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Background: Reproductive tract infections (RTIs) and gynaecological morbidities constitute a significant proportion of morbidities in women of the reproductive age group. Objective: To find out the proportion of reproductive age group women in an urban resettlement colony having the symptoms of gynaecological morbidities and RTI/sexually transmitted infection (STI) and to explore the healthcare seeking behaviour of the women during such ailments. Materials and Methods: A community-based cross-sectional study was conducted from January to June 2023 among 317 females aged 15 to 49 years in an urban resettlement area of Gautam Buddh Nagar district in Uttar Pradesh. Statistical analysis used included descriptive statistics and Chi-square test. SPSS 23 was used for data analysis. Results: There was a prevalence of 65.3% of at least one self-reported symptom suggestive of RTI/STI or gynaecological morbidity among the respondents. Among the symptomatic respondents, 54.1% were found to seek treatment. Among the reasons given for not seeking treatment in any health facility, the majority (83.1%) responded 'lack of awareness that the problem needed medical treatment' and 'stigma related with the problem' to be the main reasons. Conclusion: The reproductive healthcare seeking behaviour was found to be inadequate, with only 54.1% of symptomatic respondents seeking treatment. Enhanced health awareness sessions can be planned in the short term, and skilled communicators within or outside of the health system in the long term may be deployed to disseminate information in the community regarding sexual and gynaecological morbidities in reproductive age group women and their timely intervention.

Context: Hypertension is the leading cause of mortality and disability-adjusted life year (DALY) all over the world. World Health Organization defines quality of life (QOL) as an individual's perception of their position in life in the context of the culture and value systems in which they live and in relation to their goals, expectations, standards, and concerns. Aims: The study was aimed to compare the the QOL of adult hypertensive patients with healthy study subjects and to determine the factors associated with poor QOL among the hypertensive subjects. Materials and Methods: A community-based cross-sectional study was conducted from January 2021 to June 2022 in Gautam Budh Nagar District. The study was carried out among 250 hypertensive patients and 50 healthy persons based on World Health Organization-Quality of Life-BREF questionnaire Manual. Statistical Analysis Used: Data collected were entered and statistically analyzed using statistical software (SPSS-22). Results: Overall QOL and general health scores were significantly lower among hypertensive subjects showing worsening of QOL among diseased persons (P value

Background: Ageing is a universal process. It is influenced by a person's genetic makeup, lifestyle, and environmental factors. Nutrition plays a significant role in modulation of ageing. In developing countries like India, the health and nutritional status of the elderly population is not satisfactory. Objectives: The current study was done to assess undernutrition amongst the elderly population and to find the various associations with sociodemographic factors and social determinants. Materials and Methods: This cross-sectional study was done from February 2023 to July 2023 in rural area of District Gautam Buddha Nagar, Uttar Pradesh. The elderly participants who were 60 years of age and more and who met the inclusion criteria were selected by simple random sampling technique for the study. Undernutrition was assessed by Mini Nutritional Assessment Tool (MNA). Result: Out of the total of 400 elderly persons, 18% were found to be undernourished and 38% were at risk of undernutrition. The association between undernutrition and age group, gender, occupation, family type, living arrangements that is staying with family or not, financial dependence, any co-morbid illness, smoking, and physical activity was found to be significant. Conclusion: The present findings reveal that undernutrition is not an uncommon problem in the elderly, and further studies are needed in this regard.

AIM: Synthetic MRI (SyMRI) works on the MDME sequence, which acquires the relaxation properties of the brain and helps to measure the accurate tissue properties in 6 minutes. The aim of this study was to evaluate the synthetic MRI (SyMRI)-generated myelin (MyC) to white matter (WM) ratio, the WM fraction (WMF), MyC partial maps performing normative brain volumetry to investigate MyC loss in multiple sclerosis (MS) patients with white-matter hyperintensites (WMHs) and non-MS patients with WMHs in a clinical setting. MATERIALS and METHODS: Synthetic MRI images were acquired from 15 patients with MS, and from 15 non-MS patients on a 3T MRI scanner (Discovery MR750w; GE Healthcare; Milwaukee, USA) using MAGiC, a customized version of SyntheticMR's SyMRI® IMAGE software marketed by GE Healthcare under a license agreement. Fast multi-delay multi-echo acquisition was performed with a 2D axial pulse sequence with different combinations of echo time (TEs) and saturation delay times. The total image acquisition time was 6 minutes. SyMRI image analysis was done using SyMRI software (SyMRI Version: 11.3.6; Synthetic MR, Linköping, Sweden). SyMRI data were used to generate the MyC partial maps and WMFs to quantify the signal intensities of test group and control group, andcontrol group , and their mean values were recorded. All patients also underwent conventional diffusion-weighted imaging, i.e., T1w and T2w imaging. RESULTS: The results showed that the WMF was significantly lower in the test group than in the control group (38.8% vs 33.2%, p < 0.001). The Mann-Whitney U nonparametric t-test revealed a significant difference in the mean myelin volume between the test group and the control group (158.66 ± 32.31 vs. 138.29 ± 29.28, p = 0.044). Also, there were no significant differences in the gray matter fraction and intracranial volume between the test group and the control group. CONCLUSIONS: We observed MyC loss in test group using quantitative SyMRI. Thus, myelin loss in MS patients can be quantitatively evaluated using SyMRI.

Context: Diabetes has become the largest health emergencies of 21st century. The burden of diabetes is increasing globally especially in developing economies like India. In the recent years, Physician’s interest has turned to the concept of quality of life (QOL) as an important treatment goal and an important component of therapy in the management of diabetes. The study was aimed to compare the quality of life of adult diabetic subjects with healthy subjects and to assess the factors affecting the quality of life among diabetic subjects. Methodology: A Community based cross sectional study was conducted among 250 diabetic subjects and 50 healthy subjects more than 18 years of age, based on WHO-Quality of Life-BREF (WHO-QOL-BREF) questionnaire manual in the rural area of District Gautam Buddha Nagar from Jan 2021-June 2022. Data collected were entered and statistically analyzed using statistical software (SPSS-22) Results: Overall quality of life and general health score was significantly poor among diabetic subjects as compared to healthy subjects. Quality of life was significantly lower in diabetic subjects ≥ 60 years of age, illiterate subjects and in diabetic subjects with presence of comorbidity. Conclusions: Overall QOL was poor among diabetic subjects as compared to healthy subjects.

Introduction: Nationwide lockdown during COVID-19 pandemic, rapid transmission of SARS-CoV-2 and more intense second wave of pandemic in terms of high caseload, reduced essential supplies and scarcity of beds, and unpredictable mortality had a huge impact on mental health of general population. Objectives: This study aimed to estimate the prevalence and determine the factors of Post-Traumatic Stress Disorder (PTSD) among the general population of India during the second wave of pandemic. Methodology: An online survey was conducted to estimate the prevalence and assess the factors responsible for PTSD among 614 participants who were above 18 years of age. The prevalence of PTSD was measured using PTSD checklist for Diagnostic and Statistical Manual of Mental Disorder-5 (DSM-5). Results: In the present study, there were a total of 614 participants and the mean age of the participants was 23.64 years. More than half of the participants were female and majority of them were unmarried (76.7%). The prevalence of PTSD was 30.62%. A significant association was observed between occupation and PTSD prevalence. Also, COVID-19 illness, COVID-19 infection among the family members, work outside the home, hospital admissions, difficulty in acquiring the required medication were significantly associated with PTSD. Conclusion: COVID-19 pandemic and nationwide lockdown led to increased prevalence of psychological effects like PTSD. This highlights the importance of focusing on mental health during the pandemic.

Introduction: Smartphones and computers provide access to the Internet, which makes us highly reliant on them and leads to phubbing. Phubbing is described as the behaviour of ignoring others by looking at your mobile phone during a conversation with another individual and escaping from interpersonal communication. Almost all medical students have smartphones, so it is imperative to study this behaviour among them. Aim: To assess phubbing and its determinants among medical students of the School of Medical Sciences and Research, Gautam Buddha Nagar. Materials and Methods: A cross-sectional study was carried out from July 2021 to September 2021 among medical students of the School of Medical Sciences and Research, Sharda University, Gautam Buddha Nagar, India. A total of 422 MBBS students, who were present during data collection were included. Data was collected using a self-administered structured questionnaire, consisting of the socio-demographic profile, phubber’s characteristics, phubbing scale, smartphone addiction scale, and self-control scale. The data were analysed using frequency, percentage, and unadjusted odds ratio. Results: A total of 422 MBBS students data was analysed. Mean age, 21.28±1.27 years. The prevalence of phubbing was 42.7 percent in present study. Among them, 151 (83.9%) were college phubbers. On the phubbing scale, the majority of the participants, 306 (72.5%) reported that their phone was always within their reach. There was a significant association found between smartphone addiction (OR:3.880; 95%CI:2.290-6.574) and lack of self-control (OR:1.992; 95%CI:1.250-3.172) with phubbing. Conclusion: This study found a high prevalence of phubbing behaviour. The results presented that phubbing is determined by a lack of self-control and smartphone addiction. Findings highlight the need to regulate smartphone and social media usage during college time and at home.

Aim: The aim of this study is to elucidate the patterns of characteristic hypometabolism on 18F-Fluoro Deoxy-glucose (18F-FDG) Positron Emission Tomography/Computed Tomography (PET/CT) in the variants of Progressive supranuclear palsy (PSP) and its correlation with their core clinical features. Material and Methods: A retrospective analysis of 88 subjects with clinically suspected PSP was done. An institutional informed consent to participate in the study was taken from all the subjects. All the subjects had undergone a prior 99mTechnetium labeled Tropane derivative of dopamine transporter Single Photon Emission Computed Tomography (99mTc TRODAT-1 SPECT) study and had abnormal scans to confirm degenerative parkinsonism. The subjects were clinically examined by the neurologists using the Progressive Supranuclear Palsy Rating Scale proposed by the Movement Disorder Society and were further clinically segregated into eight subtypes. All the included subjects further underwent a detailed clinical analysis to obtain their individual Schwab and England activities of daily living scale and Modified Hoehn and Yahr staging by a neurologist. All the subjects underwent 18F-FDG PET/CT scan after adequate preparation. The scans were analyzed both qualitatively (visually) and quantitatively using Statistical Parametric Mapping. Results: The frontal, limbic, and sensorimotor cortices represented the common areas of hypometabolism among all the subtypes of PSP. The subcortical regions showing the most significant hypometabolism were the thalami, mid-brain, nucleus accumbens, caudate, globus pallidus, and putamen in descending order. Multiple cortical and subcortical regions of hypometabolism were identified in different subtypes of PSP. Conclusion: The characteristic patterns of hypometabolism observed in the different subgroups were more apparent on quantification and based on visual analysis alone, it may not be possible to differentiate the different subtypes of PSP. A good correlation was seen between some of the core clinical features and hypometabolic clusters.

Abstract SARS-CoV2 pandemic exposed the limitations of artificial intelligence based medical imaging systems. Earlier in the pandemic, the absence of sufficient training data prevented effective deep learning (DL) solutions for the diagnosis of COVID-19 based on X-Ray data. Here, addressing the lacunae in existing literature and algorithms with the paucity of initial training data; we describe CovBaseAI, an explainable tool using an ensemble of three DL models and an expert decision system (EDS) for COVID-Pneumonia diagnosis, trained entirely on pre-COVID-19 datasets. The performance and explainability of CovBaseAI was primarily validated on two independent datasets. Firstly, 1401 randomly selected CxR from an Indian quarantine center to assess effectiveness in excluding radiological COVID-Pneumonia requiring higher care. Second, curated dataset; 434 RT-PCR positive cases and 471 non-COVID/Normal historical scans, to assess performance in advanced medical settings. CovBaseAI had an accuracy of 87% with a negative predictive value of 98% in the quarantine-center data. However, sensitivity was 0.66–0.90 taking RT-PCR/radiologist opinion as ground truth. This work provides new insights on the usage of EDS with DL methods and the ability of algorithms to confidently predict COVID-Pneumonia while reinforcing the established learning; that benchmarking based on RT-PCR may not serve as reliable ground truth in radiological diagnosis. Such tools can pave the path for multi-modal high throughput detection of COVID-Pneumonia in screening and referral.

Due to repeated heavy loads, environmental conditions and non-frequent monitoring, the rail is subjected to heavy damage resulting in sudden failure. Hence, a frequent, faster, and efficient monitoring strategy is required. This paper attempts to investigate the application of guided wave (GW) generated through surface-bonded piezo-electric wafer transducer (PWT) to detect damages in rail at high frequencies. Firstly, a combined experimental and simulation study is presented in an effort to understand the dispersion characteristics of guided wave and its interaction with head damages in a relatively small rail specimen. The numerical simulation results are validated with those obtained from the experiments showing a good agreement between them. Secondly, a framework based on the machine learning algorithm is proposed to efficiently detect damage in rail head. Numerous inseparable guided wave modes are observed at higher frequencies implying the inability to detect damage through specific mode. Therefore, a machine learning framework is trained using time, frequency, and time–frequency domain features of the signal. Total 672 numerical simulations of different types of damage with different severity and location in the rail head are carried out to train and validate the model. It is found that GW generated through surface bonded PWTs is able to detect minimum defect size of 5% of head area with 1 mm thickness. Finally, the proposed framework is tested using simulation and experiment results of arbitrary damage in the rail head. The error in estimating severity was found to be in the range from 2.00% to 16.67%.

Acoustic emission (AE) is an emerging technology for real-time non-destructive testing of structures. While research on a simulated AE source in rail and testing on rail material using small beam samples have been conducted, a study is required in lab environment to investigate AE waveform characteristics generated by crack in rail. In this paper, a three-point bending test is conducted on an actual rail section of 1500 mm with transverse damage of 38% head area to simulate AE source due to crack opening in the rail. AE signals are recorded for three different loads. For data analysis, unsupervised machine learning algorithms such as k-means, fuzzy-C mean and gaussian mixture model are used to cluster and filter out usable signals from the whole dataset corrupted by noisy signals from various sources. k-mean with principal component was observed to be best technique based on silhouette score. The frequency and amplitude of waveform have been discussed in relation to load and crack opening displacement. This study establishes a baseline for linking load, crack opening, and AE wave characteristics. This work can ultimately aid in the development of robust denoising, and damage analysis algorithms based on the frequency content and dispersion of the AE waveform.