Biofortification of crops with nutrients by the application of nanofertilizers for effective agriculture.

The agricultural industry is rapidly accepting daily changes and updates, and expanding to meet the basic demands of humanity. The main objective of modern agricultural practices is high profits with minimal investment, without upsetting any other form of life or abiotic factors. According to this principle, nanofertilizers are recommended for use in agriculture and are classified in different ways based on their nutritive value, functional role in the environment, chemical composition, and form of application to ensure their persistent availability in the required quantities. These nanofertilizers meet the global crop nutrient requirement of 191.8 million metric tons along with multitudes of added value, and which are highly endorsed in the agricultural field compared to other chemical fertilizers, or their usage can be reduced to less than 50% by the use of nanofertilizers. In this review, we discuss different types of nanofertilizers, their effects on crop yield, stress tolerance, and their impact on the environment. Furthermore, the different types of nanofertilizer delivery, modes of action, and toxic impacts of nanofertilizers have been discussed. Although a large number of commercially successful effects of nanofertilizers have been demonstrated, the effects of biomagnification and cellular transformation are still disputed. The effect of the biomagnification of nanofertilizers remains unclear. A suitable strategy must be developed to easily recycle nanofertilizers. It is the need of the hour to accept the use of nanofertilizers in parallel to addressing this issue.

CORRELATION OF SPINOPELVIC PARAMETERS WITH DISABILITY STATUS IN PATIENTS WITH DEGENERATIVE LUMBAR DISEASES.

Lumbar degenerative disease usually manifests in spine clinics. This study examines the spino-pelvic characteristics of lumbar degenerative disease patients as well as the clinical ramifications in the Indian population which help in early identification of sagittal spine anomalies. Purpose - to study the spinopelvic parameters and correlate them with disability status in patients with degenerative lumbar diseases. This cross-sectional observational study focused on patients aged 40 to 60, diagnosed with degenerative lumbar spine diseases, seen at the Orthopedics Outpatient Department. Thorough history, clinical examination, and disability assessment were conducted using the modified Oswestery Disability Questionnaire (ODI). Radiological evaluation included measuring spinopelvic parameters-Pelvic Incidence (PI), Pelvic Tilt (PT), Sacral Slope (SS), and Lumbar Lordosis (LL)-correlated with disability. Disability status was determined through the Oswestry Low Back Pain Disability (ODI) Questionnaire. Among the study population, the difference in mean of Pelvic Tilt, Sacral slope, Lumbar lordosis, Pelvic incidence across disability status was not statistically significant. BMI and sacral slope showed positive correlation to sacral slope and negative correlation to Pelvic Tilt, Lumbar Lordosis, ODI. This study concluded there was no association between spinopelvic characteristics and level of disability in degenerative lumbar disease. Early detection of spinopelvic changes can aid in early intervention, slow down disease progression, and lessen impairment brought on by degenerative disc diseases.

FUNCTIONAL AND RADIOLOGICAL OUTCOME FOLLOWING EXTENDED POSTERIOR CIRCUMFERENTIAL DECOMPRESSION IN THE TUBERCULOSIS OF DORSAL SPINE.

Spinal Tuberculosis ranks as one of the most common extrapulmonary varieties of tuberculosis. The study outlines the Extended Posterior Circumferential Decompression (EPCD) procedure for managing tuberculous spondylitis, a prevalent extrapulmonary form of tuberculosis. EPCD involves 360-degree dural decompression, anterior column debridement, and reconstruction following posterior instrumentation. This technique addresses both the infection and associated complications, particularly beneficial in cases with or without paraplegia. EPCD aims to improve outcomes by effectively tackling the pathology and restoring spinal stability. Purpose - to evaluate the functional and radiological outcome following Extended Posterior Circumferential Decompression in the tuberculosis of dorsal spine. A total of 10 patients were included after fulfilling inclusion criteria between July 2019 to December 2021, all patient underwent Extended Posterior Circumferential Decompression. All patients assessed using Visual analog scale (VAS), Medical Research council (MRC) grading, Frankel grading, Kyphus angle, Erythrocyte sedimentation rate (ESR), X-rays preoperative, immediate postoperative period and 9 month follow up. All patients were available for follow up, in this study mean age was 55.7±17.91. Out of 10 patients 60% were female, 40% was male. VAS, MRC grading, Frankel, ESR values, Kyphus angle showed better results in terms of functional and radiological outcome at 9 month follow up compared to preoperative values. The Employed Posterior Costotransversectomy Decortication (EPCD) technique grants ample ingress to both the lateral and anterior domains of the spinal cord, ensuring an equally efficacious decompression. This approach, characterized by its diminished morbidity, steers clear of the entanglements linked with thoracotomy and laparotomy. Moreover, it fosters prompt mobilization, thereby forestalling the adversities entailed by protracted immobility. With its capability for favorable kyphosis correction, adept surgical decompression, and enhanced functional outcomes, it stands as a beacon of surgical finesse.

A Doctor in the House, An Ethical Consideration on Treating Their Family Members: A Mixed-Method Study.

BACKGROUND:  The ethical dilemma of doctors treating their own family members has long been a contentious issue in the field of medicine. Despite these dilemmas, doctors may feel compelled to become involved in the care of family members and reluctant to set standards for themselves. Therefore, this study aimed to assess the experience of doctors in the treatment of their families in Perambalur District, Tamil Nadu, India. METHODOLOGY:  A mixed-method study was conducted among the doctors in Perambalur District, Tamil Nadu, India from December 2021 to October 2022. A semi-structured questionnaire was used to assess the socio-demographic details and the experience of doctors in treating their family members, followed by a focused group discussion (FGD). Data were analyzed using SPSS version 16 (SPSS Inc., Chicago). A scatter plot was created to visualize the relationship between age, experience of doctors, and confidence level with the frequency of treating family members. A chi-square test was performed to find any associations, and a p-value <0.05 was considered significant. For qualitative data, a fish herringbone model was constructed. RESULTS:  A total of 72 doctors participated in the study. The study found that almost all the doctors (100%) received medical requests from family members, the median number of requests received in a year was 6.5 with an interquartile range of 4-8 and three-quarters (66.6%) of them accepted the requests and treated them. However, concerns about maintaining objectivity, emotional attachment, and loss of confidentiality were cited as primary reasons for not accepting all requests. The study also found a positive relationship between age, years of experience, and the frequency of treating family members. The FGD highlighted challenges related to potential risks in managing complex cases, emotional involvement impacting decision-making, conflicts of interest, and pressures from family members and societal norms. CONCLUSION:  In the present study, almost all the doctors received requests from their family members in the last year, and more than three-fourths of the doctors treated their family members. One-fourth of the doctors rejected requests from family members due to concerns about the potential loss of objectivity and the risk of misdiagnosing symptoms caused by emotional attachments. This study sheds light on the complexities and ethical considerations doctors face when treating family members. It emphasizes the need for medical ethics education in the curriculum to guide future doctors in making ethical decisions in such situations. Implementing clear-cut medical ethics guidelines in India would be instrumental in addressing these issues and ensuring ethical practices in the medical field.

Programmed for Automatic Bone Disorder Clustering Based on Cumulative Calcium Prediction for Feature Extraction.

BACKGROUND: The prediction of bone disorders varies between ortho-physicians. A precise bone disorder cataloging system is proposed based on a renewed method for estimating calcium value from a radiological image of the bone. METHODS: A deliberate method was employed, the binning technique, for the input image which divides the input image into non-overlapping blocks to obtain accurate calcium volume estimation. In this proposed approach, the input image undergoes two stages of the process. In stage 1, input image preprocessing is accomplished with median filtering to eliminate the unwanted noise and it increases the quality of the image. Further, the processed image is fed to the Otsu-thresholding-segmentation method to highlight the affected regions from the processed bone image. The LBP (Local Binary Pattern) is a technique implemented to pull out the feature vector alone from the input image. Calcium value is estimated from abnormal regions from the segmented bone image and with the help of extracted texture features, the calcium concentration is obtained. MSVM (Multi-class Support Vector Machine) technique is applied to categorize as normal, osteoporosis, and osteopenia. In stage 2, the entire input is divided into 4 x 4 bins and preprocessing, segmentation, feature extraction, and calcium estimation process were applied similar to stage I to each bin separately and the calcium values of all bins are added together. RESULTS: Finally, stage 1 and stage 2 calcium values are summed up to obtain a more precise calcium estimation of the input image the feature vectors which were pull-out from others. The result can prove that the proposed binning technique is best for the bone disorder classification system which attained the greater accuracy of 97.4% and sensitivity of 98.3% when compared with and without binning technique. CONCLUSIONS: Validation of the results was performed with bone images, and these bone images were declared by the physician as bone disorder-affected images. The success rate of the bone disorder prediction is 80%.

Classification of Electroencephalogram Signal for Developing Brain-Computer Interface Using Bioinspired Machine Learning Approach.

Transforming human intentions into patterns to direct the devices connected externally without any body movements is called Brain-Computer Interface (BCI). It is specially designed for rehabilitation patients to overcome their disabilities. Electroencephalogram (EEG) signal is one of the famous tools to operate such devices. In this study, we planned to conduct our research with twenty subjects from different age groups from 20 to 28 and 29 to 40 using three-electrode systems to analyze the performance for developing a mobile robot for navigation using band power features and neural network architecture trained with a bioinspired algorithm. From the experiment, we recognized that the maximum classification performance was 94.66% for the young group and the minimum classification performance was 94.18% for the adult group. We conducted a recognizing accuracy test for the two contrasting age groups to interpret the individual performances. The study proved that the recognition accuracy was maximum for the young group and minimum for the adult group. Through the graphical user interface, we conducted an online test for the young and adult groups. From the online test, the same young-aged people performed highly and actively with an average accuracy of 94.00% compared with the adult people whose performance was 92.00%. From this experiment, we concluded that, due to the age factor, the signal generated by the subjects decreased slightly.

A Torn ACL Mapping in Knee MRI Images Using Deep Convolution Neural Network with Inception-v3.

The anterior cruciate ligaments (ACL) are the fundamental structures in preserving the common biomechanics of the knees and most frequently damaged knee ligaments. An ACL injury is a tear or sprain of the ACL, one of the fundamental ligaments in the knee. ACL damage most generally happens during sports, for example, soccer, ball, football, and downhill skiing, which include sudden stops or changes in direction, jumping, and landings. Magnetic resonance imaging (MRI) has a major role in the field of diagnosis these days. Specifically, it is effective for diagnosing the cruciate ligaments and any related meniscal tears. The primary objective of this research is to detect the ACL tear from MRI knee images, which can be useful to determine the knee abnormality. In this research, a Deep Convolution Neural Network (DCNN) based Inception-v3 deep transfer learning (DTL) model was proposed for classifying the ACL tear MRI images. Preprocessing, feature extraction, and classification are the main processes performed in this research. The dataset utilized in this work was collected from the MRNet database. A total of 1,370 knee MRI images are used for evaluation. 70% of data (959 images) are used for training and testing, and 30% of data (411 images) are used in this model for performance analysis. The proposed DCNN with the Inception-v3 DTL model is evaluated and compared with existing deep learning models like VGG16, VGG19, Xception, and Inception ResNet-v28. The performance metrics like accuracy, precision, recall, specificity, and F-measure are evaluated to estimate the performance analysis of the model. The model has obtained 99.04% training accuracy and 95.42% testing accuracy in performance analysis.

Eye Movement Signal Classification for Developing Human-Computer Interface Using Electrooculogram.

Human-computer interfaces (HCI) allow people to control electronic devices, such as computers, mouses, wheelchairs, and keyboards, by bypassing the biochannel without using motor nervous system signals. These signals permit communication between people and electronic-controllable devices. This communication is due to HCI, which facilitates lives of paralyzed patients who do not have any problems with their cognitive functioning. The major plan of this study is to test out the feasibility of nine states of HCI by using modern techniques to overcome the problem faced by the paralyzed. Analog Digital Instrument T26 with a five-electrode system was used in this method. Voluntarily twenty subjects participated in this study. The extracted signals were preprocessed by applying notch filter with a range of 50 Hz to remove the external interferences; the features were extracted by applying convolution theorem. Afterwards, extracted features were classified using Elman and distributed time delay neural network. Average classification accuracy with 90.82% and 90.56% was achieved using two network models. The accuracy of the classifier was analyzed by single-trial analysis and performances of the classifier were observed using bit transfer rate (BTR) for twenty subjects to check the feasibility of designing the HCI. The achieved results showed that the ERNN model has a greater potential to classify, identify, and recognize the EOG signal compared with distributed time delay network for most of the subjects. The control signal generated by classifiers was applied as control signals to navigate the assistive devices such as mouse, keyboard, and wheelchair activities for disabled people.

His bundle pacing-is it the final frontier of physiological pacing ?-A single centre experience from the Indian sub-Continent.

BACKGROUND: Long term right ventricular pacing can have deleterious effects on left ventricular (LV) function. His bundle pacing (HBP), a novel procedure can probably circumvent this setback. We investigated if (1) HBP is associated with pacing induced LV dysfunction by using LV global longitudinal strain (GLS) and (2) intermediate term performance of the Select Secure (3830) lead in the His bundle location. This report is probably the first on HBP in the Indian population. METHODS: 61 patients, with normal LV ejection fraction (EF) with a guideline based indication for permanent pacing underwent a HBP pacemaker implantation using the His Select Secure 3830 lead; with lead guided mapping for locating the His bundle. The patients underwent GLS assessment; evaluation of the His lead parameters - sensing, impedance and capture thresholds immediately after implantation and at 6 months in addition to the standard follow up. RESULTS: At 6 month follow up, the average GLS did not show significant variation from baseline in patients requiring ventricular pacing more than 40% and was similar, irrespective of selective or non selective His bundle pacing. All the patients had stable pacemaker parameters - with little change in capture threshold, lead impedance or sensing of the His bundle lead - implying electrical and mechanical stability on intermediate term follow-up. CONCLUSION: HBP is a feasible procedure in the hands of an experienced operator, with stable lead performance. It does not appear to be associated with pacing mediated left ventricular dysfunction at intermediate term follow up. It should probably become the default method of permanent pacing.

Optimized artificial neural network based performance analysis of wheelchair movement for ALS patients.

Individuals with neurodegenerative attacks loose the entire motor neuron movements. These conditions affect the individual actions like walking, speaking impairment and totally make the person in to locked in state (LIS). To overcome the miserable condition the person need rehabilitation devices through a Brain Computer Interfaces (BCI) to satisfy their needs. BMI using Electroencephalogram (EEG) receives the mental thoughts from brain and converts into control signals to activate the exterior communication appliances in the absence of biological channels. To design the BCI, we conduct our study with three normal male subjects, three normal female subjects and three ALS affected individuals from the age of 20-60 with three electrode systems for four tasks. One Dimensional Local Binary Patterns (LBP) technique was applied to reduce the digitally sampled features collected from nine subjects was treated with Grey wolf optimization Neural Network (GWONN) to classify the mentally composed words. Using these techniques, we compared the three types of subjects to identify the performances. The study proves that subjects from normal male categories performance was maximum compared with the other subjects. To assess the individual performance of the subject, we conducted the recognition accuracy test in offline mode. From the accuracy test also, we obtained the best performance from the normal male subjects compared with female and ALS subjects with an accuracy of 98.33 %, 95.00 % and 88.33 %. Finally our study concludes that patients with ALS attack need more training than that of the other subjects.

Signal identification system for developing rehabilitative device using deep learning algorithms.

Paralyzed patients were increasing day by day. Some of the neurodegenerative diseases like amyotrophic lateral sclerosis, Brainstem Leison, Stupor and Muscular dystrophy affect the muscle movements in the body. The affected persons were unable to migrate. To overcome from their problem they need some assistive technology with the help of bio signals. Electrooculogram (EOG) based Human Computer Interaction (HCI) is one of the technique used in recent days to overcome such problem. In this paper we clearly check the possibilities of creating nine states HCI by our proposed method. Signals were captured through five electrodes placed on the subjects face around the eyes. These signals were amplified with ADT26 bio amplifier, filtered with notch filter, and processed with reference power and band power techniques to extract features to detect the eye movements and mapped with Time Delay Neural Network to classify the eye movements to generate control signal to control external hardware devices. Our experimental study reports that maximum average classification of 91.09% for reference power feature and 91.55%-for band power feature respectively. The obtained result confirms that band power features with TDNN network models shows better performance than reference features for all subjects. From this outcome we conclude that band power features with TDNN network models was more suitable for classifying the eleven difference eye movements for individual subjects. To validate the result obtained from this method we categorize the subjects in age wise to check the accuracy of the system. Single trail analysis was conducted in offline to identify the recognizing accuracy of the proposed system. The result summarize that band power features with TDNN network models exceed the reference power with TDNN network model used in this study. Through the outcome we conclude that that band power features with TDNN network was more suitable for designing EOG based HCI in offline mode.

DESIGN AND DEVELOPMENT OF HUMAN COMPUTER INTERFACE USING ELECTROOCULOGRAM WITH DEEP LEARNING.

Today's life assistive devices were playing significant role in our life to communicate with others. In that modality Human Computer Interface (HCI) based Electrooculogram (EOG) playing vital part. By using this method we can able to overcome the conventional methods in terms of performance and accuracy. To overcome such problem we analyze the EOG signal from twenty subjects to design nine states EOG based HCI using five electrodes system to measure the horizontal and vertical eye movements. Signals were preprocessed to remove the artifacts and extract the valuable information from collected data by using band power and Hilbert Huang Transform (HHT) and trained with Pattern Recognition Neural Network (PRNN) to classify the tasks. The classification results of 92.17% and 91.85% were shown for band power and HHT features using PRNN architecture. Recognition accuracy was analyzed in offline to identify the possibilities of designing HCI. We compare the two feature extraction techniques with PRNN to analyze the best method for classifying the tasks and recognizing single trail tasks to design the HCI. Our experimental result confirms that for classifying as well as recognizing accuracy of the collected signals using band power with PRNN shows better accuracy compared to other network used in this study. We compared the male subjects performance with female subjects to identify the performance. Finally we compared the male as well as female subjects in age group wise to identify the performance of the system. From that we concluded that male performance was appreciable compared with female subjects as well as age group between 26 to 32 performance and recognizing accuracy were high compared with other age groups used in this study.

Electroencephalogram based communication system for locked in state person using mentally spelled tasks with optimized network model.

Due to growth in population, Individual persons with disabilities are increasing daily. To overcome the disability especially in Locked in State (LIS) due to Spinal Cord Injury (SCI), we planned to design four states moving robot from four imagery tasks signals acquired from three electrode systems by placing the electrodes in three positions namely T1, T3 and FP1. At the time of the study we extract the features from Continuous Wavelet Transform (CWT) and trained with Optimized Neural Network model to analyze the features. The proposed network model showed the highest performances with an accuracy of 93.86 % then that of conventional network model. To confirm the performances we conduct offline test. The offline test also proved that new network model recognizing accuracy was higher than the conventional network model with recognizing accuracy of 97.50 %. To verify our result we conducted Information Transfer Rate (ITR), from this analysis we concluded that optimized network model outperforms the other network models like conventional ordinary Feed Forward Neural Network, Time Delay Neural Network and Elman Neural Networks with an accuracy of 21.67 bits per sec. By analyzing classification performances, recognizing accuracy and Information Transformation Rate (ITR), we concluded that CWT features with optimized neural network model performances were comparably greater than that of normal or conventional neural network model and also the study proved that performances of male subjects was appreciated compared to female subjects.

A Goal Unrealized: Patient Empowerment on Hand Hygiene- A Web-Based Survey from India.

INTRODUCTION: Each year, millions of patients around the world are affected by Health Care Associated Infections (HCAIs). Understanding and assessing the global burden of HCAI is one of the key areas of work to improve the hand hygiene. AIM: To assess the patient empowerment and awareness on hand hygiene among online users. MATERIALS AND METHODS: A web-based cross-sectional survey was conducted during September 2013 to December 2013 among adults. A predesigned questionnaire to assess the awareness on hand hygiene was sent to volunteers through emails and social networking sites. The data were transferred to excel sheet and analyzed in Epi info and represented in proportions and percentages. RESULTS: Total 94 (57%) participants responded to the survey among which 51.1% were males and 48.9% were females. Majority of them belongs to the age group of 20 to 35 years. Only 28.7% of them said they will ever ask health care worker to wash their hands before they examine. A 27.7% of the participants reported that their country/community have a program that educates/communicates with patients about the importance of hand hygiene. CONCLUSION: Adherence and compliance to hand hygiene practices is suboptimal among people. There seems to be a lack of knowledge regarding hand hygiene.

MRI-Based Texture Analysis to Differentiate Sinonasal Squamous Cell Carcinoma from Inverted Papilloma.

BACKGROUND AND PURPOSE: Because sinonasal inverted papilloma can harbor squamous cell carcinoma, differentiating these tumors is relevant. The objectives of this study were to determine whether MR imaging-based texture analysis can accurately classify cases of noncoexistent squamous cell carcinoma and inverted papilloma and to compare this classification performance with neuroradiologists' review. MATERIALS AND METHODS: Adult patients who had inverted papilloma or squamous cell carcinoma resected were eligible (coexistent inverted papilloma and squamous cell carcinoma were excluded). Inclusion required tumor size of >1.5 cm and preoperative MR imaging with axial T1, axial T2, and axial T1 postcontrast sequences. Five well-established texture analysis algorithms were applied to an ROI from the largest tumor cross-section. For a training dataset, machine-learning algorithms were used to identify the most accurate model, and performance was also evaluated in a validation dataset. On the basis of 3 separate blinded reviews of the ROI, isolated tumor, and entire images, 2 neuroradiologists predicted tumor type in consensus. RESULTS: The inverted papilloma (n = 24) and squamous cell carcinoma (n = 22) cohorts were matched for age and sex, while squamous cell carcinoma tumor volume was larger (P = .001). The best classification model achieved similar accuracies for training (17 squamous cell carcinomas, 16 inverted papillomas) and validation (7 squamous cell carcinomas, 6 inverted papillomas) datasets of 90.9% and 84.6%, respectively (P = .537). For the combined training and validation cohorts, the machine-learning accuracy (89.1%) was better than that of the neuroradiologists' ROI review (56.5%, P = .0004) but not significantly different from the neuroradiologists' review of the tumors (73.9%, P = .060) or entire images (87.0%, P = .748). CONCLUSIONS: MR imaging-based texture analysis has the potential to differentiate squamous cell carcinoma from inverted papilloma and may, in the future, provide incremental information to the neuroradiologist.