Axion Paradigm with Color-Mediated Neutrino Masses

We propose a generalized Kim-Shifman-Vainshtein-Zakharov-type axion framework in which colored fermions and scalars act as two-loop Majorana neutrino-mass mediators. The global Peccei-Quinn symmetry under which exotic fermions are charged solves the strong CP problem. Within our general proposal, various setups can be distinguished by probing the axion-to-photon coupling at helioscopes and haloscopes. We also comment on axion dark-matter production in the early Universe.

Human resources analytics: where do we go from here?

Purpose

Human resource analytics (HRA) has developed as a new business trend and challenge, stressing the strategic relevance of human resource management (HRM) to senior management executives. HRA is a process that uses statistical techniques, to link HR practices to organizational performance. The purpose of this study is to carry out recent development in HRA, bibliometric analysis and content analysis to present a comprehensive account of HRA to fill the gap in the evolution and status of its research.

Design/methodology/approach

The study is based on the recent advances in HRA in terms of it evolution and advancement by analyzing and drawing conclusions 480 articles retrieved from the Web of Science (WoS) database from 2003 to March 2022. The methodology is divided into four steps: data collection, analysis, visualization and interpretation. The study performed a rigorous bibliometric assessment of HRA using the bibliometric R-package and VOS viewer.

Findings

The findings based on the literature survey, and bibliometric analysis, reveal the path-breaking articles, the prominent authors, most contributing institutions and countries that have contributed to the HRA scholarship. The results show that the number of publications has significantly increased from 2015 onwards, reaching a maximum of 101 journals in 2021. The USA, China, India, Canada and the United Kingdom were the most productive countries in terms of the total number of publications. Human Resource Management Journal, Human Resource Management, International Journal of Manpower, and Journal of Organizational Effectiveness-People and Performance are the top four academic outlets in the field of HRA. Additionally, the study identifies four clusters of HRA research and the knowledge gaps in HRA scholarship.

Research limitations/implications

The present study is based on the articles retrieved from the WoS. The study underpins HRA research to understand the trends and presents a structured account. However, the study is not free from limitations. It is recommended that future research could be undertaken by combining WoS and Scopus databases to have a more detailed and comprehensive view. This study indicates that the field is still in its infancy stage. Hence, there is a need for more arduous research on the topic to help develop a better understanding of this field.

Originality/value

The findings of knowledge clusters will drive future researchers to augment the field. The evolution of the four clusters and their subsequent development will fill the gaps in the literature. This study enriches the HRA literature and the findings of this study may assist academicians, researchers and managers in furthering their research in the identified research clusters

Exploiting multimodal CNN architecture for automated teeth segmentation on dental panoramic X-ray images

Panoramic X-ray images are the major source used in field of dental image segmentation. However, such images suffers from the disturbances like low contrast, presence of jaw bones, nose bones, spinal bone, and artifacts. Thus, to observe these images manually is a tedious task, requires expertise of dentist and is time consuming. Hence, there is need to develop an automated tool for teeth segmentation. Recently, few deep models have been developed for dental image segmentation. But, such models possess large number of training parameters, thus making the segmentation a very complex task. Also, these models are based only on conventional CNN and lacks in exploiting multimodal CNN features for dental image segmentation. Thus, to address these issues, a novel encoder-decoder model based on multimodal-feature extraction for automatic segmentation of teeth area is proposed. The encoder has three different CNN based architectures: conventional CNN, atrous-CNN, and separable CNN to encode rich contextual information. Whereas decoder contains a single stream of deconvolutional layers for segmentation. The proposed model is tested on 1500 panoramic X-ray images and uses very less parameters when compared to state-of-the-art methods. Besides this, the precision and recall are 95.01% and 94.06%, which out performs the state-of-the art methods.

Impact Strength of Various Types of Acrylic Resin: An In Vitro Study

AIM

The aim of this study is to evaluate and compare the impact strength of conventional acrylic resin, high-impact acrylic resin, high-impact acrylic resin reinforced with silver nanoparticles, and high-impact acrylic resin reinforced with a zirconium oxide powder.

MATERIALS AND METHODS

A total of 60 samples were prepared of dimensions 60 mm length × 7 mm width × 4 mm thickness to test impact strength. Machined stainless steel dies of the same dimension were used to form molds for the fabrication of these samples. Of 60 samples, 15 samples were prepared each from conventional acrylic resin (Group A1), high-impact acrylic resin (Group A2), acrylic resin reinforced with silver nanoparticles (Group A3), and acrylic resin reinforced with zirconium oxide powder (Group A4). Izod-Charpy pendulum impact testing machine was used.

RESULTS

The impact strength of group A1 was in the range of 2.83-3.30 kJ/m² (M = 3.12 kJ/m², SD = 0.16), group A2 was in range of 5.10-5.78 kJ/m² (M = 5.51 kJ/m², SD = 0.18), group A3 was in range 3.18-3.56 kJ/m² (M = 3.37 kJ/m², SD = 0.11), and group A4 was in range 7.18-7.78 kJ/m² (M = 7.5 kJ/m², SD = 0.18). Statistical analysis using one-way ANOVA and t-test revealed significant differences (p < 0.001).

CONCLUSION

High-impact acrylic resin reinforced with zirconium oxide powder has the highest impact strength.

CLINICAL SIGNIFICANCE

This research sheds light on the usefulness of novel filler materials in clinical prosthodontics.

Enhancing teeth segmentation using multifusion deep neural net in panoramic X-ray images

BACKGROUND

Precise teeth segmentation from dental panoramic X-ray images is an important task in dental practice. However, several issues including poor image contrast, blurring borders of teeth, presence of jaw bones and other mouth elements, makes reading and examining such images a challenging and time-consuming task for dentists. Thus, developing a precise and automated segmentation technique is required.

OBJECTIVE

This study aims to develop and test a novel multi-fusion deep neural net consisting of encoder-decoder architecture for automatic and accurate teeth region segmentation from panoramic X-ray images.

METHODS

The encoder has two different streams based on CNN which include the conventional CNN stream and the Atrous net stream. Next, the fusion of features from these streams is done at each stage to encode the contextual rich information of teeth. A dual-type skip connection is then added between the encoder and decoder to minimise semantic information gaps. Last, the decoder comprises deconvolutional layers for reconstructing the segmented teeth map.

RESULTS

The assessment of the proposed model is performed on two different dental datasets consisting of 1,500 and 1,000 panoramic X-ray images, respectively. The new model yields accuracy of 97.0% and 97.7%, intersection over union (IoU) score of 91.1% and 90.2%, and dice coefficient score (DCS) of 92.4% and 90.7% for datasets 1 and 2, respectively.

CONCLUSION

Applying the proposed model to two datasets outperforms the recent state-of-the-art deep models with a relatively smaller number of parameters and higher accuracy, which demonstrates the potential of the new model to help dentists more accurately and efficiently diagnose dental diseases in future clinical practice.

Pentoxifylline - A Novel Adjunct in the Management of Patients with Oral Submucous Fibrosis in Terai Population of Nepal: A Prospective Case Control Study

Oral submucous fibrosis is a premalignant condition largely seen in the South-Asian countries mainly due to the consumption of areca nut. Pentoxifylline is a methylxanthine derivative, with vasodilating, anti-inflammatory and immune modulatory properties and is believed to increase the vascularity of the mucosal layer. The study was conducted to clinically assess the effectiveness of orally given pentoxifylline with dexamethasone (4mg/ml) given intralesionally and hyaluronidase 1500 IU with 0.5ml of 2.0% lignocaine plus multivitamins in the management of Oral submucous fibrosis patients pertaining to the Terai belt of Nepal. This study was conducted as an experimental study consisting of 70 oral submucous fibrosis patients who were divided blindly into two groups i.e. control or standard drug group (n=35) and experimental drug group (n=35). Standard drug group were given biweekly intralesional injections of dexamethasone (4mg/ml) and hyaluronidase 1500IU with 0.5ml of 2.0% lignocaine plus one capsule of multivitamins daily for a period of 12 weeks whereas experimental drug group were given pentoxifylline tablets 400mg 3 times daily for 12 weeks in addition to the drugs given to the standard drug group. Variables considered in the study were burning sensation, mouth opening, tongue protrusion and cheek flexibility using visual analog scale, vernier caliper, cheek retractor and a metric scale. On comparing, statistically significant results were seen in experimental drug group as far as reduction in burning sensation (p<0.001) and increase in mouth opening (p<0.001) was concerned. As far as improvement in tongue protrusion and cheek flexibility were concerned, the results were appreciating but statistically not significant (p=0.231) and (p=0.251) respectively. This study showed the effectiveness of pentoxifylline as an adjunct in the routine management of oral submucous fibrosis.

Perinatal Stroke in Fetuses, Preterm and Term Infants

Perinatal stroke is a well-defined heterogenous group of disorders involving a focal disruption of cerebral blood flow between 20 weeks gestation and 28 days of postnatal life. The most focused lifetime risk for stroke occurs during the first week after birth. The morbidity of perinatal stroke is high, as it is the most common cause of hemiparetic cerebral palsy which results in lifelong disability that becomes more apparent throughout childhood. Perinatal strokes can be classified by the timing of diagnosis (acute or retrospective), vessel involved (arterial or venous), and underlying cause (hemorrhagic or ischemic). Perinatal stroke has primarily been reported as a disorder of term infants; however, the preterm brain possesses different vulnerabilities that predispose an infant to stroke injury both in utero and after birth. Accurate diagnosis of perinatal stroke syndromes has important implications for investigations, management, and prognosis. The classification of perinatal stroke by age at presentation (fetal, preterm neonatal, term neonatal, and infancy/childhood) is summarized in this review, and includes detailed descriptions of risk factors, diagnosis, treatment, outcomes, controversies, and resources for family support.

Pre-treatment delay and out of pocket expenses by notified new tuberculosis patients in an Indian mega city

BACKGROUND

Study was carried out to find out delay from onset of symptoms and out of pocket expenditure (OOPE) until initiation of anti-TB treatment (ATT) by new Tuberculosis (TB) patients registered in public health facilities in Bengaluru.

METHODS

Notified patients (N = 228) selected purposively were interviewed at initiation of ATT regarding number and type of facilities visited and delay in initiating ATT. OOPE was elicited separately for in- and out-patient visits, towards consultation, purchase of medicines, diagnostic tests, transportation, hospitalization and food. Dissaving or money borrowed was ascertained.

RESULTS

Two-thirds of participants were 15-44 years of age and 56% were males, mean annual household income was $4357. About 75% first visited a private health facility; 68% and 87% respectively were diagnosed and started on ATT in public sector after visiting an average of three facilities and after a mean delay of 68 days; the median delay was 44 days. Of mean OOPE of $402, 54% was direct medical expenditure, 5% non-medical direct and 41% indirect. OOPE was higher for Extra-pulmonary TB compared to PTB and when number of health facilities visited before initiating treatment was >3 compared to those who visited ≤3 and when the time interval between onset of symptoms and treatment initiation (total delay) was >28 days compared to when this interval was ≤28 days. About 20% suffered catastrophic expenditure; 34% borrowed money and 37% sold assets.

CONCLUSION

Concerted efforts are needed to reduce delay and OOPE in pre-treatment period and social protection to account for indirect expenditure.

Smart manufacturing and sustainability: a bibliometric analysis

Purpose

During the past decade, the necessity to integrate manufacturing and sustainability has increased mainly to reduce the adverse effect on the manufacturing industry, transforming traditional manufacturing into smart manufacturing by adopting the latest manufacturing technology as part of the Industry 4.0 revolution. Smart manufacturing has piqued the interest of both academics and industry. Manufacturing is a foundation of products and services required for human health, safety, and well-being in modern society and from an organizational standpoint. This paper uses bibliometric analysis better to understand the relationship between smart manufacturing and sustainability scholarship and provide an up-to-date account of current industry practices.

Design/methodology/approach

This paper used the bibliometric analysis method to analyze and draw conclusions from 839 articles retrieved from the Scopus database from 1994 to February 2022. The methodology is divided into four steps: data collection, analysis, visualization, and interpretation. The current study aims to comprehend smart manufacturing and sustainability scholarship using the bibliometric R-package and VOSviewer software.

Findings

The study provides fascinating insights that may assist scholars, industry professionals, and top management in conceptualizing smart manufacturing and sustainability in their organizations. The results show that the number of publications has significantly increased from 2015 onwards, reaching a maximum of 317 journals in 2021 with an increasing publication annual growth rate of 21.9%. The United Kingdom, India, the United States of America, Italy, France, Brazil and China were the most productive countries in terms of the total number of publications. Technological Forecasting and Social Change, Journal of Cleaner Production, International Journal of Production Research, Production Planning and Control, Business Strategy and the Environment Technology in Society, and Benchmarking: An International Journal emerged as the top outlets.

Research limitations/implications

The research in the area of smart manufacturing and sustainability is underpinned by this study, which aims to understand the trends in this field over the last two decades in terms of prolific authors, most influential journals, key themes, and the field's intellectual and social structure. However, according to the research, this field is still in its early stages of development. As a result, a more in-depth analysis is required to aid in the development of a better understanding of this new field.

Originality/value

The paper focuses on integrating smart manufacturing and sustainability through increased interest from 2015 onwards through the literature review. Specific policies should be formulated to improve the manufacturing sector's competence. Furthermore, these findings can guide researchers who want to delve deeper into smart manufacturing and sustainability.

A Hybrid Dragonfly Algorithm for Efficiency Optimization of Induction Motors

Induction motors tend to have better efficiency on rated conditions, but at partial load conditions, when these motors operate on rated flux, they exhibit lower efficiency. In such conditions, when these motors operate for a long duration, a lot of electricity gets consumed by the motors, due to which the computational cost as well as the total running cost of industrial plant increases. Squirrel-cage induction motors are widely used in industries due to their low cost, robustness, easy maintenance, and good power/mass relation all through their life cycle. A significant amount of electrical energy is consumed due to the large count of operational units worldwide; hence, even an enhancement in minute efficiency can direct considerable contributions within revenue saving, global electricity consumption, and other environmental facts. In order to improve the efficiency of induction motors, this research paper presents a novel contribution to maximizing the efficiency of induction motors. As such, a model of induction motor drive is taken, in which the proportional integral (PI) controller is tuned. The optimal tuning of gains of a PI controller such as proportional gain and integral gain is conducted. The tuning procedure in the controller is performed in such a condition that the efficiency of the induction motor should be maximum. Moreover, the optimization concept relies on the development of a new hybrid algorithm, the so-called Scrounger Strikes Levy-based dragonfly algorithm (SL-DA), that hybridizes the concept of dragonfly algorithm (DA) and group search optimization (GSO). The proposed algorithm is compared with particle swarm optimization (PSO) for verification. The analysis of efficiency, speed, torque, energy savings, and output power is validated, which confirms the superior performance of the suggested method over the comparative algorithms employed.

MuST-POS: multiscale spatial-temporal 3D atrous-net and PCA guided OC-SVM for crowd panic detection

Crowd panic detection (CPD) is crucial to control crowd disasters. The recent CPD approaches fail to address crowd shape change due to perspective distortion in the frame and across the frames. To this end, we are motivated to design a simple but most effective model known as multiscale spatial-temporal atrous-net and principal component analysis (PCA) guided one-class support vector machine (OC-SVM), i.e., MuST-POS for the CPD. The proposed model utilizes two multiscale atrous-net to extract multiscale spatial and multiscale temporal features to model crowd scenes. Then we adopted PCA to reduce the dimension of the extracted multiscale features and fed them into an OC-SVM for modeling normal crowd scenes. The outliers of the OC-SVM are treated as crowd panic behavior. Three publicly available datasets: the UMN, the MED, and the Pets-2009, are used to show the effectiveness of the proposed MuST-POS. The MuST-POS achieves the detection accuracy of 99.40%, 97.61%, and 98.37% on the UMN, the MED, and the Pets-2009 datasets, respectively, and performs better to recent state-of-the-art approaches.

Evaluation of symptoms along with depression, anxiety and stress levels of Sars-CoV-2 positive patients during the second wave in the city of Indore, Central India - a cross sectional study

INTRODUCTION

The consequences of the second wave hitting India have drastically laid a huge impact on the mental state of patients. The second wave had proven to be far more dangerous and hence the psychological evaluation needed to be conducted to know the scenario of patients suffering from SARS-CoV-2.

OBJECTIVE

This study was undertaken to evaluate the symptoms of SARS-CoV-2 patients along with the existing depression, anxiety and stress levels amongst them.

MATERIAL AND METHODS

An observational, cross-sectional questionnaire-based survey was conducted among 351 patients infected with SARS-CoV-2 during the second wave in Indore, Central India. The questionnaire consisted of questions pertaining to socio-demographic characteristics, clinical signs and symptoms. Evaluation of depression, anxiety and stress levels were done by use of 21 item Depression, Anxiety, Stress Scale (DASS-21).

RESULTS

The most common symptom amongst patients was cough (42.2%) followed by fever (40.2%). Sixty-nine (19.6%) patients were asymptomatic. Depression score was found to have significant, positive weak correlation with age (ρ-0.124, p-0.020, p value <.05). No significant difference was observed between the depression, anxiety and stress score of males and females. Based on the scores assigned to the responses, patients who tested positive were belonging to normal category with no diagnosed depression, anxiety or stress.

CONCLUSION

The present study showed fever, cough, headache, weakness, and chest pain as the common sign and symptoms of COVID-19 during the second wave. There was a prevalence of low levels of anxiety, stress and depression amongst patients in Radha Saomi Covid Care Centre, Indore during the second wave.

Frequency, Prevalence, Interaction, and Gender Differences of Six Psychosocial Factors in Patients Presenting to a Psychiatric Emergency Service

INTRODUCTION

The psychiatric emergency service (PES) has become an increasingly utilized patient care approach over the past 50 years. Psychosocial factors play an important role in PES utilization and disposition of patients in this environment. PES utilization in our region has increased 450% in the past 18 years, while the population has changed <1%. Our objective was to determine the frequency and relationship between six psychosocial factors in our patients, in comparison to the general population.

METHODS

We completed a retrospective chart review of 160 patients selected at random who utilized the PES during 2009-2010. We looked for historical presence of a broken family, abuse or neglect, substance abuse, legal problems, violence, or incomplete education. We also looked at the relationship of these factors to race and gender.

RESULTS

75% had three or more of the factors measured. In our population, substance abuse and broken family were most prevalent. Females had significantly more abuse or neglect as compared to males, while male subjects had significantly more substance abuse and legal issues than females. The presence of a broken family was strongly associated with abuse or neglect, while violence was associated with incomplete education and legal issues. The prevalence of these factors in our patients was higher than the general population.

CONCLUSION

The average patient presenting to the PES has multiple major psychosocial problems at higher frequencies than the general population. Understanding the relationship between multiple psychosocial factors and increasing PES utilization can direct us towards addressing the problems causing the increase in PES presentation. A planned future prospective study will examine the incidence of these psychosocial factors in patients presenting to the PES as compared to the general population.

1251 Risk Stratification in the Management of Post-Tonsillectomy Haemorrhage

Aim

Tonsillectomy represents 17% of the elective workload in ENT and post-tonsillectomy haemorrhage is the most significant complication of this procedure. Accordingly, the GIRFT (Getting It Right First Time) report for ENT surgery focusses on the prevention of post-tonsillectomy bleeding. However, there is little guidance on the management of post-tonsillectomy haemorrhage. A local guideline for the management of post-tonsillectomy haemorrhage was introduced in 2020 based on expert consensus. This audit examines the management of patients readmitted with post-tonsillectomy haemorrhage in 2019 and compares this to the management suggested in the new guideline.

Method

Patients readmitted with post-tonsillectomy haemorrhage within 30 days of a tonsillectomy performed in 2019 were identified. These were retrospectively stratified into risk categories according to both patient and clinical factors. Management was audited against the new guideline including both the initial patient assessment and the treatment suggested for their respective risk category.

Results

Fifteen patients were identified and stratified into low, medium and high-risk categories. All patients in the “low risk” category were successfully treated conservatively. One patient from the “medium risk” category had a further bleed as an inpatient during the proposed period of observation in the new guideline and was thereafter treated as “high risk”. Within the “high risk” category two patients required return to theatre for arrest of post-tonsillectomy haemorrhage.

Conclusions

These results show that the risk stratification proposed in these guidelines may be useful in the management of post-tonsillectomy haemorrhage. Amendments to the guideline and a re-audit are in progress.

Variable Length Character N-Gram Embedding of Protein Sequences for Secondary Structure Prediction

BACKGROUND

The prediction of a protein's secondary structure from its amino acid sequence is an essential step towards predicting its 3-D structure. The prediction performance improves by incorporating homologous multiple sequence alignment information. Since homologous details not available for all proteins. Therefore, it is necessary to predict the protein secondary structure from single sequences.

OBJECTIVE AND METHODS

Protein secondary structure predicted from their primary sequences using n-gram word embedding and deep recurrent neural network. Protein secondary structure depends on local and long-range neighbor residues in primary sequences. In the proposed work, the local contextual information of amino acid residues captures variable-length character n-gram words. An embedding vector represents these variable-length character n-gram words. Further, the bidirectional long short-term memory (Bi-LSTM) model is used to capture the long-range contexts by extracting the past and future residues information in primary sequences.

RESULTS

The proposed model evaluates on three public datasets ss.txt, RS126, and CASP9. The model shows the Q3 accuracy of 92.57%, 86.48%, and 89.66% for ss.txt, RS126, and CASP9.

CONCLUSION

The proposed model performance compares with state-of-the-art methods available in the literature. After a comparative analysis, it observed that the proposed model performs better than state-of-the-art methods.

Protein Secondary Structure Prediction Using Character Bi-gram Embedding and Bi-LSTM

Background:

Protein secondary structure is vital to predicting the tertiary structure, which is essential in deciding protein function and drug designing. Therefore, there is a high requirement of computational methods to predict secondary structure from their primary sequence. Protein primary sequences represented as a linear combination of twenty amino acid characters and contain the contextual information for secondary structure prediction.

Objective and Methods:

Protein secondary structure predicted from their primary sequences using a deep recurrent neural network. Protein secondary structure depends on local and long-range residues in primary sequences. In the proposed work, the local contextual information of amino acid residues captures with character n-gram. A dense embedding vector represents this local contextual information. Furthermore, the bidirectional long short-term memory (Bi-LSTM) model is used to capture the long-range contexts by extracting the past and future residues information in primary sequences.

Results:

The proposed deep recurrent architecture is evaluated for its efficacy for datasets, namely ss.txt, RS126, and CASP9. The model shows the Q3 accuracies of 88.45%, 83.48%, and 86.69% for ss.txt, RS126, and CASP9, respectively. The performance of the proposed model is also compared with other state-of-the-art methods available in the literature.

Conclusion:

After a comparative analysis, it was observed that the proposed model is performing better in comparison to state-of-art methods.

Synthesis, biological evaluation, and molecular docking study of some new rohitukine analogs as protein tyrosine phosphatase 1B inhibitors

Rohitukine (RH) was extracted from the stem bark of Dysoxylum binectariferum Hook. It was derivatized to different arylsulphanmides by treating with the corresponding aryl sulphonyl chlorides. These derivatives were tested in-vitro on protein tyrosine phosphatase 1B (PTP1B) inhibition. Among these the active compounds K2, K3, K5, and K8 significantly inhibited the PTP1B by 51.3%, 65.6%, 71.9%, and 55.9% respectively at 10 µg/ml, the results were also supported by in-silico docking experiments. The most potent compound K5 was analyzed for antidiabetic and antidyslipidemic activity in vivo. It showed a marked reduction in blood glucose level (random and fasting) and serum insulin level in db/db mice. It improved glucose intolerance as ascertained by the oral glucose tolerance test (OGTT). These NCEs (New Chemical Entities) also lowered cholesterol and triglyceride profiles while improved high-density lipoprotein cholesterol in db/db mice. The K5 was further evaluated for antiadipogenic activity on MDI (Methylisobutylxanthine, dexamethasone, and insulin)-induced adipogenesis. where it significantly inhibited MDI-induced adipogenesis in 3 T3-L1 preadipocytes, at 10 µM and 20 µM concentration. These results were compared with the parent compound RH which inhibited 35% and 45% lipid accumulation while the RH analog K5 inhibited the lipid accumulation by 41% and 51% at 10 and 20 µM concentration, respectively. These results well corroborated with in-silico studies.

A Survey on Medical Image Analysis in Capsule Endoscopy

BACKGROUND AND OBJECTIVE

Capsule Endoscopy (CE) is a non-invasive, patient-friendly alternative to conventional endoscopy procedure. However, CE produces 6 to 8 hrs long video posing a tedious challenge to a gastroenterologist for abnormality detection. Major challenges to an expert are lengthy videos, need of constant concentration and subjectivity of the abnormality. To address these challenges along with high diagnostic accuracy, design and development of automated abnormality detection system is a must. Machine learning and computer vision techniques are devised to develop such automated systems.

METHODS

Study presents a review of quality research papers published in IEEE, Scopus, and Science Direct database with search criteria as capsule endoscopy, engineering, and journal papers. The initial search retrieved 144 publications. After evaluating all articles, 62 publications pertaining to image analysis are selected.

RESULTS

This paper presents a rigorous review comprising all the aspects of medical image analysis concerning capsule endoscopy namely video summarization and redundant image elimination, Image enhancement and interpretation, segmentation and region identification, Computer-aided abnormality detection in capsule endoscopy, Image and video compression. The study provides a comparative analysis of various approaches, experimental setup, performance, strengths, and limitations of the aspects stated above.

CONCLUSIONS

The analyzed image analysis techniques for capsule endoscopy have not yet overcome all current challenges mainly due to lack of dataset and complex nature of the gastrointestinal tract.

Efficiency Enhancement in Organic Solar Cells by Use of Cobalt Phthalocyanine (CoPc) Thin Films

Cobalt phthalocyanine (CoPc) nano thin films have been introduced as a hole buffer layer in organic solar cells with active layer of Poly(3-hexylthiophene) (P3HT) and [6,6]-phenyl-C₆₁-butyric acid methyl ester (PCBM). The surface morphology and opto-electrical properties of the CoPc thin films have made it an applicable materials for organic solar cells. The nano-thin films of CoPc are continuously distributed over the studied area and the roughness are around 5 to 7 nm for all thickness. The dominant optical absorptions are in the visible range of wavelengths 500 to 800 nm. The CoPc buffer layer is suitable for energy level matching in energy level diagram and enhances the absorption spectrum as well, which facilitate the charge carrier generation, increases charge transport, decreases charge recombination, hence enhance the all device parameters short circuit current density (J_sc), open circuit voltage (V_oc) and fill factor (FF). The solar cells efficiency increases by ˜70% and the fill factor increases by ˜45% in comparison to the standard cells. The increase in efficiency and the fill factors of the solar cells may also be attributed to the increasing of shunt and lowering the series resistance of the cells. The cole-cole plots of the devices may be modeled in electrical circuit as a single parallel resistance R_b and capacitance C_b network with a series resistance R_c.

Validity and reliability of intraoral conventional tracer and intraoral digital tracer in different positions for recording horizontal jaw relation in edentulous patients

Aim

The purpose of the study is to evaluate and compare the horizontal mandibular positions recorded between intraoral conventional tracer and intraoral digital tracer in upright and supine position.

Materials and Methods

Twenty-four edentulous patients with well-formed ridge and adequate interarch distance space were selected. MATLAB software was assimilated with intraoral digital tracer and was utilized in the study for recording the horizontal movements of the mandible, i.e., gothic arch tracing by intraoral digital tracer and was observed on a laptop with the help of MATLAB Software. For each subject, multiple mandibular readings were recorded and analyzed through software, and similar readings were recorded with conventional intraoral tracers. The comparison between intraoral conventional tracer and intraoral digital tracer was done to assess the reliability. Moreover, the consistency of recording horizontal mandibular position was also compared between upright and supine position.

Results

The data were procured and utilized in comparison for different positions revealed statistically significant difference by using Student's Paired t-test. The test resulted in supine position better compared to upright position (P = 0.0001). The association between supine position with upright position was calculated using Fischer's exact test, and it was also found to be statistically significant (P = 0.002). The Pearson's Correlation analysis was performed to check the agreement between upright and supine position and very weak downhill correlation (r ²= -0.130) was observed between the two variables.

Conclusion

On evaluation and comparison of horizontal mandibular position, it was found that the intraoral digital tracing technique is more valid compared to conventional intraoral tracer technique. It was also observed that the consistency of reproducibility in recording horizontal mandibular position in supine position is significantly higher than upright position.

Abstract P3-16-01: Responding at patient's time of need: Scaling rapid access to evidence-based treatment plans

Background: Cancer patients worldwide feel intense anxiety, often racing to start treatments at non expert centers. Further, imbalanced oncologist to patient ratios (˜1600: 1.8 M in India, ˜23,000: 15M in USA), impedes access to expertise. We study the impact of rapid evidence based expert treatment plans in relieving patient anxiety. Navya, a health services technology, generates personalized treatment plans that maps within NCCN Resource Stratified Guidelines [SABCS 2017]. This is vetted on mobile by oncologists at tertiary centers like TMC NCG to provide expert opinion reports to patients. Since 2015, ˜19,457 patients from 57 countries have reached out for an online opinion. On the ground, 78% of patients received evidence based treatments recommended by Navya [ASCO 2017].

Methods: To assess impact of timeliness, a prospective series of patients (from Sep '17 to April '18) were asked: “Were you relieved to receive expert opinion report in [x] days?” “Does it matter to you to receive expert opinion report in 1 day?” To assess time savings, preliminary reports with treatment options from NCCN and TMC NCG guidelines were shared with patients who matched all guidelines criteria. Subsequently, expert opinion reports were shared as usual.

Results: 543/701 patients responded to phone follow-up. 97% [± 3.2] were relieved to receive expert opinion reports in 1-2 days (103/106) vs 83% [± 3.8 ] for 3+ days (365/437). Of those not relieved by 3+ day turnaround, 83% stated that it would matter to receive expert opinion reports in 1 day (60/72). The first 300 preliminary reports shared in median time of 3.37 hours, resulted in 90% time savings vs expert opinion reports. On 10% of the preliminary reports, experts added information such as de-/escalating therapy (18/31), and additional diagnostic tests (6/31).

Conclusions: Navya relieves patient anxieties by responding at the time of need with evidence based treatment plans. Scaling such health services technologies to patients worldwide is feasible.

Citation Format: Ramarajan N, Srivastava R, Begum F, Gupta S, Pramesh C, Badwe R. Responding at patient's time of need: Scaling rapid access to evidence-based treatment plans [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr P3-16-01.

AFOMP policy number 6: code of ethics for medical physicists in AFOMP Countries

This policy statement, which is the sixth of a series of documents prepared by the Asia-Oceania Federation of Organizations for Medical Physics (AFOMP) Professional Development Committee, gives guidance on how medical physicists in AFOMP countries should conduct themselves in an ethical manner in their professional practice (Ng et al. in Australas Phys Eng Sci Med 32:175-179, 2009; Round et al. in Australas Phys Eng Sci Med 33:7-10, 2010; Round et al. in Australas Phys Eng Sci Med 34:303-307, 2011; Round et al. in Australas Phys Eng Sci Med 35:393-398, 2012; Round et al. in Australas Phys Eng Sci Med 38:217-221, 2015). It was developed after the ethics policies and codes of conducts of several medical physics societies and other professional organisations were studied. The policy was adopted at the Annual General Meeting of AFOMP held in Jaipur, India, in November 2017.

Lipopolysaccharide-induced priming enhances NO-mediated activation of defense responses in pearl millet challenged with Sclerospora graminicola

Lipopolysaccharide (LPS) elicitors isolated from Pseudomonas fluorescens UOM SAR 14 effectively induced systemic and durable resistance against pearl millet downy mildew disease caused by the oomycete Sclerospora graminicola. Rapid and increased callose deposition and H2O2 accumulation were evidenced in downy mildew susceptible seeds pre-treated with LPS (SLPS) in comparison with the control seedlings, which also correlated with expression of various other defense responses. Biochemical analysis of enzymes and quantitative real-time polymerase chain reaction data suggested that LPS protects pearl millet against downy mildew through the activation of plant defense mechanisms such as generation of nitric oxide (NO), increased expression, and activities of defense enzymes and proteins. Elevation of NO concentrations was shown to be essential for LPS-mediated defense manifestation in pearl millet and had an impact on the other downstream defense responses like enhanced activation of enzymes and pathogen-related (PR) proteins. Temporal expression analysis of defense enzymes and PR-proteins in SLPS seedlings challenged with the downy mildew pathogen revealed that the activity and expression of peroxidase, phenylalanine ammonia lyase, and the PR-proteins (PR-1 and PR-5) were significantly enhanced compared to untreated control. Higher gene expression and protein activities of hydroxyproline-rich glycoproteins (HRGPs) were observed in SLPS seedlings which were similar to that of the resistant check. Collectively, our results suggest that, in pearl millet-downy mildew interaction, LPS pre-treatment affects defense signaling through the central regulator NO which triggers the activities of PAL, POX, PR-1, PR-5, and HRGPs.

Evaluation of the Protection Provided by Hepatitis B Vaccination in India

OBJECTIVE

In India, Hepatitis B vaccination is recommended at 6 wk except for hospital-deliveries. The authors examined protection afforded by the birth dose.

METHODS

A case-control study was done. HBsAg and HBcAb were tested in 2671 children, 1 to 5 y and HBsAb was evaluated in a subset of 1413 children. Vaccination history was recorded. Cases were HBsAg carriers. In another analysis, children who got infected (HBsAg and/or HBcAb positive) were considered as cases. Exposed were the unvaccinated. In another analysis, exposed were those vaccinated without the birth dose.

RESULTS

The odds ratio (OR) for HBsAg positivity with birth vaccination was 0.35 (95% CI 0.19-0.66); while with vaccination at 6 wk was 0.29 (95%CI 0.14-0.61), both compared to unvaccinated. Birth vaccination has no added protection when compared to the unvaccinated. Unvaccinated children in index study had HBsAg positivity of 4.38%. The number needed to treat (NNT) to prevent one case of HBsAg positivity was 32.6 (95% CI, 20.9 to 73.6). The odds of getting HBV infection was 0.42 (CI 0.25-0.68) with birth dose and 0.49 (CI 0.30-0.82) without the birth dose compared to the unvaccinated. Protective antibody (HBsAb) was present in about 70% of the vaccinated. In the unimmunised, in the first 2 y HBsAb protection was present in 40%. The odds ratio (OR) for HBsAb in the fully vaccinated between 4 and 5 y was 1.4 (95%CI 0.9-2.18) compared to the unvaccinated.

CONCLUSIONS

The present study lends support to the pragmatic approach of the Government to vaccinate babies born at home starting at 6 wk.

Automated and effective content-based image retrieval for digital mammography

Nowadays, huge number of mammograms has been generated in hospitals for the diagnosis of breast cancer. Content-based image retrieval (CBIR) can contribute more reliable diagnosis by classifying the query mammograms and retrieving similar mammograms already annotated by diagnostic descriptions and treatment results. Since labels, artifacts, and pectoral muscles present in mammograms can bias the retrieval procedures, automated detection and exclusion of these image noise patterns and/or non-breast regions is an essential pre-processing step. In this study, an efficient and automated CBIR system of mammograms was developed and tested. First, the pre-processing steps including automatic labelling-artifact suppression, automatic pectoral muscle removal, and image enhancement using the adaptive median filter were applied. Next, pre-processed images were segmented using the co-occurrence thresholds based seeded region growing algorithm. Furthermore, a set of image features including shape, histogram based statistical, Gabor, wavelet, and Gray Level Co-occurrence Matrix (GLCM) features, was computed from the segmented region. In order to select the optimal features, a minimum redundancy maximum relevance (mRMR) feature selection method was then applied. Finally, similar images were retrieved using Euclidean distance similarity measure. The comparative experiments conducted with reference to benchmark mammographic images analysis society (MIAS) database confirmed the effectiveness of the proposed work concerning average precision of 72% and 61.30% for normal & abnormal classes of mammograms, respectively.

Risk of cardiovascular diseases and gastrointestinal tract disorders in relation to the impact of shift work among male workers of textile mills in Pali district of Rajasthan, India

he present study was conducted in textile mill of Pali city with the objective to study the risk of cardiac and gastric problems in relation to shift work among male workers. The results showed significant difference between Body Mass Index (χ2=14.2, p=0.0008) and waist hip ratio (χ2=5.2, p=0.0226) of shift and non shift workers. Results on food intake showed comparatively more deficient daily intake of nutrients like fiber (7.21 g), β carotene (1842.89 μg), and folic acid (99.03 μg) in shift workers with respect to recommended dietary allowances and an overconsumption of fats (37.72 g). Alcoholism (χ2=17.3, p=0.0001) and smoking habit (χ2=14, p=0.0029) was found to be significantly higher among shift workers. Comparatively higher segment of shift workers suffered from hypertension (56.0%), disturbed appetite (87.50%), stomach upsets (90.0%), nausea (88.50%), heartburn (85.0%) and breathlessness (34.0%). Shift workers also had higher mean levels of lipid profile fractions and relatively more subjects fell into the category of high risk (20.50%) of CVD. Longer duration of involvement in shift work also influenced the occurrence of gastric and cardiac problems like disturbed appetite (χ2=9.96, p=0.0411), stomach upsets (χ2=9.73, p=0.0452), stomach ache (χ2=10.1, p=0.0389), constipation (χ2=9.88, p=0.0424), dizziness (47.37%), breathlessness (50.0%) and swollen feet (44.74%). The study concludes that shift work affects food intake, BMI, WHR, BP, alcohol intake, smoking habits and lipid profile of the subjects, thus increasing the risk of health problems like cardiovascular and gastric diseases.

A fourth order PDE based fuzzy c- means approach for segmentation of microscopic biopsy images in presence of Poisson noise for cancer detection

BACKGROUND AND OBJECTIVE

For cancer detection from microscopic biopsy images, image segmentation step used for segmentation of cells and nuclei play an important role. Accuracy of segmentation approach dominate the final results. Also the microscopic biopsy images have intrinsic Poisson noise and if it is present in the image the segmentation results may not be accurate. The objective is to propose an efficient fuzzy c-means based segmentation approach which can also handle the noise present in the image during the segmentation process itself i.e. noise removal and segmentation is combined in one step.

METHODS

To address the above issues, in this paper a fourth order partial differential equation (FPDE) based nonlinear filter adapted to Poisson noise with fuzzy c-means segmentation method is proposed. This approach is capable of effectively handling the segmentation problem of blocky artifacts while achieving good tradeoff between Poisson noise removals and edge preservation of the microscopic biopsy images during segmentation process for cancer detection from cells.

RESULTS

The proposed approach is tested on breast cancer microscopic biopsy data set with region of interest (ROI) segmented ground truth images. The microscopic biopsy data set contains 31 benign and 27 malignant images of size 896 × 768. The region of interest selected ground truth of all 58 images are also available for this data set. Finally, the result obtained from proposed approach is compared with the results of popular segmentation algorithms; fuzzy c-means, color k-means, texture based segmentation, and total variation fuzzy c-means approaches.

CONCLUSIONS

The experimental results shows that proposed approach is providing better results in terms of various performance measures such as Jaccard coefficient, dice index, Tanimoto coefficient, area under curve, accuracy, true positive rate, true negative rate, false positive rate, false negative rate, random index, global consistency error, and variance of information as compared to other segmentation approaches used for cancer detection.

Kinetic study of furan and furfural generation during baking of cake models

This study describes the kinetics of furan and furfural generation in a cake model, for the first time. These process-induced compounds impact safety and sensory aspects of baked products. Understanding their generation with regards to process dynamics will serve food quality design. However, the complexity of real products makes this task challenging. This work provides a novel approach to understand and model chemical reactivity by implementing an inert cake model (starch, water and cellulose), specifically designed for mimicking a sponge cake structure. The addition of reaction precursors (glucose and leucine) to follow Maillard and caramelization reactions, resulted in browning and generated considerable levels of furanic compounds (up to 17.61ng/g for furan and 38.99μg/g for furfural, dry basis). Multiresponse data modeling resulted in a kinetic model which adequately describes experimental concentrations and makes it possible to estimate the degradation of precursors and the behavior of two hypothetic intermediates.