Improved selectivity of electrochemical aniline sensing using one-dimensional silver nanorods with high aspect ratio synthesized by ascorbic acid assisted method.

BACKGROUND: Aniline serves as a pivotal precursor in many industries such as pesticides, pharmaceuticals, and chemicals. However, its ingestion can lead to severe health consequences, including the potential to induce cancer, respiratory tract irritation, and adverse effects on the nervous and digestive systems in the human body. The widespread use of aniline in industrial processes, coupled with inadequate wastewater management that allows for the direct release of aniline into the environment, leads to surface and groundwater contamination. Therefore, it becomes crucial to devise a reliable electrochemical sensor capable of detecting even trace amounts of aniline. RESULTS: This study presents a modified polyol synthesis method for producing silver nanorods (AgNRs, length: 861-1345 nm, diameter: 66-107 nm) with preferential growth along the (111) crystal plane. Immobilizing AgNRs on a glassy carbon (GC) electrode with Nafion as a binder decreases its charge transfer resistance from 3040 to 129 kΩ and increases its electroactive area from 0.034 to 0.101 cm2. AgNRs/GC electrode exhibited an aniline detection limit of 0.032 µM and sensitivity of 1.4841 µA.M-1cm-2 within a linear range of 0-10 µM using square wave voltammetry (SWV). The reaction rate constant of aniline sensing was determined to be 0.08697 s-1. Chlorobenzene, acephate, and chlorpyrifos could not interfere aniline detection, and 26 % decrease in peak response was observed after the 10th cycle of aniline sensing. The sensor demonstrated ∼100 % recovery for aniline, comparable to the performance of high-performance liquid chromatography when applied to real-world samples like tap and river water. SIGNIFICANCE: The electrochemical sensing of aniline is notably efficient in tap and river water within the acceptable limit, by utilizing one dimensional AgNRs functionalized GC electrode. Importantly, the presence of interferents does not compromise the sensitivity of the sensor. Therefore, one dimensional AgNRs synthesized via a modified polyol route emerge as a promising electrocatalyst for the in-situ detection and determination of aniline.

Comparative study on costs incurred for treatment of patients with bacterial and fungal keratitis - A retrospective analysis.

Purpose: To compare the costs associated with medications and travel of patients with smear-proven bacterial keratitis and fungal keratitis in a tertiary care center in India. Methods: Retrospective analysis of case records of a cohort of patients who presented between April 2017 and March 2018 to a tertiary care center in India, with infectious keratitis who were smear-positive for bacteria or fungi, and whose costs of treatment and travel were supported by a philanthropic program. Results: In total, 672 case records of 177 smear-positive bacterial keratitis (BK) and 495 smear-positive fungal keratitis (FK) were included in the study. Further, 62% of BK and 75% of FK received more than one antimicrobial drug (P < 0.001). The mean total medication cost (INR) was significantly more in FK (959.1 ± 675.2) compared to BK (674.9 ± 463.7) (P < 0.0001). The mean medication cost (INR) per visit was also more for FK (201.1 ± 109.4) compared to BK (155.2 ± 84.1) (P < 0.0001). The mean total medication cost was significantly more for FK for both patients who healed with medical treatment (611.6 ± 395.6 for BK, 801.5 ± 599.9 for FK, P = 0.0005) and for patients who required TPK (953.7 ± 653.1 for BK, 1374.6 ± 701.5 for FK, P = 0.0023) compared to their respective counterparts in BK. Conclusion: Patients with fungal keratitis incurred significantly more on medications compared to patients with bacterial keratitis irrespective of whether they had healed with successful medical treatment or required therapeutic keratoplasty. Prolonged duration of treatment and the high costs of antifungal medications account for the significant economic burden of fungal keratitis.

Classification of childhood medulloblastoma into WHO-defined multiple subtypes based on textural analysis.

Childhood medulloblastoma is a case of a childhood brain tumour that requires close attention due to the low survival rate. Effective prognosis depends a lot on accurate detection of its subtype. The present study proposes a texture-based computer-aided categorization of childhood medulloblastoma samples. According to the World Health Organization, it has four subtypes (desmoplastic, classic, nodular and large). Classification is done in two levels: (i) normal and abnormal and (ii) its four subtypes. The system is evaluated on indigenous patient samples collected from the region. The main objective of database generation is to create a data set of childhood medulloblastoma samples since there exists no available benchmark data set. The proposed framework for automated classification is based on the architectural property and the distribution of cells. Five texture features were extracted for the feature set, namely: grey-level co-occurrence matrix, grey-level run length matrix, first-order histogram features, local binary pattern and Tamura features. The performance of each feature set was evaluated, both individually and in combinations, using five different classifiers. Fivefold cross-validation was used for training and testing the data set. Experiments on both individual feature sets and combinations (best-2, best-3, best-4 and all-5) of feature sets were evaluated based on the accuracy of performance. It was revealed that the combined best-4 feature set resulted in the highest accuracy of 91.3%. The precision, recall and specificity were 0.913, 0.913 and 0.97, respectively. Significantly, it implied that the all-5 feature set is not necessary to have a useful classification. Feature reduction by principal component analysis resulted in increased accuracy of 96.7%. LAY DESCRIPTION: Childhood medulloblastoma is a case of childhood brain tumour that requires high attention due to a low survival rate. Effective prognosis depends a lot on accurate detection of its subtype. The present study proposes a texture-based computer-aided categorization of childhood medulloblastoma samples. According to the World Health Organization (W.H.O), it has four subtypes (desmoplastic, classic, nodular, and large). Classification is done in two levels: i) normal and abnormal ii) its four subtypes. The system is evaluated on indigenous patient samples collected from the region. The main objective of database generation is to create a data set of childhood medulloblastoma samples since there exists no available benchmark data set. The proposed framework is a model for the automatic classification of the samples. The tissue samples obtained post-operation by doctors are converted into images, and then necessary algorithms are applied so that certain features describing each group of the image are known and studied for classification. Later these images are classified using the image features into the subtypes of abnormal samples.

Study on Contribution of Biological Interpretable and Computer-Aided Features Towards the Classification of Childhood Medulloblastoma Cells.

Diagnosis and Prognosis of brain tumour in children is always a critical case. Medulloblastoma is that subtype of brain tumour which occurs most frequently amongst children. Post-operation, the classification of its subtype is most vital for further clinical management. In this paper a novel approach of pathological subtype classification using biological interpretable and computer-aided textural features is forwarded. The classifier for accurate features prediction is built purely on the feature set obtained by segmentation of the ground truth cells from the original histological tissue images, marked by an experienced pathologist. The work is divided into five stages: marking of ground truth, segmentation of ground truth images, feature extraction, feature reduction and finally classification. Kmeans colour segmentation is used to segment out the ground truth cells from histological images. For feature extraction we used morphological, colour and textural features of the cells followed by feature reduction using Principal Component Analysis. Finally both binary and multiclass classification is done using Support Vector Method (SVM). The classification was compared using six different classifiers and performance was evaluated employing five-fold cross-validation technique. The accuracy achieved for binary and multiclass classification before applying PCA were 95.4 and 62.1% and after applying PCA were 100 and 84.9% respectively. The run-time analysis are also shown. Results reveal that this technique of cell level classification can be successfully adopted as architectural view can be confusing. Moreover it conforms substantially to the pathologist's point of view regarding morphological and colour features, with the addition of computer assisted texture feature.