Groundwater quality characterization using an integrated water quality index and multivariate statistical techniques.

This study attempts to characterize and interpret the groundwater quality (GWQ) using a GIS environment and multivariate statistical approach (MSA) for the Jakham River Basin (JRB) in Southern Rajasthan. In this paper, analysis of various statistical indicators such as the Water Quality Index (WQI) and multivariate statistical methods, i.e., principal component analysis and correspondence analysis (PCA and CA), were implemented on the pre and post-monsoon water quality datasets. All these methods help identify the most critical factor in controlling GWQ for potable water. In pre-monsoon (PRM) and post-monsoon (POM) seasons, the computed value of WQI has ranged between 28.28 to 116.74 and from 29.49 to 111.98, respectively. As per the GIS-based WQI findings, 63.42 percent of the groundwater samples during the PRM season and 42.02 percent during the POM were classed as 'good' and could be consumed for drinking. The Principal component analysis (PCA) is a suitable tool for simplification of the evaluation process in water quality analysis. The PCA correlation matrix defines the relation among the water quality parameters, which helps to detect the natural or anthropogenic influence on sub-surface water. The finding of PCA's factor analysis shows the impact of geological and human intervention, as increased levels of EC, TDS, Na+, Cl-, HCO3-, F-, and SO42- on potable water. In this study, hierarchical cluster analysis (HCA) was used to categories the WQ parameters for PRM and POR seasons using the Ward technique. The research outcomes of this study can be used as baseline data for GWQ development activities and protect human health from water-borne diseases in the southern region of Rajasthan.

Prediction of weighted arithmetic water quality index for urban water quality using ensemble machine learning model.

Urban water quality index (WQI) is an important factor for assessment quality of groundwater in the urban and rural area. In this research, the Weighted Arithmetic Water Quality Index (WA-WQI) was estimated for understanding the groundwater quality. Four machine learning (ML) models were developed including artificial neural network (ANN), support vector machine (SVM), random forest (RF), and extreme gradient boosting (XG-Boost) in addition to multiple linear regression (MLR) for WA-WQI prediction at the Ujjain city of Madhya Pradesh in India. Groundwater quality samples were collected from 54 wards under the urban area, the main eight different physiochemical parameters were selected for WA-WQI prediction. The different input parameters data were analysed and calculated for the relationships of their ability to predict the results of WA-WQI. The ML models performance were calculated using three statistical metrics such as determination coefficient (R2), mean absolute error (MAE), and root mean square error (RMSE). In this research shown the XG-Boost model is better results other than other ML models. The best modelling results over the training phase revealed R2 = 0.969, RMSE = 2.169, MAE = 2.013 and over the testing phase R2 = 0.987, RMSE = 3.273, MAE = 2.727). All the ML models results were validated using receiver operating characteristic (ROC) curve for the best models selection. The results of best model area under curve (AUC) was 0.9048. Hence, XG-Boost model was given the accurate prediction of WA-WQI in the urban area. Based on the graphical presentation evaluation, XG-Boost model showed similar results of superiority. The obtained modelling results emphasis the utility of computer aid models for better planning and essential information for decision-makers, and water experts. The implement agency can adopt the procedures of water quality to decrease pollution and safe and healthy water provide to entire Ujjain city.

Source identification and potential health risks from elevated groundwater nitrate contamination in Sundarbans coastal aquifers, India.

In recent years groundwater contamination through nitrate contamination has increased rapidly in the managementof water research. In our study, fourteen nitrate conditioning factors were used, and multi-collinearity analysis is done. Among all variables, pH is crucial and ranked one, with a value of 0.77, which controls the nitrate concentration in the coastal aquifer in South 24 Parganas. The second important factor is Cl-, the value of which is 0.71. Other factors like-As, F-, EC and Mg2+ ranked third, fourth and fifth position, and their value are 0.69, 0.69, 0.67 and 0.55, respectively. Due to contaminated water, people of this district are suffering from several diseases like kidney damage (around 60%), liver (about 40%), low pressure due to salinity, fever, and headache. The applied method is for other regions to determine the nitrate concentration predictions and for the justifiable alterationof some management strategies.

Monitoring of wetland turbidity using multi-temporal Landsat-8 and Landsat-9 satellite imagery in the Bisalpur wetland, Rajasthan, India.

Satellite imagery has emerged as the predominant method for performing spatial and temporal water quality analyses on a global scale. This study employs remote sensing techniques to monitor the water quality of the Bisalpur wetland during both the pre and post-monsoon seasons in 2013 and 2022. The study aims to investigate the prospective use of Landsat-8 (L8) and Landsat-9 (L9) data acquired from the Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) for the temporal monitoring of turbidity. Concurrently, the study examines the relationship of turbidity with water surface temperature (WST) and chlorophyll-a (Chl-a) concentrations. We utilized visible and near-infrared (NIR) bands to conduct a single-band spectral response analysis of wetland turbidity. The results reveal a notable increase in turbidity concentration in May 2022, as this timeframe recorded the highest reflectance (0.28) in the NIR band. Additionally, the normalized difference turbidity index (NDTI) formula was used to assess the overall turbidity levels in the wetland. The results indicated that the highest concentration was observed in May 2013, with a value of 0.37, while the second-highest concentration was recorded in May 2022, with a value of 0.25. The WST was calculated using thermal band-10 in conjunction with Chlorophyll-a, utilizing the normalized difference chlorophyll index (NDCI). The regression analysis shows a positive correlation between turbidity and WST, as indicated by R2 values of 0.41 in May 2013 and 0.40 in May 2022. Furthermore, a robust positive relationship exists between turbidity and Chl-a, with a high R2 value of 0.71 in May 2022. These findings emphasize the efficacy of the L8 and L9 datasets for conducting temporal analyses of wetland turbidity, WST, and Chl-a. Additionally, this research underscores the critical role of satellite imagery in assessing and managing water quality, particularly in situations where in-situ data is lacking.

Development of landslide susceptibility maps of Tripura, India using GIS and analytical hierarchy process (AHP).

Landslides are one of the most extensive and destructive geological hazards on the globe. Tripura, a northeastern hilly state of India experiences landslides almost every year during monsoon season causing casualties and huge economic losses. Hence, it is required to assess the landslide susceptibility of the area that would support short- and long-term planning and mitigation. The analytic hierarchy process (AHP) integrated with geospatial technology has been adopted for landslide susceptibility mapping in the state. Eight influencing factors such as slope, lithology, drainage density, rainfall, land use land cover, distance from rivers and roads, and soil type were selected to map the landslide susceptibility. Landslide susceptibility index (LSI) was found to vary from 6.205 during monsoon to 1.427 during post-monsoon season. The LSI values were classified into very high, high, moderate, low, and very low susceptibility. Landslide susceptibility maps for three different seasons, namely, pre-monsoon, monsoon, and post-monsoon, were prepared. The study showed that most of the areas of the state come under very low to moderate landslide susceptibility zones. Around 73.2% area of the state is found to be under low landslide-susceptible zones during the pre-monsoon season, around 62% area is prone to landslides with moderate susceptibility during the monsoon season, and 68.5% area comes under landslides with low susceptibility zones during the post-monsoon season. The results of this study may be referred to the engineers and planners for the assessment, control, and mitigation of landslides and the development of basic infrastructure in the state.

Driving errors as a function of listening to music and FM radio: A simulator study.

OBJECTIVES: Driving is a dynamic activity that takes place in a constantly changing environment, carrying safety implications not only for the driver but also for other road users. Despite the potentially life-threatening consequences of incorrect driving behavior, drivers often engage in activities unrelated to driving. This study aims to investigate the frequency and types of errors committed by drivers when they are distracted compared to when they are not distracted. METHODS: A total of 64 young male participants volunteered for the study, completing four driving trials in a driving simulator. The trials consisted of different distraction conditions: listening to researcher-selected music, driver-selected music, FM radio conversation, and driving without any auditory distractions. The simulated driving scenario resembled a semi-urban environment, with a track length of 12 km. RESULTS: The findings of the study indicate that drivers are more prone to making errors when engaged in FM radio conversations compared to listening to music. Additionally, errors related to speeding were found to be more prevalent across all experimental conditions. CONCLUSIONS: These results emphasize the significance of reducing distractions while driving to improve road safety. The findings add to our understanding of the particular distractions that carry higher risks and underscore the necessity for focused interventions to reduce driver errors, especially related to FM radio conversations. Future research can delve into additional factors that contribute to driving errors and develop effective strategies to promote safer driving practices.

Aneurysmal Subarachnoid Hemorrhage and Cardiac Related Fatality: Who Dies and Why?

Medical complications are a notable source of in-hospital death following aneurysmal subarachnoid hemorrhage (aSAH). However, there is a paucity of literature examining medical complications on a national scale. This study uses a national dataset to analyze the incidence rates, case fatality rates, and risk factors for in-hospital complications and mortality following aSAH. We found that the most common complications in aSAH patients (N = 170, 869) were hydrocephalus (29.3%) and hyponatremia (17.3%). Cardiac arrest was the most common cardiac complication (3.2%) and was associated with the highest case fatality rate overall (82%). Patients with cardiac arrest also had the highest odds of in-hospital mortality [odds ratio (OR), 22.92; 95% confidence interval (CI), 19.24-27.30; P < 0.0001], followed by patients with cardiogenic shock (OR, 2.96; 95% CI, 2.146-4.07; P < 0.0001). Advanced age and National Inpatient Sample-SAH Severity Score were found to be associated with an increased risk of in-hospital mortality (OR, 1.03; 95% CI, 1.03-1.03; P < 0.0001 and OR, 1.70; 95% CI, 1.65-1.75; P < 0.0001, respectively). Renal and cardiac complications are significant factors to consider in aSAH management, with cardiac arrest being the strongest indicator of case fatality and in-hospital mortality. Further research is needed to characterize factors that have contributed to the decreasing trend in case fatality rates identified for certain complications.

Integration of hydrogeological data, GIS and AHP techniques applied to delineate groundwater potential zones in sandstone, limestone and shales rocks of the Damoh district, (MP) central India.

The Damoh district, which is located in the central India and characterized by limestone, shales, and sandstone compact rock. The district has been facing groundwater development challenges and problems for several decades. To facilitate groundwater management, it is crucial to monitoring and planning based on geology, slope, relief, land use, geomorphology, and the types of the basaltic aquifer in the drought-groundwater deficit area. Moreover, the majority of farmers in the area are heavily dependent on groundwater for their crops. Therefore, delineation of groundwater potential zones (GPZ) is essential, which is defined based on various thematic layers, including geology, geomorphology, slope, aspect, drainage density, lineament density, topographic wetness index (TWI), topographic ruggedness index (TRI), and land use/land cover (LULC). The processing and analysis of this information were carried out using Geographic Information System (GIS) and Analytic Hierarchy Process (AHP) methods. The validity of the results was trained and tested using Receiver Operating Characteristic (ROC) curves, which showed training and testing accuracies of 0.713 and 0.701, respectively. The GPZ map was classified into five classes such as very high, high, moderate, low, and very low. The study revealed that approximately 45% of the area falls under the moderate GPZ, while only 30% of the region is classified as having a high GPZ. The area receives high rainfall but has very high surface runoff due to no proper developed soil and lack of water conservation structures. Every summer season show a declined groundwater level. In this context, results of study area are useful to maintain the groundwater under climate change and summer season. The GPZ map plays an important role in implementing artificial recharge structures (ARS), such as percolation ponds, tube wells, bore wells, cement nala bunds (CNBs), continuous contour trenching (CCTs), and others for development of ground level. This study is significant for developing sustainable groundwater management policies in semi-arid regions, that are experiencing climate change. Proper groundwater potential mapping and watershed development policies can help mitigate the effects of drought, climate change, and water scarcity, while preserving the ecosystem in the Limestone, Shales, and Sandstone compact rock region. The results of this study are essential for farmers, regional planners, policy-makers, climate change experts, and local governments, enabling them to understand the groundwater development possibilities in the study area.

Prediction of meteorological drought and standardized precipitation index based on the random forest (RF), random tree (RT), and Gaussian process regression (GPR) models.

Agriculture, meteorological, and hydrological drought is a natural hazard which affects ecosystems in the central India of Maharashtra state. Due to limited historical data for drought monitoring and forecasting available in the central India of Maharashtra state, implementing machine learning (ML) algorithms could allow for the prediction of future drought events. In this paper, we have focused on the prediction accuracy of meteorological drought in the semi-arid region based on the standardized precipitation index (SPI) using the random forest (RF), random tree (RT), and Gaussian process regression (GPR-PUK kernel) models. A different combination of machine learning models and variables has been performed for the forecasting of metrological drought based on the SPI-6 and 12 months. Models were developed using monthly rainfall data for the period of 2000-2019 at two meteorological stations, namely, Karanjali and Gangawdi, each representing a geographical region of Upper Godavari river basin area in the central India of Maharashtra state which frequently experiences droughts. Historical data from the SPI from 2000 to 2013 was processed to train the model into machine learning model, and the rest of the 2014 to 2019-year data were used for testing to forecast the SPI and metrological drought. The mean square error (MSE), root mean square error (RMSE), adjusted R2, Mallows' (Cp), Akaike's (AIC), Schwarz's (SBC), and Amemiya's PC were used to identify the best combination input model and best subregression analysis for both stations of SPI-6 and 12. The correlation coefficient ([Formula: see text]), mean absolute error (MAE), root mean square error (RMSE), relative absolute error (RAE), and root relative squared error (RRSE) were used to perform evaluation for SPI-6 and 12 months of both stations with RF, RT, and GPR-PUK kernel models during the training and testing scenarios. The results during testing phase revealed that the RF was found as the best model in forecasting droughts with values of [Formula: see text], MAE, RMSE, RAE (%), and RRSE (%) being 0.856, 0.551, 0.718, 74.778, and 54.019, respectively, for SPI-6 while 0.961, 0.361, 0.538, 34.926, and 28.262, respectively, for SPI-12 scales at Gangawdi station. Further, the respective values of evaluators at Karanjali station were 0.913 and 0.966, 0.541 and 0.386, 0.604 and 0.589, 52.592 and 36.959, and 42.315 and 31.394 for PUK kernel and RT models, respectively, during SPI-6 and SPI-12. Machine learning models are potential drought warning techniques because they take less time, have fewer inputs, and are less sophisticated than dynamic or scientific models.

Single-Atom Catalysts (SACs) for Photocatalytic CO2 Reduction with H2 O: Activity, Product Selectivity, Stability, and Surface Chemistry.

In recent years, single-atom catalysts (SACs) have attracted the interest of researchers owing to their suitability for various catalytic applications. For instance, their optoelectronic features, site-specific activity, and cost-effectiveness make SACs ideal for photocatalytic CO2 reduction. The activity, product selectivity, and photostability of SACs depend on various factors such as the nature of the metal/support material, the interaction between the metal atoms and support, light-harvesting ability, charge separation behavior, CO2 adsorption ability, active sites, and defects. Consequently, it is necessary to investigate these factors in depth to elucidate the working principle(s) of SACs for catalytic applications. Herein, the recent progress in the development of SACs for photocatalytic CO2 reduction with H2 O is reviewed. First, a brief overview of CO2 photoreduction and SACs for CO2 conversion is provided. Several synthesis strategies and useful techniques for characterizing SACs employed in heterogeneous catalysis are then described. Next, the challenges of SACs for photocatalytic CO2 reduction and related optimization strategies, in terms of activity, product selectivity, and stability, are explored. The progress in the development of noble metal- and transition metal-based SACs and dual-SACs for photocatalytic CO2 reduction is discussed. Finally, the prospects of SACs for CO2 reduction are considered.

Modelling the reference crop evapotranspiration in the Beas-Sutlej basin (India): an artificial neural network approach based on different combinations of meteorological data.

Accurate prediction of the reference evapotranspiration (ET0) is vital for estimating the crop water requirements precisely. In this study, we developed multi-layer perceptron artificial neural network (MLP-ANN) models considering different combinations of the meteorological data for predicting the ET0 in the Beas-Sutlej basin of Himachal Pradesh (India). Four climatic locations in the basin namely, Kullu, Mandi, Bilaspur, and Chaba were selected. The meteorological dataset comprised air temperature (maximum, minimum and mean), relative humidity, solar radiation, and wind speed, recorded daily for a period of 35 years (1984-2019). The datasets from 1984 to 2012 and 2013 to 2019 were utilized for training and testing the models, respectively. The performance of the developed models was evaluated using several statistical indices. For each location, the best performed MLP-ANN model was the one with the complete combination of the meteorological data. The architecture of the best performing model for Kullu, Mandi, Bilaspur, and Chaba was (6-2-4-1), (6-5-4-1), (6-5-4-1), and (6-4-6-1), respectively. It was observed, however, that the performance of other models was also relatively good, given the limited meteorological data utilized in those models. Further, to appreciate the relative predictive ability of the developed models, a comparison was performed with four existing established empirical models. The approach adopted in this study can be effectively utilized by water users and field researchers for modelling and predicting ET0 in data-scarce locations.

The critically ill brain after cardiac arrest.

Cardiac arrest can cause hypoxic-anoxic ischemic brain injury due to signaling cascades that lead to damaged cell membranes and vital cellular organelles, resulting in cell death in the setting of low or no oxygen. Some brain areas are more prone to damage than others, so patients with hypoxic-anoxic ischemic brain injury present with several outcomes, including reduced level of consciousness or alertness, memory deficits, uncoordinated movements, and seizures. Some patients may have mild deficits, while others may have such severe injury that it can progress to brain death. High-quality cardiopulmonary resuscitation is a proven technique to improve outcome after cardiac arrest, although morbidity and mortality remain high. Induced hypothermia, which involves artificially cooling the body immediately after cardiac arrest, may reduce injury to the brain and improve morbidity and mortality. Neuroprognostication after cardiac arrest is challenging and requires a multimodal approach involving clinical neurologic examinations, brain imaging, electrical studies to assess brain activity, and biomarkers to predict outcome.

Prediction of irrigation groundwater quality parameters using ANN, LSTM, and MLR models.

Forecasting the irrigation groundwater parameters helps plan irrigation water and crop, and it is commonly expensive because it needs various parameters, mainly in developing nations. Therefore, the present research's core objective is to create accurate and reliable machine learning models for irrigation parameters. To accomplish this determination, three machine learning (ML) models, viz. long short-term memory (LSTM), multi-linear regression (MLR), and artificial neural network (ANN), have been trained. It is validated with mean squared error (MSE) and correlation coefficients (r), root mean square error (RMSE), and mean absolute error (MAE). These machine learning models have been used and applied for predicating the six irrigation water quality parameters such as sodium absorption ratio (SAR), percentage of sodium (%Na), residual sodium carbonate (RSC), magnesium hazard (MH), Permeability Index (PI), and Kelly ratio (KR). Therefore, the two scenario performances of ANN, LSTM, and MLR have been developed for each model to predict irrigation water quality parameters. The first and second scenario performance was created based on all and second reduction input variables. The ANN, LSTM, and MLR models have discovered that excluding for ANN and MLR models shows high accuracy in first and second scenario models, respectively. These model's accuracy was checked based on the mean squared error (MSE), correlation coefficients (r), and root mean square error (RMSE) for training and testing processes serially. The RSC values are highly accurate predicated values using ANN and MLR models. As a result, machine learning models may improve irrigation water quality parameters, and such types of results are essential to farmers and crop planning in various irrigation processes.

Applications of various data-driven models for the prediction of groundwater quality index in the Akot basin, Maharashtra, India.

Data-driven models are important to predict groundwater quality which is controlling human health. The water quality index (WQI) has been developed based on the physicochemical parameters of water samples. In this area, water quality is medium to poor and is found in saline zones; very high pH ranges are directly affected on the water quality in this study area. Conventional WQI computation demands more time and is often observed with enormous errors during the calculation of sub-indices. In the present work, four standalone methods such as additive regression (AR), M5P tree model (M5P), random subspace (RSS), and support vector machine (SVM) were employed to predict WQI based on variable elimination technique. The groundwater samples were collected from the Akot basin area, located in the Akola district, Maharashtra, in India. A total of nine different input combinations were developed in this study. The datasets were demarcated into two classes (ratio 80:20) for model construction (training dataset) and model verification (testing dataset) using a fivefold cross-validation approach. The models were assessed using statistical and graphical appraisal metrics. The best input combinations varied among the model, generally, the optimal input variables (EC, pH, TDS, Ca, Mg, and Cl) during the training and validation stages. Results show that AR outperformed the other data-driven models (R2 = 0.9993, MAE = 0.5243, RMSE = 0.0.6356, %RAE = 3.8449, and RRSE% = 3.9925). The AR is proposed as an ideal model with satisfactory results due to enhanced prediction precision with the minimum number of input parameters and can thus act as the reliable and precise method in the prediction of WQI at the Akot basin.

Recent Developments in Lead and Lead-Free Halide Perovskite Nanostructures towards Photocatalytic CO2 Reduction.

Perovskite materials have been widely considered as emerging photocatalysts for CO2 reduction due to their extraordinary physicochemical and optical properties. Perovskites offer a wide range of benefits compared to conventional semiconductors, including tunable bandgap, high surface energy, high charge carrier lifetime, and flexible crystal structure, making them ideal for high-performance photocatalytic CO2 reduction. Notably, defect-induced perovskites, for example, crystallographic defects in perovskites, have given excellent opportunities to tune perovskites' catalytic properties. Recently, lead (Pb) halide perovskite and their composites or heterojunction with other semiconductors, metal nanoparticles (NPs), metal complexes, graphene, and metal-organic frameworks (MOFs) have been well established for CO2 conversion. Besides, various halide perovskites have come under focus to avoid the toxicity of lead-based materials. Therefore, we reviewed the recent progress made by Pb and Pb-free halide perovskites in photo-assisted CO2 reduction into useful chemicals. We also discussed the importance of various factors like change in solvent, structure defects, and compositions in the fabrication of halide perovskites to efficiently convert CO2 into value-added products.

Cerebral Venous Sinus Thrombosis in the COVID-19 Pandemic.

BACKGROUND: Recent studies have noted concern for increased thromboembolic events in the setting of Coronavirus Disease 2019 (COVID-19). Cerebral venous sinus thrombosis (CVST) is a form of thromboembolism that has been observed as a neuro-ophthalmologic complication of COVID-19. METHODS: Review of the scientific literature. RESULTS: In this article, we report an overview of CVST epidemiology, clinical presentation, diagnostics, disease pathophysiology, and management in the setting of COVID-19. CONCLUSION: CVST is an uncommon thromboembolic event with variable phenotypes and multiple etiologies. Neurologic complications can be severe, including significant visual deficits and death. Current observations suggest that the risk of CVST may be profoundly impacted by this novel COVID-19 pandemic, thus prompting increased attention to disease presentation, pathogenesis, and management.

Cathelicidin modulates synthesis of Toll-like Receptors (TLRs) 4 and 9 in colonic epithelium.

Cathelicidin are innate antimicrobial peptides with broad immunomodulatory functions; however, their role in regulating intestinal defenses is not well characterized. This study aimed to investigate the role of cathelicidin modulating expression of Toll-like receptors (TLRs) 4 and 9 in colonic epithelium in response to bacterial patterns. We demonstrated herein that intestinal epithelial cells, when primed by bacterial lipopolysaccharide (LPS), responded to cathelicidin by increased transcription and protein synthesis of TLR4. This cathelicidin-induced response required the interaction of LPS-TLR4 and activation of MAPK signalling pathways. However, cathelicidin blocked TLR9 responses induced by TLR9 ligand CpG oligodeoxynucleotide (CpG ODN) in these colonic epithelial cells. Modulations of TLRs triggered by cathelicidin in intestinal epithelium occurred mainly in the apical compartment of intestinal cells. Activation of TLR4 by ligands in combination with cathelicidin promoted CXCL8 chemokine secretion and epithelial antimicrobial defenses against Escherichia coli. We concluded that cathelicidin selectively modulated synthesis of TLR4 and 9 in intestinal epithelium, but only when cells were exposed to virulence factors, mostly from apical surfaces. Enhanced TLR4 expression promoted by cathelicidin in intestinal epithelium may be crucial for controlling enteric infectious diseases.

Rebamipide gargle in preventive management of chemo-radiotherapy induced oral mucositis.

BACKGROUND & OBJECTIVES: Oral mucositis is inflammation of mucosa of oral cavity which is an inevitable and acute side effect in patients undergoing chemoradiotherapy for head & neck cancer. Though many agents have been tried in prevention & treatment of oral mucositis, until date no single agent exists that is universally established to be effective. METHODS: 60 Patients diagnosed with Head & Neck cancer recruited for concurrent chemo-radiotherapy were assigned in a double blind fashion into 2 groups using computer based 1:1 ratio randomization. Subjects in Group 1 were given Rebamipide gargle while subjects in Group 2 were given Placebo gargle in similar colour coded bottles to gargle 6 times/day. Subjective assessment of oral mucositis was done by Numeric Rating Scale (NRS) and objective scoring according to RTOG system. RESULTS: All subjects in the Group 1 reported good treatment compliance but 4 subjects in Group 2 developed burning sensation to gargle and were excluded. Onset of oral mucositis was 3.5days earlier in Group 2 (mean=11.17) as compared to Group 1 (mean=14.63). At the end of chemo-radiotherapy, severity of oral mucositis was significantly lower in Group 1 (mean=1.97) than in Group 2 (mean=2.81). INTERPRETATION & CONCLUSION: Findings of this study revealed that Rebamipide gargle may be an effective means to prolong the onset of oral mucositis and may reduce the severity of oral mucositis in undergoing chemo-radiotherapy.

Hybrid ameloblastoma: An amalgam of rare and conventional ameloblastoma.

Ameloblastoma is a common benign odontogenic tumor with multiple histologic types. This case report describes an unusual type of ameloblastoma called "Hybrid Ameloblastoma" with features of both follicular and desmoplastic ameloblastoma in a 50-year-old female. This is a very rare form of ameloblastoma as <30 cases have been reported so far in literature. Though this rare form of ameloblastoma is only a histologic variant, it poses a great challenge to diagnosticians and thus to surgeons as there will be mismatch of biopsy reports at different sites in the same tumor thereby changing the treatment plan. This case report is one such example of diverse presentation of this ameloblastoma with conflicting histopathological diagnosis at initial biopsy and on surgical excision.

Association of Right Ventricular Pressure and Volume Overload with Non-Ischemic Septal Fibrosis on Cardiac Magnetic Resonance.

BACKGROUND: Non-ischemic fibrosis (NIF) on cardiac magnetic resonance (CMR) has been linked to poor prognosis, but its association with adverse right ventricular (RV) remodeling is unknown. This study examined a broad cohort of patients with RV dysfunction, so as to identify relationships between NIF and RV remodeling indices, including RV pressure load, volume and wall stress. METHODS AND RESULTS: The population comprised patients with RV dysfunction (EF<50%) undergoing CMR and transthoracic echo within a 14 day (5 ± 3) interval. Cardiac structure, function, and NIF were assessed on CMR. Pulmonary artery systolic pressure (PASP) was measured on echo. 118 patients with RV dysfunction were studied, among whom 47% had NIF. Patients with NIF had lower RVEF (34 ± 10 vs. 39 ± 9%; p = 0.01) but similar LVEF (40 ± 21 vs. 39 ± 18%; p = 0.7) and LV volumes (p = NS). RV wall stress was higher with NIF (17 ± 7 vs. 12 ± 6 kPa; p < 0.001) corresponding to increased RV end-systolic volume (143 ± 79 vs. 110 ± 36 ml; p = 0.006), myocardial mass (60 ± 21 vs. 53 ± 17 gm; p = 0.04), and PASP (52 ± 18 vs. 41 ± 18 mmHg; p = 0.001). NIF was associated with increased wall stress among subgroups with isolated RV (p = 0.005) and both RV and LV dysfunction (p = 0.003). In multivariable analysis, NIF was independently associated with RV volume (OR = 1.17 per 10 ml, [CI 1.04-1.32]; p = 0.01) and PASP (OR = 1.43 per 10 mmHg, [1.14-1.81]; p = 0.002) but not RV mass (OR = 0.91 per 10 gm, [0.69-1.20]; p = 0.5) [model χ2 = 21; p<0.001]. NIF prevalence was higher in relation to PA pressure and RV dilation and was > 6-fold more common in the highest, vs. the lowest, common tertile of PASP and RV size (p<0.001). CONCLUSION: Among wall stress components, NIF was independently associated with RV chamber dilation and afterload, supporting the concept that NIF is linked to adverse RV chamber remodeling.