Integration of graph network with kernel SVM and logistic regression for identification of biomarkers in SCA12 and its diagnosis.

Spinocerebellar ataxia type 12 is a hereditary and neurodegenerative illness commonly found in India. However, there is no established noninvasive automatic diagnostic system for its diagnosis and identification of imaging biomarkers. This work proposes a novel four-phase machine learning-based diagnostic framework to find spinocerebellar ataxia type 12 disease-specific atrophic-brain regions and distinguish spinocerebellar ataxia type 12 from healthy using a real structural magnetic resonance imaging dataset. Firstly, each brain region is represented in terms of statistics of coefficients obtained using 3D-discrete wavelet transform. Secondly, a set of relevant regions are selected using a graph network-based method. Thirdly, a kernel support vector machine is used to capture nonlinear relationships among the voxels of a brain region. Finally, the linear relationship among the brain regions is captured to build a decision model to distinguish spinocerebellar ataxia type 12 from healthy by using the regularized logistic regression method. A classification accuracy of 95% and a harmonic mean of precision and recall, i.e. F1-score of 94.92%, is achieved. The proposed framework provides relevant regions responsible for the atrophy. The importance of each region is captured using Shapley Additive exPlanations values. We also performed a statistical analysis to find volumetric changes in spinocerebellar ataxia type 12 group compared to healthy. The promising result of the proposed framework shows that clinicians can use it for early and timely diagnosis of spinocerebellar ataxia type 12.

Workplace Violence in Healthcare Settings: A Cross-Sectional Survey among Healthcare Workers of North India.

Background: Workplace violence (WPV) is a significant problem in both developed and developing countries, especially among healthcare workers. It has widespread implications for their overall health and well-being. Objective: The study was conducted to assess the problem of violence among doctors and other healthcare workers in healthcare settings. Material and Methods: A cross-sectional survey was conducted using a validated questionnaire from August 21 to September 18, 2021, based on purposive and snowball-sampling techniques for data collection. Appropriate statistical methods were applied to study the association between sociodemographics and characteristics of violence. Results: A total of 601 responses were analyzed. The results showed that approximately 75% of the participants experienced violence in some form at their workplace. These episodes lead to a significant impact on the physical and mental health of these workers. Around one-third of the participants felt uncomfortable reporting these incidents. Some of the most common risk factors and mitigation strategies were also reported by the participants. Conclusion: The findings of this study can be used by the legislators, administrators, and policymakers to develop strategies that can help in mitigating these episodes of violence for the better functioning of the healthcare system.

Study of 2D Feature Extraction Techniques for Classification of Spinocerebellar Ataxia Type 12 (SCA12).

Spinocerebellar ataxia type 12 (SCA12) is a neurodegenerative genetic disorder triggered by abnormal CAG repeat expansion at locus 5q32. MRI recognises dissimilarities in affected areas of SCA12 patients and healthy subjects. But manual diagnosis is time-consuming and prone to subjective errors. This has motivated us in developing a systematic and authentic decision model for computer-aided diagnosis (CAD) of SCA12. Four different feature extraction techniques are examined in this research work, such as First Order Statistics, GLRLM, GLCM, and GLGCM, to obtain distinguishable characteristics for differentiating SCA12 patients from healthy subjects. The model's performance is measured using sensitivity, specificity, accuracy and F1-score with leave-one-out cross-validation scheme. Our experimental results show that features based on the GLRLM can distinguish SCA12 from healthy subjects with a maximum classification accuracy of 85% among all the different function extraction techniques used.

A combination of 3-D discrete wavelet transform and 3-D local binary pattern for classification of mild cognitive impairment.

BACKGROUND: The detection of Alzheimer's Disease (AD) in its formative stages, especially in Mild Cognitive Impairments (MCI), has the potential of helping the clinicians in understanding the condition. The literature review shows that the classification of MCI-converts and MCI-non-converts has not been explored profusely and the maximum classification accuracy reported is rather low. Thus, this paper proposes a Machine Learning approach for classifying patients of MCI into two groups one who converted to AD and the others who are not diagnosed with any signs of AD. The proposed algorithm is also used to distinguish MCI patients from controls (CN). This work uses the Structural Magnetic Resonance Imaging data. METHODS: This work proposes a 3-D variant of Local Binary Pattern (LBP), called LBP-20 for extracting features. The method has been compared with 3D-Discrete Wavelet Transform (3D-DWT). Subsequently, a combination of 3D-DWT and LBP-20 has been used for extracting features. The relevant features are selected using the Fisher Discriminant Ratio (FDR) and finally the classification has been carried out using the Support Vector Machine. RESULTS: The combination of 3D-DWT with LBP-20 results in a maximum accuracy of 88.77. Similarly, the proposed combination of methods is also applied to distinguish MCI from CN. The proposed method results in the classification accuracy of 90.31 in this data. CONCLUSION: The proposed combination is able to extract relevant distribution of microstructures from each component, obtained with the use of DWT and thereby improving the classification accuracy. Moreover, the number of features used for classification is significantly less as compared to those obtained by 3D-DWT. The performance of the proposed method is measured in terms of accuracy, specificity and sensitivity and is found superior in comparison to the existing methods. Thus, the proposed method may contribute to effective diagnosis of MCI and may prove advantageous in clinical settings.

Reduction in cisplatin genotoxicity (micronucleus formation) in non target cells of mice by protransfersome gel formulation used for management of cutaneous squamous cell carcinoma.

Cisplatin-loaded protransfersome system was prepared and characterized for in vitro drug permeation, drug deposition and antitumor effect. A histopathological study and a genotoxicity study were also done. The skin permeation data of cisplatin from protransfersome gel formulation revealed 494.33 ± 11.87 µg cm-2, which was significantly higher than that from the control plain drug solution in 0.9 % NaCl (p < 0.001). Untreated group of animals showed invasive moderately differentiated keratinizing squamous cell carcinoma (malignant stage). However, with cisplatin loaded protransfersome gel system simple epithelial hyperplasia (pre-cancerous stage) with no cancerous growth was observed. Also, a significant induction in micronucleus formation was found in the group that was treated with injectable intraperitoneal cisplatin preparation in 0.9 % saline as compared to the group treated with topical protransfersome gel formulation. The findings of this research work appear to support improved, site-specific and localized drug action in the skin, thus providing a better option for dealing with skin related problems like squamous cell carcinoma.

Detection of superior sagittal sinus thrombosis on FDG PET.

A 51-year-old woman with infiltrating ductal carcinoma of left breast, who postsurgery, chemotherapy, and external radiotherapy presented with recurrence in the right chest wall a year after completion of treatment. An excision biopsy of the right chest wall nodule showed deposits of infiltrating ductal carcinoma. She also complained of excruciating headache. A FDG PET scan was done an hour after intravenous administration of 444 MBq (12 mCi) of F-18 FDG, on a dedicated PET scanner. The scan showed multiple hypermetabolic sites in the mediastinum, liver, spleen, abdomen, right scapula, and right ilium. In addition to all this, a triangular area of uptake was seen along the falx in the region of the superior sagittal sinus. A CT scan confirmed a superior sagittal sinus thrombosis. On contrast-enhanced CT scan an "empty delta sign" was observed in the superior sagittal sinus from enhancement of the dural leaves surrounding the comparatively less dense thrombosed sinus. This sign is highly diagnostic for sagittal sinus thrombosis.

Unsuspected second malignancy detection by FDG PET scan.

A whole body F-18 FDG PET scan was done on a 45-year-old man with a small cell carcinoma of the left lung for a metastatic survey. Imaging showed intense uptake in the left lung (maximum standardized uptake value (SUVmax) of 8.32 corrected for body weight), at the site of the primary and in the hilar lymph nodes. Focal intense uptake was also seen in the rectum (SUVmax of 21.73 corrected for body weight). No anatomic imaging for the pelvis was done, as the patient had no bowel symptoms. Posttreatment PET scan done 9 months after the first scan showed significant reduction in the primary mass in the lung (SUVmax 4.45) but an increase in the rectal mass (SUVmax 83.22). He now complained of bleeding per rectum. Colonoscopy and CT scan of the abdomen showed a mass in the rectum, which on biopsy revealed invasive adenocarcinoma.