Functional and radiological outcome of proximal femoral nailing versus dynamic hip screw in unstable intertrochanteric femur fractures

Background: Intertrochanteric femur fractures account half of the hip fractures in elderly, the other majority being neck of femur fracture. 35-40% of intertrochanteric are unstable (Tronzo’s classification type 3, 4 and 5). The dynamic hip screw (DHS) has achieved widespread acclaim in the last few years and is currently considered to be the standard device for outcome assessment. Though, the DHS has been shown to produce good results, but complications are frequent, particularly in unstable inter-trochanteric fracture. The advantage of Proximal Femur Nailing fixation is that it provides a more biomechanically stable construct by reducing the distance between hip joint and implant. The goal of this study is to assess the clinical and radiographical outcomes of the DHS (load bearing implant) and PFN (load sharing implant) for the treatment of Intertrochanteric hip fractures.Methods: We assessed the same in 52 cases of unstable femur fracture 26 operated with DHS and 26 with PFN and followed up with sequential radiographs for radiological union and sequential interview with Harris hip score calculation for functional outcome assessment.Results: Patients operated for unstable intertrochanteric femur fracture with Proximal femoral nailing had better Harris hip scores (excellent 4, good 14) compared to dynamic hip screw group (Excellent 6, good 5) and earlier weight bearing (At 18 weeks, 100% in PFN compared to 65.5% in DHS). PFN has lesser incidence of postoperative complications (15% in PFN compared to 38% in DHS).Conclusions:The proximal femoral nail has better functional outcome in terms of Harris hip score and early radiologic union in unstable intertrochanteric fractures of femur. 

Gender differences in work stressors and psychiatric morbidity at workplace in doctors and nurses

Background: Work environment is one of the key factors responsible for stress in human life. Different aspects of working conditions affect person’s work. Work stress in turn leads to psychiatric problems. Doctors and nurses are prone to be affected by work stressors as their work includes more human interactions.Methods: It is a cross sectional observational study conducted at a Medical College affiliated General Hospital. Total 400 subjects were surveyed for this study including 200 doctors and 200 nurses with equal gender distribution. Subjects were interviewed in groups and asked to fill up self-rated questionnaires during the interview. The demographic details were collected through a specially designed semi-structured proforma with workplace stressors checklist, presence of past or present psychiatric illness and presence of enduring stress other than workplace stress. Psychiatric morbidity was assessed by DASS and GHQ-28.Results: In males lack of staff or resources and in females disputes with staff or seniors are important stressors at workplace while long working hours and work responsibilities on holiday were considered as the least stressful. 9.45%, 20.2% and 29.2% having self-rated depression, stress and anxiety respectively in DASS. No significant difference found in gender as well as class as regards of psychiatric morbidity. Past or present psychiatric illness and stress other than work place are significantly associated with GHQ 28 Case-ness. 3.5% females were having suicidal ideas (GHQ item D7) as compared to 0.5% males.Conclusions: Measures for improving working conditions and environment, adequate resources, clarifying roles, constructive resolution of conflicts and stress management training are required to increase work productivity.

Machine-Learning Techniques for Predicting Phishing Attacks in Blockchain Networks: A Comparative Study

Security in the blockchain has become a topic of concern because of the recent developments in the field. One of the most common cyberattacks is the so-called phishing attack, wherein the attacker tricks the miner into adding a malicious block to the chain under genuine conditions to avoid detection and potentially destroy the entire blockchain. The current attempts at detection include the consensus protocol; however, it fails when a genuine miner tries to add a new block to the blockchain. Zero-trust policies have started making the rounds in the field as they ensure the complete detection of phishing attempts; however, they are still in the process of deployment, which may take a significant amount of time. A more accurate measure of phishing detection involves machine-learning models that use specific features to automate the entire process of classifying an attempt as either a phishing attempt or a safe attempt. This paper highlights several models that may give safe results and help eradicate blockchain phishing attempts

Assessment of ophthalmic morbidities in school children (6-14 years) in rural community

Background: School children are affected by various eye disorders like refractive errors, squint, Vitamin A deficiency and eye infections. Most of the ophthalmic morbidities originate in childhood and if they are not diagnosed early they may result in severe disabilities and affects children’s performance in the school. Objectives: To find out prevalence of various ophthalmic morbidities in the school children (6 – 14 years) and to study the socio demographic profile of school children (6 – 14 years) related to ophthalmic morbidities. Materials and Methods: A cross-sectional study was carried out on 500 school children in schools of Aliabada village, Jamnagar district. A Pretested semi-structured proforma was used to collect the information related to study and students were screened for eye disorders by visual acuity testing using snellen’s chart and colour blindness was checked by using Ishihara’s chart and torch light examination. Visual acuity was assessed. Data entry and analysis was done using Microsoft excel 2007 and Epi info. Results: The prevalence of ophthalmic morbidity was found to be 33%. Among the various ophthalmic morbidities, major were refractive errors i.e. 25.6% followed by Vit A deficiency in 8.6% cases and followed by squint 2.4% and various others. The study also found that social class and age were associated with ophthalmic morbidity. It is found statistically significant. Conclusions: Refractive error was the most common ophthalmic morbidity in the school children followed by Vit A deficiency, squint and various others. In the study we found that social class and age were significantly associated with ophthalmic morbidity

Volumetric CT-based segmentation of NSCLC using 3D-Slicer

Accurate volumetric assessment in non-small cell lung cancer (NSCLC) is critical for adequately informing treatments. In this study we assessed the clinical relevance of a semiautomatic computed tomography (CT)-based segmentation method using the competitive region-growing based algorithm, implemented in the free and public available 3D-Slicer software platform. We compared the 3D-Slicer segmented volumes by three independent observers, who segmented the primary tumour of 20 NSCLC patients twice, to manual slice-by-slice delineations of five physicians. Furthermore, we compared all tumour contours to the macroscopic diameter of the tumour in pathology, considered as the “gold standard”. The 3D-Slicer segmented volumes demonstrated high agreement (overlap fractions > 0.90), lower volume variability (p = 0.0003) and smaller uncertainty areas (p = 0.0002), compared to manual slice-by-slice delineations. Furthermore, 3D-Slicer segmentations showed a strong correlation to pathology (r = 0.89, 95%CI, 0.81–0.94). Our results show that semiautomatic 3D-Slicer segmentations can be used for accurate contouring and are more stable than manual delineations. Therefore, 3D-Slicer can be employed as a starting point for treatment decisions or for high-throughput data mining research, such as Radiomics, where manual delineating often represent a time-consuming bottleneck

Robust Radiomics Feature Quantification Using Semiautomatic Volumetric Segmentation

Due to advances in the acquisition and analysis of medical imaging, it is currently possible to quantify the tumor phenotype. The emerging field of Radiomics addresses this issue by converting medical images into minable data by extracting a large number of quantitative imaging features. One of the main challenges of Radiomics is tumor segmentation. Where manual delineation is time consuming and prone to inter-observer variability, it has been shown that semi-automated approaches are fast and reduce inter-observer variability. In this study, a semiautomatic region growing volumetric segmentation algorithm, implemented in the free and publicly available 3D-Slicer platform, was investigated in terms of its robustness for quantitative imaging feature extraction. Fifty-six 3D-radiomic features, quantifying phenotypic differences based on tumor intensity, shape and texture, were extracted from the computed tomography images of twenty lung cancer patients. These radiomic features were derived from the 3D-tumor volumes defined by three independent observers twice using 3D-Slicer, and compared to manual slice-by-slice delineations of five independent physicians in terms of intra-class correlation coefficient (ICC) and feature range. Radiomic features extracted from 3D-Slicer segmentations had significantly higher reproducibility (ICC = 0.85±0.15, p = 0.0009) compared to the features extracted from the manual segmentations (ICC = 0.77±0.17). Furthermore, we found that features extracted from 3D-Slicer segmentations were more robust, as the range was significantly smaller across observers (p = 3.819e-07), and overlapping with the feature ranges extracted from manual contouring (boundary lower: p = 0.007, higher: p = 5.863e-06). Our results show that 3D-Slicer segmented tumor volumes provide a better alternative to the manual delineation for feature quantification, as they yield more reproducible imaging descriptors. Therefore, 3D-Slicer can be employed for quantitative image feature extraction and image data mining research in large patient cohorts

Associations between radiologist-defined semantic and automatically computed radiomic features in non-small cell lung cancer

Tumor phenotypes captured in computed tomography (CT) images can be described qualitatively and quantitatively using radiologist-defined “semantic” and computer-derived “radiomic” features, respectively. While both types of features have shown to be promising predictors of prognosis, the association between these groups of features remains unclear. We investigated the associations between semantic and radiomic features in CT images of 258 non-small cell lung adenocarcinomas. The tumor imaging phenotypes were described using 9 qualitative semantic features that were scored by radiologists, and 57 quantitative radiomic features that were automatically calculated using mathematical algorithms. Of the 9 semantic features, 3 were rated on a binary scale (cavitation, air bronchogram, and calcification) and 6 were rated on a categorical scale (texture, border definition, contour, lobulation, spiculation, and concavity). 32–41 radiomic features were associated with the binary semantic features (AUC = 0.56–0.76). The relationship between all radiomic features and the categorical semantic features ranged from weak to moderate (|Spearmen’s correlation| = 0.002–0.65). There are associations between semantic and radiomic features, however the associations were not strong despite being significant. Our results indicate that radiomic features may capture distinct tumor phenotypes that fail to be perceived by naked eye that semantic features do not describe and vice versa

Evaluation & Comparison of Nicotine quantification in smokeless tobacco products

Introduction: Smokeless tobacco products (STPS) represent a significant health risk and have been associated with oral and pancreatic cancers, oral lesions, coronary artery and peripheral vascular disease and adverse pregnancy outcomes. So, the aim of the present study is to perform quantitative determination of nicotine, the main alkaloid of smokeless product (Gutkha) available in Vadodara, Gujarat. Method: Collection of sample was done from local tobacco selling shopkeeper from Vadodara i.e. vimal, RMD, pan vilas, rajnigandha and raag. All the samples included in the study the same products available everywhere in Gujarat. The quantification of nicotine was done by High Performance Thin Layer Chromatography (HPTLC) using the mobile phase toluene: ethyl acetate: Diethylamine (6:4:0.5). Spectrodensitometric measurement was carried out at absorption maximum 254 nm. Results:  Five different smokeless tobacco samples were estimated using HPTLC method. Nicotine content was found to be 2.45% in Vimal, 3.11% in RMD, 2.60%- Pan Vilas, 3.06%- Rajigandha,3.32%- Raag. Conclusion: A considerable variation of nicotine content was found among the five investigated smokeless tobacco product where sample raag revealed the highest amount of nicotine than the other samples. The nicotine concentration of commercially available chewing tobacco products was found to be much lower than that of the smoking form of tobacco, but the higher average daily consumption made it comparable to the smoking form