cerebral venous thrombosis, or embolic arterial blood clot. OBJECTIVE: In this paper, authors have proposed a robust framework for the detection of cerebrovascular diseases employing two different proposals which were validated by use of other dataset. METHODS: In proposed model 1, the Discrete Fourier transform is used for the fusion of CT and MR images which was classified them using machine learning techniques and pre-trained models while in proposed model 2, the cascaded model was proposed. The performance evaluation parameters like accuracy and losses were evaluated. RESULTS: 92% accuracy was obtained using Support Vector Machine using Gray Level Difference Statistics and Shape features with Principal Component Analysis as a feature selection technique while Inception V3 resulted in 95.6% accuracy while the cascaded model resulted in 96.21% accuracy. CONCLUSION: The cascaded model is later validated on other datasets which results in 0.11% and 0.14% accuracy improvement for TCIA and BRaTS datasets respectively.

A Study of the Long-Term Electrochemical Stability of Thin-Film Titanium-Platinum Microelectrodes and Their Comparison to Classic, Wire-Based Platinum Microelectrodes in Selected Inorganic Electrolytes.

This paper discusses the electrochemical properties of thin-film, planar, titanium-platinum (Ti-Pt) microelectrodes fabricated using glass or silicon substrates and compares their performance to the classic platinum (Pt) microelectrodes embedded in glass. To analyze the possible differences coming both from the size of the tested electrodes as well as from the substrate, short- and long-term electrochemical tests were performed on selected water electrolytes (KCl, HCl, KOH). To study the electrochemical response of the electrodes, the cyclic voltammetry (CV) measurements were carried out at different scanning rates (from 5 to 200 mV/s). Long-term tests were also conducted, including one thousand cycles with a 100 mV/s scan rate to investigate the stability of the tested electrodes. Before and after electrochemical measurements, the film morphology was analyzed using a scanning electron microscope (SEM). The good quality of the thin-film Pt electrodes and the high repeatability in electrochemical response have been shown. There are minor differences in standard deviation values taken from electrochemical measurements, comparing thin-film and wire-based electrodes. Damages or any changes on the electrodes' surfaces were revealed by SEM observations after long-term electrochemical tests.

Attention-deficit hyperactivity disorder and psychostimulant use in patients seeking dental care-Associations with common orofacial pain complaints.

BACKGROUND: Dental medicine should expand its scope to properly assess medical and psychosocial factors that might have an impact on patients' oral health. Based on previous literature and clinical experience, attention-deficit/hyperactivity disorder and psychostimulant medications might represent factors associated with orofacial pain symptoms. OBJECTIVE: The aim of the study was to assess whether common orofacial pain complaints such as jaw pain, jaw clicking, teeth clenching and headaches are more prevalent in dental patients who have an ADHD diagnosis and/or use psychostimulant medications. METHODS: Orofacial pain symptoms prevalence was compared among four groups from a sample of new patients seeking dental care at Tufts University School of Dental Medicine (n = 11 699) based on ADHD diagnosis and psychostimulants intake: G1: no ADHD, no stimulants; G2: yes ADHD, yes stimulants; G3: yes ADHD, no stimulants; G4: no ADHD, yes stimulants. RESULTS: In multivariable logistic regression models adjusting for age, gender, tobacco use, and alcohol consumption, significant differences were found for clenching (p < .0001), jaw pain (p < .0001), and headache (p < .0001). Compared to G1, two groups (G2 and G4) exhibited significantly higher odds of clenching and headaches, whereas only G2 exhibited significantly higher odds of jaw pain. CONCLUSIONS: In comparison with patients without ADHD and not taking psychostimulants medications, dental patients using psychostimulants with and without ADHD diagnosis report headaches and teeth clenching more frequently, while jaw pain is reported more frequently only by those taking psychostimulants with an ADHD diagnosis. Further research is necessary to assess the nature of these associations and their clinical relevance.

Evaluation of marginal and internal adaptation of veneers generated by the guided prosthetic tooth preparation system.

OBJECTIVE: This in vitro study aimed to assess and contrast the marginal and internal adaptation of all-ceramic prefabricated veneers manufactured via the FirstFit guided tooth preparation system against all-ceramic veneers produced using the chairside Computer-Aided Design/Computer Aided Manufacture (CAD/CAM) system following identical guided preparation protocols. MATERIALS AND METHODS: Two main groups were included, with 16 lithium disilicate veneers per group. Four typodonts were used for the test (FirstFit) and control CAD/CAM groups. Intraoral scans created master casts and preparation guides. Guides performed preparations on typodont teeth (two central incisors and two lateral incisors). Prepared teeth were scanned (CEREC Omnicam) to design and mill CAD/CAM veneers. Marginal gap thickness and cement space thickness were measured using light microscopy at four locations: marginal, cervical internal, middle internal, and incisal internal. RESULTS: No significant difference existed between groups for marginal adaptation (p = 0.058) or incisal internal adaptation (p = 0.076). The control group had significantly lower values for middle internal adaptation (p = 0.023) and cervical internal adaptation (p = 0.019). CONCLUSIONS: Guided preparation evaluation showed no significant differences in marginal or incisal internal adaptation. The CAD/CAM group had significantly lower middle and cervical internal adaptation values.

Computational Model for the Detection of Diabetic Retinopathy in 2-D Color Fundus Retina Scan.

BACKGROUND: Diabetic Retinopathy (DR) is a growing problem in Asian countries. DR accounts for 5% to 7% of all blindness in the entire area. In India, the record of DR-affected patients will reach around 79.4 million by 2030. AIMS: The main objective of the investigation is to utilize 2-D colored fundus retina scans to determine if an individual possesses DR or not. In this regard, Engineering-based techniques such as deep learning and neural networks play a methodical role in fighting against this fatal disease. METHODS: In this research work, a Computational Model for detecting DR using Convolutional Neural Network (DRCNN) is proposed. This method contrasts the fundus retina scans of the DR-afflicted eye with the usual human eyes. Using CNN and layers like Conv2D, Pooling, Dense, Flatten, and Dropout, the model aids in comprehending the scan's curve and color-based features. For training and error reduction, the Visual Geometry Group (VGG-16) model and Adaptive Moment Estimation Optimizer are utilized. RESULTS: The variations in a dataset like 50%, 60%, 70%, 80%, and 90% images are reserved for the training phase, and the rest images are reserved for the testing phase. In the proposed model, the VGG-16 model comprises 138M parameters. The accuracy is achieved maximum rate of 90% when the training dataset is reserved at 80%. The model was validated using other datasets. CONCLUSION: The suggested contribution to research determines conclusively whether the provided OCT scan utilizes an effective method for detecting DRaffected individuals within just a few moments.

Empirical Curvelet-ridgelet Wavelet Transform for Multimodal Fusion of Brain Images.

BACKGROUND: Empirical curvelet and ridgelet image fusion is an emerging technique in the field of image processing that aims to combine the benefits of both transforms. OBJECTIVE: The proposed method begins by decomposing the input images into curvelet and ridgelet coefficients using respective transform algorithms for Computerized Tomography (CT) and magnetic Resonance Imaging (MR) brain images. METHODS: An empirical coefficient selection strategy is then employed to identify the most significant coefficients from both domains based on their magnitude and directionality. These selected coefficients are coalesced using a fusion rule to generate a fused coefficient map. To reconstruct the image, an inverse curvelet and ridgelet transform was applied to the fused coefficient map, resulting in a high-resolution fused image that incorporates the salient features from both input images. RESULTS: The experimental outcomes on real-world datasets show how the suggested strategy preserves crucial information, improves image quality, and outperforms more conventional fusion techniques. For CT Ridgelet-MR Curvelet and CT Curvelet-MR Ridgelet, the authors' maximum PSNRs were 58.97 dB and 55.03 dB, respectively. Other datasets are compared with the suggested methodology. CONCLUSION: The proposed method's ability to capture fine details, handle complex geometries, and provide an improved trade-off between spatial and spectral information makes it a valuable tool for image fusion tasks.

Effect of varying hydrologic regime on seasonal total maximum daily loads (TDML) in an agricultural watershed.

Rising hypoxia due to the eutrophication of riverine ecosystems is primarily caused by the transport of nutrients. The majority of existing TMDL models cannot be efficienty applied to represent nutrient concentrations in riverine ecosystems having varying flow regimes due to seasonal differences. Accurate TMDL assessment requires nutrient loads and suspended matter estimation under varying flow regimes with minimal uncertainty. Though a large database can enhance accuracy, it can be resource intensive. This study presents the design of an innovative modeling strategy to optimize the use of existing datasets to effectively represent streamflow-load dynamics while minimizing uncertainty. The study developed an approach to assess TMDLs using six different flux models and kriging techniques (i) to enhance the accuracy of nutrient load estimation under different hydrologic regimes (flow stratifications) and (ii) to derive an optimal modeling strategy and sampling scheme for minimizing uncertainty. The flux models account for uncertainty in load prediction across varying flow strata, and the deployment of multiple load calculation procedures. Further, the proposed flux approach allows the determination of load exceedance under different TMDL scenarios aimed at minimizing uncertainty to achieve reliable load predictions. The study employed a 10-year dataset (2009-2018) consisting of daily flow data (m3/sec) and weekly data (mg/L) for nitrogen (N), phosphorus (P) and total suspended solids (TSS) concentrations in three distinct agricultural sites in+ the Minnesota River Watershed. The outcomes were analyzed geospatially in a Geographic Information System (GIS) environment using the kriging interpolation technique. The study recommends (i) triple stratification of flows to obtain accurate load estimates, and (ii) an optimal sampling scheme for nitrogen and phosphorous with 30.6 % and 49.8 % datapoints from high flow strata. The study outcomes are expected to contribute to the planning of economically and technically sound combinations of best management practices (BMPs) required for achieving total maximum daily loads (TMDL) in a watershed.

Bandgaps of long-period polytypes of IV, IV-IV, and III-V semiconductors estimated with an Ising-type additivity model.

We apply an Ising-type model to estimate the bandgaps of the polytypes of group IV elements (C, Si, and Ge) and binary compounds of groups: IV-IV (SiC, GeC, and GeSi), and III-V (nitride, phosphide, and arsenide of B, Al, and Ga). The models use reference bandgaps of the simplest polytypes comprising 2-6 bilayers calculated with the hybrid density functional approximation, HSE06. We report four models capable of estimating bandgaps of nine polytypes containing 7 and 8 bilayers with an average error of ≲0.05 eV. We apply the best model with an error of <0.04 eV to predict the bandgaps of 497 polytypes with up to 15 bilayers in the unit cell, providing a comprehensive view of the variation in the electronic structure with the degree of hexagonality of the crystal structure. Within our enumeration, we identify four rhombohedral polytypes of SiC-9R, 12R, 15R(1), and 15R(2)-and perform detailed stability and band structure analysis. Of these, 15R(1) that has not been experimentally characterized has the widest bandgap (>3.4 eV); phonon analysis and cohesive energy reveal 15R(1)-SiC to be metastable. Additionally, we model the energies of valence and conduction bands of the rhombohedral SiC phases at the high-symmetry points of the Brillouin zone and predict band structure characteristics around the Fermi level. The models presented in this study may aid in identifying polytypic phases suitable for various applications, such as the design of wide-gap materials, that are relevant to high-voltage applications. In particular, the method holds promise for forecasting electronic properties of long-period and ultra-long-period polytypes for which accurate first-principles modeling is computationally challenging.