Diagnosis of Prostate Cancer Using GLCM Enabled KNN Technique by Analyzing MRI Images.

Cancer has a disproportionately large influence on the death rate of adults. A patient needs to get a diagnosis of their condition as quickly as is humanly feasible in order to have the greatest chance of surviving their sickness. Skilled medical professionals use medical imaging and other traditional diagnostic methods to search for clues that may indicate the presence of malignant tendencies inside the body. Nevertheless, manual diagnosis may be time-consuming and subjective owing to the wide range of interobserver variability induced by the enormous number of medical imaging data. This variability is caused by the fact that medical imaging data are collected. Because of this, the process of accurately diagnosing a patient could become more difficult. To execute jobs that included machine learning and the interpretation of complicated imagery, cutting-edge computer technology was necessary. Since the 1980s, researchers have been working on developing a computer-aided diagnostic system that would help medical professionals in the early diagnosis of various malignancies. According to the most recent projections, prostate cancer will be discovered in the body of one out of every seven men at some time throughout the course of their life. It is unacceptable how many men are being told that they have prostate cancer, and the condition is responsible for the deaths of a rising number of men every year. Because of the high quality and multidimensionality of the MRI pictures, you will also need a powerful diagnosis system in addition to the CAD tools. Since it has been shown that CAD technology is beneficial, researchers are looking at methods to improve the accuracy, precision, and speed of the systems that use it. The effectiveness of CAD technology has been shown. This research proposes a strategy that is both effective and efficient for the processing of images and the extraction of features as well as for machine learning. This work makes use of MRI scans and machine learning in an effort to detect prostate cancer at an early stage. Histogram equalization is used while doing the preliminary processing on photographs. The image's overall quality is elevated as a result. The fuzzy C means approach is used in order to segment the images. Using a Gray Level Cooccurrence Matrix (GLCM), it is feasible to extract features from a dataset. The KNN, random forest, and AdaBoost classification algorithms are used in the classification process.

Development of Machine Learning and Medical Enabled Multimodal for Segmentation and Classification of Brain Tumor Using MRI Images.

The improper and excessive growth of brain cells may lead to the formation of a brain tumor. Brain tumors are the major cause of death from cancer. As a direct consequence of this, it is becoming more challenging to identify a treatment that is effective for a specific kind of brain tumor. The brain may be imaged in three dimensions using a standard MRI scan. Its primary function is to examine, identify, diagnose, and classify a variety of neurological conditions. Radiation therapy is employed in the treatment of tumors, and MRI segmentation is used to guide treatment. Because of this, we are able to assess whether or not a piece that was spotted by an MRI is a tumor. Using MRI scans, this study proposes a machine learning and medically assisted multimodal approach to segmenting and classifying brain tumors. MRI pictures contain noise. The geometric mean filter is utilized during picture preprocessing to facilitate the removal of noise. Fuzzy c-means algorithms are responsible for segmenting an image into smaller parts. The identification of a region of interest is facilitated by segmentation. The GLCM Grey-level co-occurrence matrix is utilized in order to carry out the process of dimension reduction. The GLCM algorithm is used to extract features from photographs. The photos are then categorized using various machine learning methods, including SVM, RBF, ANN, and AdaBoost. The performance of the SVM RBF algorithm is superior when it comes to the classification and detection of brain tumors.

Evaluation of Acute Symptomatic Seizures and Etiological Factors in a Tertiary Care Hospital From a Developing Country.

INTRODUCTION: The etiologies of acute symptomatic seizures (ASS) differ across the globe. We aimed to evaluate the etiological spectrum of acute seizures and to observe the pattern of seizure types among study participants. METHODOLOGY: We conducted this prospective study from 2016 to 18. We included all patients aged 20 years or older, presenting with ASS. We excluded those with pseudoseizures. We performed appropriate descriptive analyses to describe the demographic details, etiology of ASS, and pattern of ASS. RESULTS: One hundred and thirty-eight patients were enrolled, constituting about 0.8% of total hospital admissions. The mean age at presentation was 44.33 ± 17.73 years. The most common etiologies for ASS were cerebrovascular accidents (CVA - 32.6%), neuroinfections (26.8%), metabolic derangements (13%), alcohol withdrawal (10.9%), and intracranial tumors (4.3%). 71% of the patients presented with only a single episode of ASS. The predominant type of seizure was generalized tonic-clonic seizures, seen in 70.2% of all patients, followed by focal with the bilateral tonic-clonic type (15.9%) and focal seizures (10.1%). New-onset seizures presenting as status epilepticus were observed in 3.6%. DISCUSSION: CVA and neuroinfections were the most common causes of ASS in our study, highlighting the need for community awareness of these conditions and the need to seek rapid care. The majority of our patients had only a single episode of seizures, with generalized seizures being the most common type, followed by focal onset seizures.

Integrating bio medical sensors in detecting hidden signatures of COVID-19 with Artificial intelligence.

Today COVID-19 pandemic articulates high stress on clinical resources around the world. At present, physical and viral tests are slowly emerging, and there is a need for robust pandemic detection that biomedical sensors can aid. The utility of biomedical sensors is correlated with the medical instruments with physiological metrics. These Biomedical sensors are integrated with the systematic device to track the target analytes with a biomedical component. The COVID-19 patients' samples are collected, and biomarkers are detected using four sensors: blood pressure sensor, G-FET based biosensor, electrochemical sensor, and potentiometric sensor with different quantifiable measures. The imputed data is then profiled with chest X-ray images from the Covid-19 patients.Multi-Layer Perceptron (MLP), an AI model, is deployed to identify the hidden signatures with biomarkers. The performance of the biosensor is measured with three parameters such as sensitivity, specificity and detection limit by generating the calibration plots that accurately fits the model.

Perspectives into the possible effects of the B.1.1.7 variant of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) on spermatogenesis.

B.1.1.7 is a recently discovered variant of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) associated with increased transmissibility. Recent findings indicate that this variant has a propensity to infect adolescents and children at higher rates than adults. The virus gains entry into various body cells utilizing angiotensin-converting enzyme 2 (ACE-2) and basigin (CD147) as receptors. The virus mainly affects type II pneumocytes of lungs, endothelial cells, enterocytes, and renal tubular cells. It is reported to affect testes, causing testicular pain, and producing histopathological changes, as observed in some autopsies. The B.1.1.7 variant can also affect various cells in the testes. This raises a major concern regarding the long-term effects of the viral infection on spermatogenesis and highlights the pressing need for a robust database of serum samples from infected male children.

Hospital-Based Contact Tracing of Patients With COVID-19 and Health Care Workers During the COVID-19 Pandemic in Eastern India: Cross-sectional Study.

BACKGROUND: The contact tracing and subsequent quarantining of health care workers (HCWs) are essential to minimizing the further transmission of SARS-CoV-2 infection and mitigating the shortage of HCWs during the COVID-19 pandemic situation. OBJECTIVE: This study aimed to assess the yield of contact tracing for COVID-19 cases and the risk stratification of HCWs who are exposed to these cases. METHODS: This was an analysis of routine data that were collected for the contact tracing of COVID-19 cases at the All India Institute of Medical Sciences, Bhubaneswar, in Odisha, India. Data from March 19 to August 31, 2020, were considered for this study. COVID-19 cases were admitted patients, outpatients, or HCWs in the hospital. HCWs who were exposed to COVID-19 cases were categorized, per the risk stratification guidelines, as high-risk contacts or low-risk contacts. RESULTS: During contact tracing, 3411 HCWs were identified as those who were exposed to 360 COVID-19 cases. Of these 360 cases, 269 (74.7%) were either admitted patients or outpatients, and 91 (25.3%) were HCWs. After the risk stratification of the 3411 HCWs, 890 (26.1%) were categorized as high-risk contacts, and 2521 (73.9%) were categorized as low-risk contacts. The COVID-19 test positivity rates of high-risk contacts and low-risk contacts were 3.8% (34/890) and 1.9% (48/2521), respectively. The average number of high-risk contacts was significantly higher when the COVID-19 case was an admitted patient (number of contacts: mean 6.6) rather than when the COVID-19 case was an HCW (number of contacts: mean 4.0) or outpatient (number of contacts: mean 0.2; P=.009). Similarly, the average number of high-risk contacts was higher when the COVID-19 case was admitted in a non-COVID-19 area (number of contacts: mean 15.8) rather than when such cases were admitted in a COVID-19 area (number of contacts: mean 0.27; P<.001). There was a significant decline in the mean number of high-risk contacts over the study period (P=.003). CONCLUSIONS: Contact tracing and risk stratification were effective and helped to reduce the number of HCWs requiring quarantine. There was also a decline in the number of high-risk contacts during the study period. This indicates the role of the implementation of hospital-based, COVID-19-related infection control strategies. The contact tracing and risk stratification approaches that were designed in this study can also be implemented in other health care settings.

Genomic sequence analysis of lung infections using artificial intelligence technique.

Attributable to the modernization of Artificial Intelligence (AI) procedures in healthcare services, various developments including Support Vector Machine (SVM), and profound learning. For example, Convolutional Neural systems (CNN) have prevalently engaged in a significant job of various classificational investigation in lung malignant growth, and different infections. In this paper, Parallel based SVM (P-SVM) and IoT has been utilized to examine the ideal order of lung infections caused by genomic sequence. The proposed method develops a new methodology to locate the ideal characterization of lung sicknesses and determine its growth in its early stages, to control the growth and prevent lung sickness. Further, in the investigation, the P-SVM calculation has been created for arranging high-dimensional distinctive lung ailment datasets. The data used in the assessment has been fetched from real-time data through cloud and IoT. The acquired outcome demonstrates that the developed P-SVM calculation has 83% higher accuracy and 88% precision in characterization with ideal informational collections when contrasted with other learning methods.

Hospital based contact tracing of COVID-19 patients and health care workers and risk stratification of exposed health care workers during the COVID-19 Pandemic in Eastern India

IntroductionContact tracing and subsequent quarantining of Health Care Workers (HCWs) is essential to minimize further transmission of COVID-19 infection. In this study, we have reported the yield of Contact Tracing of COVID-19 Patients and HCWs and risk stratification of exposed HCWs. MethodologyThis is a secondary analysis of routine data collected for contact tracing from 19th March to 31st August 2020 at All India Institute of Medical Sciences, Bhubaneswar, Odisha, India. HCWs exposed to COVID-19 infections were categorized as per the risk stratification guidelines and the high-risk contacts were quarantined for 14 days and tested on 7th day from last day of exposure. The low risk contacts were encouraged to closely monitor their symptoms while continuing to work. ResultsOut of 3411 HCWs exposed to COVID 19 patients (n=269) and HCWs (n=91), 890 (26.1%) were high risk contacts and 2521 (73.9%) were low risk contacts. The test positivity rate of high-risk contact was 3.82% and for low risk contact was 1.90%. Average number of high-risk contacts was significantly higher; for admitted patients (6.6) as compared to HCWs (4.0) and outpatients (0.2), p value = 0.009; for patients admitted in non-COVID areas (15.8) as compared to COVID areas (0.27), p value < 0.001; and when clustering of cases was present (14.3) as compared to isolated cases (8.2); p value < 0.001. Trend analysis (15 days block period) showed a significant decline in number of mean numbers of high-risk contacts during the study period. ConclusionContact tracing and risk stratification was effective and helped in reducing the number of HCWs going for quarantine. There was also a decline in high-risk contacts during study period suggesting role of implementation of hospital based COVID related infection control strategies. This contact tracing and risk stratification approach designed in the current study can also be implemented in other healthcare settings.

HANN: A Hybrid Model for Liver Syndrome Classification by Feature Assortment Optimization.

Early detection of any sort of disease is mandatory for effective medical treatment. Medical diagnosis relies heavily on Data Mining for automated disease classification and detection. It relies on data mining algorithms to examine medical data. Liver diseases have become more common these days with many new patients being diagnosed with Heptasis B and C. Early diagnosis of Liver Disorder is essential for treatment. It can be achieved by setting up intelligent systems for early diagnose and prognosis of Liver diseases. The existing automated classification systems lack accuracy in results when compared to surgical biopsy. We propose a new hybrid model for liver syndrome classification for analysis of the patient's medical data via hybrid artificial neural network. The medical records are classified based on the possibility of existence of disease. The proposed method uses M-PSO for feature selection of input variables and M-ANN algorithm for disease classification. The presented hybrid approach significantly improves the accuracy compared to existing classification algorithms. The results of the algorithm were examined and evaluated using Spark tool in this work.

Examining predictors of help-seeking behaviours in patients with mood and anxiety symptoms.

Relatively little is known about individual characteristics that factor into the decision to seek help for mood and anxiety symptoms. The current study was undertaken in order to examine factors that predict the likelihood of seeking help for mood and anxiety symptoms amongst a clinical population. Patients (N = 278) referred to a tertiary care clinic in Toronto, Canada were asked about their help-seeking behaviours (HSB) through initial intake assessments and self-administered questionnaires, including the Beck Anxiety Inventory, Anxiety Sensitivity Index, Intolerance of Uncertainty Scale, and Beck Depression Inventory-II. Correlates of anxiety and depression were examined to determine whether they could predict HSB amongst individuals with Generalized Anxiety Disorder with or without comorbid Major Depressive Disorder, as well as Panic Disorder and Social Anxiety Disorder. Psychiatric diagnoses were then examined to determine whether comorbidity and demographic factors impacted HSB. Results indicated that there were significant differences in anxiety and depression correlating mainly with anxiety sensitivity, as a predictor of HSB, and that there is a complex relationship between disorder type and demographic variables. The implications of these findings and suggested targeted interventions are discussed.

Catalase and sodium fluoride mediated rehabilitation of enamel bleached with 37% hydrogen peroxide.

BACKGROUND: Bleaching agents bring about a range of unwanted changes in the physical structure of enamel which needs to be restored qualitatively and timely. Catalase being an antioxidant ensures the effective removal of free radicals and improvement in fluoride mediated remineralization from the enamel microstructure which if retained may harm the integrity and affect the hardness of enamel. MATERIALS AND METHODS: Thirty freshly extracted incisors were sectioned to 6 slabs which were divided into 5 groups: Group A, control; Group B, treatment with 37% hydrogen peroxide (HP); Group C, treatment with 37% HP and catalase, Group D, treatment with 37% HP and 5% sodium fluoride application, Group E, treatment with 37% HP followed by catalase and 5% sodium fluoride. Scanning electron microscope and microhardness analysis were done for all slabs. One-way ANOVA test was applied among different groups. RESULTS: Vicker's microhardness number (VHN) of Group B and C was significantly lower. No significant difference between VHN of Group B and C. VHN of Group D was significantly higher than Group A, B, and C; but significantly lower than Group E. VHN of Group E was significantly higher than any other experimental group. One-way ANOVA revealed a highly significant P value (P = 0.0001) and so Tukey's post-hoc Test for the group comparisons was employed. CONCLUSION: Subsequent treatment of bleached enamel with catalase and fluoride varnish separately results in repairing and significantly increasing the microhardness.

Effect of Catalase and Sodium Fluoride on Human Enamel bleached with 35% Carbamide Peroxide.

AIM: To evaluate the effects of postbleaching antioxidant application fluoridation treatment on the surface morphology and microhardness of human enamel. MATERIALS AND METHODS: Ten freshly extracted human maxillary central incisors were cut at cementoenamel junction. Crown portion was sectioned into six slabs which were divided into five groups: group A - untreated controls; group B - 35% carbamide peroxide (CP); group C - 35% CP and catalase; group D - treatment with 35% CP and 5% sodium fluoride; group E - 35% CP, catalase and 5% sodium fluoride. Thirty-five percent carbamide peroxide application included two applications of 30 minutes each at a 5-day interval. After treatment, the slabs were thoroughly washed with water for 10 seconds and stored in artificial saliva at 37°C until the next treatment. Two percent sodium fluoride included application for 5 minutes. Three catalase included application for 3 minutes. RESULTS: After 5 days, groups B and C showed significantly decreased enamel microhardness compared to control. Group D specimens showed relatively less reduction in enamel micro-hardness than group C specimens. There is a marked increase in enamel microhardness in group E specimens. CONCLUSIONS: Fluoride take up was comparatively enhanced after catalase application resulting in less demineralization and increased microhardness. How to cite this article: Thakur R, Shigli AL, Sharma DS, Thakur G. Effect of Catalase and Sodium Fluoride on Human Enamel bleached with 35% Carbamide Peroxide. Int J Clin Pediatr Dent 2015;8(1):12-17.

Juvenile obsessive compulsive disorder in a paediatric dentistry set-up.

Obsessive-compulsive disorder (OCD) is an anxiety disorder comprising uncontrollable thought processes and repetitive, ritualised behaviours that one feels compelled to perform. If an individual has OCD, he/she probably realises that his/her obsessive thoughts and compulsive behaviours are irrational but would still feel unable to resist them. Since a pedodontist's association with the child patient and parents is established at quite an early age, they should make good use of the opportunity to diagnose psychological disorders in child patients as well as adolescents. Prompt diagnosis in such cases would enable timely medical intervention and hence help in achieving a more cooperative dental patient to ensure instillation of a positive dental attitude. This endeavour highlights a case of a 10-year-old boy who had reported to a private dental set-up with dental problems and was concurrently diagnosed for OCD.

Phenytoin-induced cerebellar atrophy in an epileptic boy.

Epilepsy is an important health problem due to its high prevalence and potential for causing long-term morbidity. It is commonly treated in children with phenytoin sodium. It has wide pharmacokinetic variability and a narrow therapeutic range that leads to toxicity. Here, we report a case of phenytoin-induced cerebellar atrophy in a 16-year-old epileptic boy who presented to the hospital with a viral infection.

Repositioning a laterally luxated maxillary central incisor with a vacuum-formed splint: a case report.

Splinting is the standard care for stabilizing replanted or repositioned permanent teeth following trauma. Traditionally, semirigid splints are used to stabilize a repositioned tooth, with support provided by adjacent teeth. However, in cases when the adjacent teeth are wide and/or can only provide minimal anchorage and support, it is difficult to fix the luxated tooth to its original position. This case report describes how a clear vacuum-formed splint was used to reposition and stabilize an avulsed maxillary left central incisor. Due to the avulsion, an unerupted canine, and partially erupted premolars, it was difficult to fabricate a traditional splint, hence the creation of the vacuum-formed splint.

Percutaneous ultrasound-guided fine-needle aspiration of portal vein thrombi as a diagnostic and staging technique for hepatocellular carcinoma.

BACKGROUND: Detection of portal vein tumor invasion in hepatocellular carcinoma (HCC) is important in determining therapy and prognosis. Patients with portal vein thrombus (PVT) due to tumor are considered to have advanced disease and are only offered palliative therapy. Therefore, every possible attempt should be made to accurately differentiate benign from malignant PVT. METHODS: In this study, 20 patients presenting to the out-patient department with a PVT and a diagnosis/diagnostic suspicion of HCC were subjected to FNAC of PVT. Clinical, cytological, and histopathological data for these patients were analyzed. RESULTS: The patients had a median age of 58 years, with majority being cirrhotic (80%) and males (80%). Thirteen patients had a prior radiological diagnosis of HCC at the time of FNAC. In three patients without any mass on imaging, FNAC made the initial diagnosis and staged the disease simultaneously. 50% of the thrombi were limited to 1st-order portal vein branches (vp3). Sixteen of the aspirates were positive for malignancy with 50% of the tumors being moderately differentiated. On histologic follow-up, three of the patients with negative aspirates had bland thrombi in their portal veins. No complications resulted from the procedure. CONCLUSIONS: FNAC of PVT is a simple, safe, effective, well-tolerated, and economical method for staging of patients with HCC. When used as the initial diagnostic procedure, in selected patients, it can provide the diagnosis and staging information simultaneously.

LED fluorescence microscopy increases the detection of smear-positive pulmonary tuberculosis in medical colleges of India.

In July 2012, light-emitting diode fluorescence microscopy (LED-FM) replaced conventional light microscopy using Ziehl-Neelsen stain in the detection of sputum-positive pulmonary tuberculosis in 190 microscopy centres of medical colleges operating under India's Revised National Tuberculosis Control Programme. We compared the performance of LED-FM (July-December 2012) to that of conventional microscopy (July-December 2011) across 190 sites. Of 222 658 patients examined using conventional microscopy, 28 042 (12.6%) were smear-positive, while of 224 714 examined using LED-FM, 33 552 (14.9%) were smear-positive, an additional yield of 5251 cases after adjusting for the increase in patients examined. We recommend replacing conventional microscopy with LED-FM in high workload microscopy centres in India.

Dans 190 centres de microscopie des collèges médicaux travaillant dans le Programme Révisé de Lutte contre la Tuberculose en Inde, l'examen microscopique par fluorescence à diode émettrice de lumière (LED-FM) a remplacé l'examen microscopique à lumière conventionnelle utilisant la coloration de Ziehl-Neelsen pour la détection de la tuberculose pulmonaire à frottis positif. Nous avons comparé les performances du LED-FM entre juillet et décembre 2012 à celles de l'examen microscopique conventionnel (juillet­décembre 2011) dans l'ensemble des 190 sites. Les frottis ont été positifs chez 28 042 (12.6%) des 222 658 patients examinés par microscopie conventionnelle comparé à 33 552 (14.9%) des 224 714 patients examinés par LED-FM, un rendement supplémentaire de 5251 cas après ajustement pour l'augmentation du nombre de patients examinés. Nous recommandons le remplacement de l'examen microscopique conventionnel par le LED-FM en Inde dans les centres de microscopie dont la charge de travail est élevée.

En julio del 2012 la microscopia de fluorescencia con diodos electroluminescentes (LED-FM) reemplazó el sistema tradicional de microscopia de luz con tinción de Ziehl-Neelsen en la detección de casos de tuberculosis pulmonar con baciloscopia positiva en 190 centros de microscopia de las facultadas de medicina que funcionan en conformidad con el Programa Nacional Revisado de Control de la Tuberculosis de la India. En el presente artículo se comparó el rendimiento diagnóstico de este nuevo sistema de microscopia (entre julio y diciembre del 2012) con el rendimiento de la microscopia de luz (entre julio y diciembre del 2011) en 190 centros. De los 222 658 pacientes investigados mediante la microscopia de luz, 28 042 (12,6 %) presentaron baciloscopia positiva. De los 224 714 pacientes cuyas muestras se examinaron mediante la LED-FM, 33 552 (14,9 %) obtuvieron una baciloscopia positiva, lo cual corresponde a un incremento del rendimiento de 5251 casos, después de corregir con respecto al aumento del número de pacientes examinados. Se recomienda sustituir el sistema tradicional de microscopia de luz por la LED-FM en los centros con un alto volumen de trabajo en la India.