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Unison of in situ data with satellite remote sensing data has been widely used for water body quality monitoring; however, the developed synergized model is unique and thus needs to be recalibrated and revalidated before applying to other water bodies. In some Indian locations, water quality monitoring is conducted only once a year due to associated costs and time. To aid such instances, in this study, stepwise linear regression models were developed using in situ (annual) and Landsat 7 (biweekly) remote sensing data and validated for two inland water bodies (Sursagar and Nalsarovar lakes) in Gujarat state for dissolved oxygen, biochemical oxygen demand, electrical conductivity, pH, and nitrate. Results indicated that all models showed good to excellent performance metrics based on an r value (p < 0.01) ranging from 0.86 to 0.98 and 0.72 to 0.99 for Sursagar and Nalsarovar lakes, respectively. All models had root mean square errors less than 0.5, and residual predictive deviations greater than 2, which depicted good predictability. The models were able to increase the water quality assessment from annual resolution to biweekly resolution and provided insights on the dynamics of water quality parameters, improved understanding on key drivers for the change, and identified peak pollution leading to unfit conditions for domestic or agricultural consumption.

Background/Objective During the coronavirus disease 2019 (COVID-19) pandemic, several homeopathic prognostic factor research (PFR) projects have been undertaken. We found two projects with comparable outcomes to assess consistency and possible flaws. Methods Two comparisons were made. (1) Outcome of a PFR data collection from the Liga Medicorum Homoeopathica Internationalis (LMHI) by about 100 doctors with 541 cases was compared with a previous analysis of 161 cases in the same database. (2) The updated LMHI database was also compared with a data collection carried out in India by four doctors with a total of 1,445 cases. Differences that resulted in conflicting outcomes (indication in one, contraindication in the other) were examined for possible causes. Results There was only a single outcome in the updated LMHI database that conflicted with the previous dataset, and this could have been due to statistical variation. The Indian data contained many cases, from few doctors, while the LMHI database had few cases per doctor, but many doctors. The overlap between the projects (individual cases entered in both) was between zero and 22%. In 72 comparisons we found six (8.3%) conflicting outcomes. Possible causes were statistical error due to small numbers of cases and/or observers, confirmation bias, and keynote prescribing if this resulted in symptoms being inadequately checked. Conclusion There was little conflict between the outcomes of the two versions of one project and between the two different PFR projects. Differences could mostly be explained by causes that can be managed. This consistency should primarily be interpreted as showing a strong overall consensus between homeopathic practitioners worldwide, but with variation of consensus between small groups of practitioners.

Bibliometric analysis of the groundwater-related literature available for South and Southeast Asia from the Scopus database over the last 50 years (1970–2020) was conducted to provide valuable insights and aid in effective groundwater management.

Background The clinical profile and course of COVID-19 evolved perilously in a second wave, leading to the use of various treatment modalities that included homeopathy. This prognostic factor research (PFR) study aimed to identify clinically useful homeopathic medicines in this second wave. Methods This was a retrospective, multi-centred observational study performed from March 2021 to May 2021 on confirmed COVID-19 cases who were either in home isolation or at COVID Care Centres in Delhi, India. The data were collected from integrated COVID Care Centres where homeopathic medicines were prescribed along with conventional treatment. Only those cases that met a set of selection criteria were considered for analysis. The likelihood ratio (LR) was calculated for the frequently occurring symptoms of the prescribed medicines. An LR of 1.3 or greater was considered meaningful. Results Out of 769 confirmed COVID-19 cases reported, 514 cases were selected for analysis, including 467 in home isolation. The most common complaints were cough, fever, myalgia, sore throat, loss of taste and/or smell, and anxiety. Most cases improved and there was no adverse reaction. Certain new symptoms, e.g., headache, dryness of mouth and conjunctivitis, were also seen. Thirty-nine medicines were prescribed, the most frequent being Bryonia alba followed by Arsenicum album, Pulsatilla nigricans, Belladonna, Gelsemium sempervirens, Hepar sulphuris, Phosphorus, Rhus toxicodendron and Mercurius solubilis. By calculating LR, the prescribing indications of these nine medicines were ascertained. Conclusion Add-on use of homeopathic medicines has shown encouraging results in the second wave of COVID-19 in integrated care facilities. Further COVID-related research is required to be undertaken on the most commonly prescribed medicines.

Sensor networks are critical for building smart environments for monitoring various physical and environmental conditions. Several automated tasks involving continuous and critical practically becomes infeasible for humans to perform with precision. Therefore, wireless sensor networks have emerged as the next-generation technology to permeate the technological upgradations into our daily activities. Such intelligent networks, embedded with sensing expertise, however, are severely energy-constrained. Sensor networks have to process and transmit large volumes of data from sensors to sink or base station, requiring a lot of energy consumption. Since energy is a critical resource in the sensor network to drive all its basic functioning, hence, it needs to be efficiently utilized for elongating network lifetime. This makes energy conservation primarily significant in sensor network design, especially at the sensor node level. Our research proposes an On-balance volume indicator-based Data Prediction (ODP) model for predicting the temperature in the sensor network. Our proposed model can be used to predict temperature with a permissible error of tolerance. This helps in reducing excessive power consumption expended in redundant transmissions, thereby increasing the network lifetime. The proposed data prediction model is compared with existing benchmark time series prediction models, namely Linear Regression (LR) and Auto-Regressive Integrated Moving Average (ARIMA). Experimental outcomes endorsed that our proposed prediction model outperformed the existing counterparts in terms of prediction accuracy and reduction in the number of transmissions in clustered architecture.

The sensor networks have revolutionized wireless technology with next-generation internetworking and scalable infrastructure. Since, the autonomous sensor operates in vulnerable and unattended environmental conditions, hence, securing the sensed data becomes a critical issue. Advanced Encryption Standard popularly known as AES is one of the most common and extensively used algorithms for private key encryption in sensor networks. However, the substitution box lookup table is a time-consuming process in AES. Therefore, our research targets to improve the running time and throughput of AES by proposing a novel technique of splitting AES S-box followed with parallel substituting of two bytes at a time using multithreading. The proposed Enhanced-AES has the feasibility of implementation in the heterogeneous and hierarchical multicore sensor network. Energy consumption and execution time are the parameters used to evaluate the functioning of the Enhanced-AES. Our proposed model outperformed the existing security mechanisms; thereby increasing the lifetime of the sensor network and maintaining the data freshness.

Wireless sensor networks have battery-operated sensor nodes, which need to be conserved to have prolonged network lifetime. The amount of power consumed for routing sensed data from the sensor node to the sink node is large. Thus, in order to optimize the energy usage in sensor network efficient data aggregation techniques are needed. Particle swarm optimization (PSO) is a speculative and evolutionary computing technique based on swarm intelligence for solving optimization problems in sensor network such as nodes deployment, node scheduling, data clustering, and aggregation. The paper proposes a PSO-based sensor network aggregation protocol (PSO-SNAP) with K-means to provide initial centroid. The PSO has been used to find the optimal aggregated value having minimum quantization error. The output of the K-means algorithm is used as an initial centroid in PSO. Apart from K-means, K-medoid and simple average has also been used to provide initial seed to the PSO algorithm and results of all three approaches are compared.

HDP-gestosis score is a risk scoring system (score 1-3) for the development of pre-eclampsia. When a pregnant woman's total score is equal to or greater than 3, she is labelled as "at risk for pre-eclampsia" and is managed accordingly.To determine the sensitivity, specificity, Positive predictive value (PPV), negative predictive value (NPV) and diagnostic accuracy of HDP-gestosis score for predicting pre-eclampsia.This prospective study included 473 pregnant women who presented at the department of Obstetrics and Gynaecology, from June 2020 to December 2021. After 20 weeks of pregnancy, the patients were assessed for the development of pre-eclampsia. Details of age, gravida, obstetric history, menstrual cycle regularity, polycystic ovarian disease history, duration of marriage, parity, past medical and surgical intervention, previous/present medication, and family history were taken. Gestosis score was calculated and classified into mild (score of 1), moderate (score of 2) and high risk (score of ≥ 3) for the development of Pre-eclampsia (PE). Sensitivity, Specificity, PPV, NPV and diagnostic accuracy of HDP-gestosis score for predicting the development of PE were determined.The mean age, gestational age, and BMI of the women were 28.4 ± 6.8 years, 11.5 ± 2.04 weeks, and 24.5 ± 3.7 kg/mGestosis score is a novel early marker for prediction of the development of PE allowing for a prompt management for the patients, thereby curbing the adverse consequences.

Background and Aims: Vast first MBBS course has been posing great problems for a fresh entrant in medical college. A student is merely 18-19 years when he gains entry to a medical college in India. Multiple problems pile up for him at the very onset of his academic year in Department of Anatomy. The present study aimed to identify various stress factors and further assess the effect of counselling/mentorship programme on solving such issues. Materials and Methods: A pre-validated, predesigned questionnaire was administered to hundred first year medical students. They were individually counselled with various faculty persons. Based on the questionnaire given, the results were analysed. Results: 45% of the students were sure that their language problem was solved after the session. 48% students accepted that they did have some adjustment problems, which were solved then. 30% expressed that some of their personal problems were also solved. 54% were now able to manage their time judiciously. 64% opined that counselling helped to plan their studies well. 52% students admitted that counselling improved their performance in theory. 46% expressed that counselling also helped to improve their performance in practicals. 65% told us that their skills both in theory and practical had improved after the session. 39% consented that counselling relieved them of anxiety. 67% recognised that their knowledge in the subject of anatomy had increased. 75% were of the view that similar sessions should be conducted more frequently. 40% preferred to express their views in their regional language. 85% students advised us to carry forward these sessions to the next batches. Conclusion: Our study highlights the need for counselling to solve multifaceted adjustment problems.

Sensor networks are data-centric networks constrained with limited battery power and processing capabilities. One of the crucial challenges in sensor network is energy hole problem. In order to deal with the challenge, there exists several mechanisms, of which clustering is considered an energy-efficient solution. In general, clustering refers to the technique of grouping nodes on the basis of similarity in spatial arrangement. An appropriately clustered network helps in processing and aggregation of sensed data before routing the information to destined location. This paper proposes a soft computing based recursive approach for implementing density-based hierarchical clustering, for Gaussian distributed sensor network. Our proposed probabilistic approach creates hierarchical clusters recursively, which not only addresses the problem of energy hole, but also reduces transmission delay, thereby maintaining data freshness.

Background: Severe pain associated with proximal femur fractures makes the positioning for regional anesthesia a challenge. Systemic administration of analgesics can have adverse effects. Individually, both the fascia iliaca block (FIB) and femoral nerve blocks (FNB) have been studied. However, there is little evidence comparing the two. The aim of this study was to compare the overall efficacy of the two blocks in patients with proximal femur fracture before positioning for spinal anesthesia. Methods: ASA (American Society of Anesthesiologists) class I, II, and III patients scheduled for elective and emergency surgery with the diagnosis of proximal femur fracture between October 2018 and June 2019 were included in the study. The patients were assigned to two groups by convenience nonprobability sampling of 35 each. Results: Our study showed a reduction in visual analogue scale scores at 3, 4, and 5 minutes after administration of the FIB being 5.1 ± 1.1, 4.1 ± 1.3, and 2.8 ± 0.8, and those after the FNB as 4.4 ± 1.1, 3.3 ± 1.1, and 2.1 ± 1.4 with P < 0.05, which was statistically significant. The mean first rescue analgesia time for the FIB was 7.1 ± 2.1 hours, while for the FNB it was 5.2 ± 0.7 hours. The P value was less than 0.001, which was significant. Conclusions: Both ultrasound guided FNB and FIB techniques provide sufficient analgesia for patient’s positioning before spinal anesthesia. However, the duration of postoperative analgesia provided by FIB was greater than that of the FNB.

Groundwater plays a pivotal role in sustaining the water needs of the population of South and Southeast Asia. However, long-term over-exploitation and unsustainable practices have caused groundwater depletion, and deterioration in many parts of the region, further impacting human health and ecosystem services. Thus, understanding the current groundwater research activities and identifying the issues is crucial for improving future studies. This study conducted a bibliometric analysis to evaluate the groundwater-related literature available for South and Southeast Asia from the Scopus database for the last 50 years (1970-2020). Of the total, this study identified 7,895 documents, representing 13% of the total global research productivity. India was the most productive country, followed by Pakistan and Malaysia. National Geophysical Research Institute, Anna University and Indian Institute of Technology, Kharagpur were the top three institutions with the highest number of groundwater-related publications. In international research collaboration, the United States and Japan were the two most collaborated countries with the South and Southeast Asian countries. Environmental Science, Earth and Planetary Sciences and Agricultural and Biological Sciences were the top three disciplines. The Environmental Earth Sciences journal published the highest number of groundwater-related publications in the study period. Research topic trends were observed through keyword analysis revealing increased outputs for groundwater quality, availability and suitability, recharge, and management. Our results provide valuable insights on groundwater issues that have received the most attention in South and Southeast Asia and identify the potential research topics and opportunities for researchers working in the groundwater domain.

Background Signaling molecules such as cytokines and interleukins are key mediators for the immune response in responding to internal or external stimuli. Homeopathically prepared signaling molecules have been used therapeutically for about five decades. However, these types of products are not available in many countries and their usage by homoeopaths is also infrequent. The aim of this scoping review is to map the available pre-clinical and clinical data related to the therapeutic use of homeopathically prepared signaling molecules. Methods We conducted a scoping review of clinical and pre-clinical studies of therapeutically used signaling molecules that have been prepared in accordance with an officially recognized homeopathic pharmacopoeia. Articles in peer-reviewed journals reporting original clinical or pre-clinical research of homeopathically prepared signaling molecules such as interleukins, cytokines, antibodies, growth factors, neuropeptides and hormones, were eligible. Non-English language papers were excluded, unless we were able to obtain an English translation. An appraisal of eligible studies took place by rating the direction of the outcomes on a five-point scale. The quality of the papers was not systematically assessed. Results Twenty-eight eligible papers, reporting findings for four different manufacturers' products, were identified and reviewed. Seventeen papers reported pre-clinical studies, and 11 reported clinical studies (six experimental, five observational). A wide range of signaling molecules, as well as normal T-cell expressed specific nucleic acids, were used. A majority of the products (21 of 28) contained two or more signaling molecules. The most common clinical indications were psoriasis, vitiligo, rheumatoid arthritis, respiratory allergies, polycystic ovary syndrome, and herpes. The direction of the outcomes was positive in 26 papers and unclear in two papers. Conclusion This scoping review found that there is a body of evidence on the use of homeopathically prepared signaling molecules. From a homeopathy perspective, these substances appear to have therapeutic potential. Further steps to explore this potential are warranted.

Background Prognostic factor research (PFR), prevalence of symptoms and likelihood ratio (LR) play an important role in identifying prescribing indications of useful homeopathic remedies. It involves meticulous unbiased collection and analysis of data collected during clinical practice. This paper is an attempt to identify causes of bias and suggests ways to mitigate them for improving the accuracy in prescribing for better clinical outcomes and execution of randomized controlled studies. Methods A prospective, open label, observational study was performed from April 2020 to December 2020 at two COVID Health Centers. A custom-made Excel spreadsheet containing 71 fields covering a spectrum of COVID-19 symptoms was shared with doctors for regular reporting. Cases suitable for PFR were selected. LR was calculated for commonly occurring symptoms. Outlier values with LR ≥5 were identified and variance of LRs was calculated. Results Out of 1,889 treated cases of confirmed COVID-19, 1,445 cases were selected for pre-specified reasons. Nine medicines, Arsenicum album, Bryonia alba, Gelsemium sempervirens, Pulsatilla nigricans, Hepar sulphuricus, Magnesia muriaticum, Phosphorus, Nux vomica and Belladonna, were most frequently prescribed. Outlier values and large variance for Hepar sulphuricus and Magnesia muriaticum were noticed as indication of bias. Confirmation bias leading to lowering of symptom threshold, keynote prescribing, and deficiency in checking of all symptoms in each case were identified as the most important sources of bias. Conclusion Careful identification of biases and remedial steps such as training of doctors, regular monitoring of data, checking of all pre-defined symptoms, and multicenter data collection are important steps to mitigate biases.

Background/Objective Coronavirus disease 2019 (COVID-19) is a new disease; its clinical profile and natural history are evolving. Each well-recorded case in homeopathic practice is important for deciding the future course of action. This study aims at identifying clinically useful homeopathic remedies and their prescribing symptoms using the prognostic factor research model.Methods This was an open-label, multi-centric, observational study performed from April 2020 to July 2020 at various public health care clinics. The data were collected prospectively from clinical practice at integrated COVID-19 care facilities in India. Good-quality cases were selected using a specific set of criteria. These cases were analyzed for elucidating prognostic factors by calculating the likelihood ratio (LR) of each frequently occurring symptom. The symptoms with high LR values (>1) were considered as prescribing indications of the specific remedy.Results Out of 327 COVID-19 cases reported, 211 met the selection criteria for analysis. The most common complaints were fatigue, sore throat, dry cough, myalgia, fever, dry mouth and throat, increased thirst, headache, decreased appetite, anxiety, and altered taste. Twenty-seven remedies were prescribed and four of them—Arsenicum album, Bryonia alba, Gelsemium sempervirens, and Pulsatilla nigricans—were the most frequently used. A high LR was obtained for certain symptoms, which enabled differentiation between the remedies for a given patient.Conclusion Homeopathic medicines were associated with improvement in symptoms of COVID-19 cases. Characteristic symptoms of four frequently indicated remedies have been identified using prognostic factor research, findings that can contribute to accurate homeopathic prescribing during future controlled research in COVID-19.

Objective The aim of the study was to identify indicated homeopathic remedies based on the clinical characteristics of coronavirus disease 2019 (COVID-19) patients in India. Methods In this retrospective, cohort study, confirmed COVID-19 patients admitted at a COVID Health Centre in New Delhi between April 29 and June 17, 2020 were given conventional and homeopathic treatment. Patients were grouped into mild, moderate or severe categories of disease. Their symptomatologic profiles were analyzed to identify indicated homeopathic medicines. Results A total of 196 COVID-19 patients were admitted. One hundred and seventy-eight patients had mild symptoms; eighteen patients had moderate symptoms; no patients with severe symptoms were included as they were referred to tertiary care centers with ventilatory support. The mean age of patients with mild symptoms was significantly lower (38.6 years; standard deviation or SD ± 15.8) compared with patients in the moderate category (66.0 years; SD ± 9.09). The most important symptoms identified were fever (43.4%), cough (47.4%), sore throat (29.6%), headache (18.4%), myalgia (17.9%), fatigue (16.8%), chest discomfort (13.8%), chills (12.6%), shortness of breath (11.2%) and loss of taste (10.2%). Twenty-eight homeopathic medicines were prescribed, the most frequently indicated being Bryonia alba (33.3%), Arsenicum album (18.1%), Pulsatilla nigricans (13.8%), Nux vomica (8%), Rhus toxicodendron (7.2%) and Gelsemium sempervirens (5.8%), in 30C potency. Conclusion Data from the current study reveal that Arsenicum album, Bryonia alba, Pulsatilla nigricans, Nux vomica, Rhus toxicodendron and Gelsemium sempervirens are the most frequently indicated homeopathic medicines. A randomized controlled clinical trial based on this finding is the next step.

Context: Coronavirus disease 2019 (COVID-19) has been one of the deadliest pandemics in recent decade. The virus has specifically targeted the comorbid population in terms of mortality. The present systematic review and meta-analysis aimed to determine the overall mortality and diabetes-associated mortality in COVID-19 patients. Methods: To obtain the related data, six databases, including Pubmed, Embase, MEDLINE, Web of Science, Google Scholar, and DOAJ, were searched. The full-texts of articles presenting the data of COVID-19 mortality and diabetes-associated mortality were screened and retrieved. Statistical analysis was performed using the Stata (version 13). The odds ratio (OR) of mortality in diabetic patients was calculated with 95% confidence interval (CI). Random-effects model was used to synthesize data for the relevant outcomes. Heterogeneity was evaluated using I2 statistic. Forest plots visually showed the effect estimates of the included studies. We used funnel plots to evaluate potential publication bias. A two tailed P < 0.05 was considered as statistically significant. Results: A total of 35 studies with 25,934 patients were finally included for meta-analysis. The pooled prevalence of diabetes mellitus in patients with COVID-19 was 16.8% (n = 4381). The overall mortality seen in all the studies was 12.81% (n = 3159), and diabetes-associated mortality was 22.14% (n = 970). The pooled analysis of included studies showed that diabetes mellitus had a significantly higher mortality rate (22.14% vs. 12.81%, P < 0.05) with higher odds of death (pooled OR 1.83, 95% CI: 1.61 - 2.05). The funnel plot was symmetric, thereby indicating a low risk of publication bias. Conclusions: In conclusion, the presence of diabetes was associated with a significantly increased risk of mortality in patients admitted to the hospital with COVID-19. Thus, this subpopulation must be continuously monitored for glycemic levels, coagulation abnormalities, and inflammatory surge.

Apparently, two-thirds of our planet is covered by water bodies, of which oceanic coverage deeply impacts terrestrial life. Oceans form a majestic resource of most of the amazing marine creatures and natural resources, especially mineral oil. This makes underwater monitoring one of the most demanding areas to explore. For monitoring the oceanic layer, a specialized form of underwater vehicle network is employed using underwater drones for acoustic monitoring. The underwater drones are autonomously operated and remotely tracked using onshore and offshore base stations. The underwater research draws eminence from a wide variety of application-based research, including collaborative undersea exploration, naval surveillance and other oceanographic mining and engineering tasks. Underwater communication confronts unique challenges that involve severe bandwidth constraints, high bit error rates, variable propagation delays and limited spatial correlation. Such constraints mandate the use of sound waves for information transfer that further requires complex signal processing at receiving ends. Owing to such complex capabilities, acoustic sensors are quite expensive and therefore, sparsely deployed. Moreover, oceanic tides and drifts results in high vulnerability for acoustic devices towards failure due to fouling and corrosion underwater. Our research is focused on presenting surveys of architecture and applications of underwater drones for acoustic sensor networks abided by significant characteristics and challenges.

Background and objectives In compliance with MCI's recommendation, a month-long Foundation Course was conducted in our institution in Aug'19. The present study was conducted to seek the opinion of the students and faculty regarding relevance of the topics included in the course to enable revisions to be made in designing the course for the subsequent sessions, to make it more effective and student-oriented. Methods A questionnaire-based cross-sectional study was conducted to seek the opinion of 100 students who underwent the Foundation Course and 35 faculty members who were involved in teaching the topics allotted. Results Positive feedback was recorded from63% of the students and 69% of the faculty regarding the overall experience of the course. Among the different modules, Skills module was voted as relevant by majority (73%) of students, whereas a module on Enhancement of Language and Computer Skills was found relevant to the course by a mere 52% students. In contrast, module on Sports and Extracurricular activities was adjudged as most relevant while as module on Enhancement of Language and Computer Skills got the least positive feedback (81.43% vs. 60.36%, respectively) from the faculty. Interpretation and conclusions Foundation Course recommended by MCI for MBBS students at the entry level is a welcome step. This can further be made more beneficial by making necessary modifications in the planning of the course in light of suggestions received from the participants.

COVID-19 has affected over 24 million patients with over 8 lacs deaths worldwide. Till date, no specific drug or vaccine has been proven effective for the treatment of SARS-CoV-2 patients. In a recent observational cohort study conducted at Delhi Government designated COVID hospital on 221 patients, important facts have emerged indicating the benefits of using unconventional treatment protocol (consisting of combination of homeopathic medicines, allopathic medicines & non-pharmacological interventions) in COVID-19 situation. The interdisciplinary team of doctors and scientists have found that out of 221 COVID-19 positive patients (diagnosed with RT-PCR), 169 patients were clinically classified as patients of mild category and 21 were labeled as moderate whereas 31 patients were asymptomatic. 170 patients were treated with either homeopathic medicines, allopathic medicines or the combination of both. The unconventional treatment protocol combining homeopathic medicines and allopathic systems showed rapid improvement in clinical condition. We found that fever (temperature) was reduced to normal on an average of 7.45 ± 5.19 days. The treatment outcome of this study showed better results when compared with control datasets obtained from independent studies published earlier. All the patients were discharged on an average of 9.29 ± 3.52 days. The mortality rate was zero and there were no adverse events for the patients. Apart from temperature and fever, symptoms such as cough, sore throat, myalgia, & dyspnea also improved significantly in the patients enrolled in this study. The average time for clinical improvement was significantly better than previously published clinical studies on COVID-19 patients in independent hospitals. This study indicates that the use of unconventional treatment protocol can emerge as an effective treatment option for COVID-19 patients. Despite the challenges (i.e. limited availability of high quality clinical data), we generated a machine learning model for semi-automated prediction of potential treatments (i.e. medicines, non-pharmacological interventions) that should be prescribed to the patient based on the time dependent clinical features, comorbidities, & demographic profiling. Further, efforts are underway to include additional clinical data from COVID19 patients to provide a robust and accurate machine learning based prescription system.

The study is an attempt to design a watershed scorecard by identifying and evaluating selected set of indicators, such as surface water quality, ground water quality, soil condition, agriculture condition, and forest condition, which accurately reflect the health of the watershed. Ur River Watershed in Tikamgarh District, Madhya Pradesh was taken as a case study to assess the watershed health. Evaluation was done by calculating different indices for the selected set of indicators and comparing them with the National standards and guidelines. Based on the performance of each indicator, the grades were assigned to the indicators which helped in designing the watershed scorecard. The results revealed that within the watershed, the forest and soil conditions need a considerable plan for improvement in order to maintain the ecosystem whereas the surface water quality, groundwater quality and the agricultural conditions requires protection as well as enhancement in certain areas. Keywords: Watershed scorecard, Ur River, Bundelkhand, Water quality, Agricultural condition

Adnexal masses in postmenopausal women represent a spectrum of conditions from gynaecologic and non gynaecologic sources. Post-menopausal women are especially at high risk of gynaecologic malignancy however even in this population majority of adnexal masses are benign. Tumor markers and high frequency transvaginal ultrasonography with doppler studies project high quality images allowing for detailed macroscopic appearances of masses and remain the least expensive modalities available. Adnexal masses that are suspicious for cancer, women should be referred to gynaecologic oncologist for optimal care. How to cite this article Yadav P, Gupta M, Agarwal M, Garg R, Verma U, Gupta S. Role of Imaging in Dilemma of Adnexal Masses in Postmenopausal Women. J South Asian Feder Menopause Soc 2017;5(1):45-50.

Natural disasters like droughts have worsened the conditions of the villages of Bundelkhand region, India. Droughts have caused a diverse impact on the economic, environmental and social conditions of the districts. Therefore, in order to identify the variability of vulnerability, Livelihood Vulnerability Index (LVI) was calculated to assess the vulnerability, with the purpose of identifying relevant adaptation response mechanisms. The index is applied in a comparative study of four selected watersheds of Bundelkhand region, that is Ur watershed (Tikamgarh district, Madhya Pradesh), Kathan watershed (Chhatarpur–Sagar district, Madhya Pradesh), Patrahi–Lakheri watershed (Jhansi district, Uttar Pradesh) and Sajnam watershed (Lalitpur district, Uttar Pradesh). The sub-watershed-based classification was used to assess the vulnerability of people, livelihood and ecosystem to climate change, using primary and secondary data, to identify highly vulnerable sub-watersheds within a watershed, and a comparative analysis was done amongst the four districts. The LVI–IPCC approach was used to reflect the vulnerability based on 39 environmental and socio-economic sub-indicators, through IPCC-identified components: exposure, sensitivity and adaptive capacity. The overall vulnerability results reveal that the Ur watershed in Tikamgarh district was the most vulnerable to climate change than the rest due to high sensitivity and less adaptive capacity. The findings helped in suggesting sector specific as well as overall drought management and adaptation strategies to cope up with the climate change. These can be implemented by the state government and the local bodies to reduce the vulnerability and enhance adaptive capacity of all the four drought-prone districts.

Introduction Since menopause was related to variety of genitourinary, vasomotor, psychological and musculoskeletal changes, conjugated equine estrogen (CEE) was introduced for all menopause-related symptoms in various doses. Materials and methods It is a comparative study in which 100 postmenopausal women were selected (natural or surgical menopause) with one or more menopausal symptoms. All patients were randomly divided in two groups. Group A received 0.3 mg CEE and group B received 0.625 mg CEE, and both groups were compared with each other in various aspects. Results Both the groups were comparable to each other with respect to mean age, residence, type of menopause, total duration of menopause. Both the groups show comparable improvement in vasomotor, genitourinary and psychological symptoms and p > 0.05 which is not significant. On evaluation of bone mineral density (BMD), the group B showed significant improvement than group A (p < 0.001). Effect on endometrium was not significant. Conclusion Because of the complications of estrogen ± progestin, it should be prescribed at the lowest effective doses and for the shortest duration consistent with treatment goals and risk for individual women. How to cite this article Yadav P, Singh R, Kaur H, Gupta M, Kumar S. Comparative Study of Low Dose Conjugate Equine Estrogen 0.3 mg vs Standard Dose Conjugate Equine Estrogen 0.625 mg as Hormone Replacement Therapy. J South Asian Feder Menopause Soc 2013;1(2):45-49.

Background: Caesarean section is one of the most widely performed surgical procedures in obstetrics worldwide. It was mainly evolved as a lifesaving procedure for mother and foetus during the difficult delivery. To analyze the rate and indications for C-Section and associated maternal morbidity and mortality were the main objectives of present study. Methods: This retrospective study was conducted over a period of one year from 1 st Jan 2016 to 31 st Dec 2016 at the Department of OBG, RUHS College of Medical Sciences, Jaipur (Rajasthan), North India. Data of patients who delivered by C-Section in our hospital during the defined study period was recorded and a statistical analysis of various parameters namely, the caesarean section rates, its indications, the patient’s morbidity and mortality was done. Results: The total numbers of women delivered over the study period were 2959, out of which C-Sections were 931. The overall CS rate was 31.46%. Previous LSCS was the leading indication to the CS rate (36.52%) followed by arrest of labour (13.65%), CPD (12.03%), foetal distress (11.82%), breech presentation (7.52%), oligohydroamnios/IUGR (5.16%) and failed induction of labour (3.54%). 11.60% patients had various complications mainly infection (5.58%) and haemorrhage (3.98%). There was no mortality during this period. Conclusions: Being a tertiary care hospital, a high rate of Caesarean deliveries was observed. Individualization of the indication and careful evaluation, following standardized guidelines, practice of evidenced-based obstetrics and audits in the institution, can help us limit CSR.

One source of phosphorous pollution in areas of high chicken production is runoff from fields using fertilizer from these operations. A potential solution is to feed chicken high available phosphorus (HAP) corn, reducing phosphorus in manure. This study examined consumer purchase likelihood of chickens fed HAP, created traditionally or through genetic modification, and other genetically modified (GM) corn including Bt and Roundup-ready. Survey results from the Delmarva Peninsula found considerable interest in non-GM HAP corn, although GM HAP corn was not typically viewed as more acceptable than other GM varieties. Overall, the marketplace appears open to products geared toward environmental benefits.