Evaluating trends in groundwater quality of coastal alluvial aquifers of Eastern India for sustainable groundwater management.

Groundwater is a precious natural element which ensures global water, food, and environmental security in the twenty-first century. Systematic monitoring, sustainable utilization, preservation and remediation are critical aspects of efficient groundwater resource management. This study deals with the analysis of spatial variability and trend in groundwater chemistry as well as identification of possible contamination sources in a coastal alluvial basin of eastern India. Pre-monsoon season data of 14 groundwater-quality variables measured in 'leaky confined' and 'confined' aquifers were analyzed for ten years (2012-2021). Mann-Kendall (M-K) test with the Sen's Slope Estimator, Spearman Rank Order Correlation (SROC) and Innovative Trend Analysis (ITA) tests were employed to assess decadal (2012-2021) trends. The analysis of the results indicated that the 'critical' water-quality parameters exceeding the acceptable limits for drinking are TDS, EC, TH, pH, Mg2+, Na+, K+, Fe2+, HCO3-, Cl- and NO3-. Weak negative correlations between rainfall and groundwater elevation for both the aquifers reveal poor rainfall recharge into the aquifers. Therefore, a reduction in groundwater abstraction and augmentation of groundwater recharge is recommended. Trend analysis results indicated that the concentrations of TH, Mg2+ and Fe2+ exhibit significant increasing trends in the 'leaky confined aquifer'. In contrast, significant rising trends in TH, Mg2+, Na+, Fe2+, HCO3- and NO3- concentrations are identified in the 'confined aquifer'. Further, the SROC test could not detect the trends in groundwater quality in most blocks and for many parameters. On the other hand, the ITA test revealed significant trends in most of the parameters of the two aquifers in almost all the blocks. Trend magnitudes of the groundwater-quality parameters based on the Sen's Slope Estimator and the ITA test vary from -63.7 to 58.65 mg/L/year for TDS, -14 to 39.07 mg/L/year for TH, -1.49 to 4.83 mg/L/year for Mg2+, -7.14 to 22.96 mg/L/year for Na+, -0.32 to 0.44 mg/L/year for Fe2+, -8.33 to 20.75 mg/L/year for HCO3-, -26.52 to 31.01 mg/L/year for Cl- and 1.29 to 3.76 mg/L/year for NO3- over the study area. The results of M-K and ITA tests were found in agreement in all the blocks for both the aquifers. Groundwater contamination in both the aquifers can be attributed to weathering, geogenic processes, mineral dissolution, seawater intrusion, poor recharge pattern and injudicious anthropogenic activities. It is strongly recommended that concerned authorities urgently formulate efficient strategies for managing groundwater quality in the 'leaky confined' and 'confined' aquifers which serve as vital sources of drinking and irrigation water supplies in the study area.

Application of Hybrid ANN Techniques for Drought Forecasting in the Semi-Arid Region of India.

The intensity and frequency of diverse hydro-meteorological disasters viz., extreme droughts, severe floods, and cyclones have increasing trends due to unsustainable management of land and water resources, coupled with increasing industrialization, urbanization and climate change. This study focuses on the forecasting of drought using selected Artificial Neural Network (ANN)-based models to enable decision-makers to improve regional water management plans and disaster mitigation/reduction plans. Four ANN models were developed in this study, viz., one conventional ANN model and three hybrid ANN models: (a) Wavelet based-ANN (WANN), (b) Bootstrap based-ANN (BANN), and (c) Wavelet-Bootstrap based-ANN (WBANN). The Standardized Precipitation Evapotranspiration Index (SPEI), the best drought index identified for the study area, was used as a variable for drought forecasting. Three drought indices, such as SPEI-3, SPEI-6 and SPEI-12 respectively representing "short-term", "intermediate-term", and "long-term" drought conditions, were forecasted for 1-month to 3-month lead times for six weather stations over the study area. Both statistical and graphical indicators were considered to assess the performance of the developed models. For the hybrid wavelet model, the performance was evaluated for different vanishing moments of Daubechies wavelets and decomposition levels. The best-performing bootstrap-based model was further used for analysing the uncertainty associated with different drought forecasts. Among the models developed for drought forecasting for 1 to 3 months, the performances of the WANN and WBANN models are superior to the simple ANN and BANN models for the SPEI-3, SPEI-6, and SPEI-12 up to the 3-month lead time. The performance of the WANN and WBANN models is the best for SPEI-12 (MAE = 0.091-0.347, NSE = 0.873-0.982) followed by SPEI-6 (MAE = 0.258-0.593; NSE = 0.487-0.848) and SPEI-3 (MAE = 0.332-0.787, NSE = 0.196-0.825) for all the stations up to 3-month lead time. This finding is supported by the WBANN analyze uncertainties as narrower band width for SPEI-12 (0.240-0.898) as compared to SPEI-6 (0.402-1.62) and SPEI-3 (0.474-2.304). Therefore, the WBANN model is recommended for the early warning of drought events as it facilitates the uncertainty analysis of drought forecasting results.

A copula model of extracting DEM-based cross-sections for estimating ecological flow regimes in data-limited deltaic-branched river systems.

For operational flood control and estimating ecological flow regimes in deltaic branched-river systems with limited surveyed cross-sections, accurate river stage and discharge estimation using public domain Digital Elevation Model (DEM)-extracted cross-sections are challenging. To estimate the spatiotemporal variability of streamflow and river stage in a deltaic river system using a hydrodynamic model, this study demonstrates a novel copula-based framework to obtain reliable river cross-sections from SRTM (Shuttle Radar Topographic Mission) and ASTER (Advanced Spaceborne Thermal Emission and Reflection) DEMs. Firstly, the accuracy of the CSRTM and CASTER models was assessed against the surveyed river cross-sections. Thereafter, the sensitivity of the copula-based river cross-sections was evaluated by simulating river stage and discharge using MIKE11-HD in a complex deltaic branched-river system (7000 km2) of Eastern India having a network of 19 distributaries. For this, three MIKE11-HD models were developed based on surveyed cross-sections and synthetic cross-sections (CSRTM and CASTER models). The results indicated that the developed Copula-SRTM (CSRTM) and Copula-ASTER (CASTER) models significantly reduce biases (NSE>0.8; IOA>0.9) in the DEM-derived cross-sections and hence, are capable of satisfactorily reproducing observed streamflow regimes and water levels using MIKE11-HD. The performance evaluation metrics and uncertainty analysis indicated that the MIKE11-HD model based on the surveyed cross-sections simulates with higher accuracies (streamflow regimes: NSE>0.81; water levels: NSE>0.70). The MIKE11-HD model based on the CSRTM and CASTER cross-sections, reasonably simulates streamflow regimes (CSRTM: NSE>0.74; CASTER: NSE>0.61) and water levels (CSRTM: NSE>0.54; CASTER: NSE>0.51). Conclusively, the proposed framework is a useful tool for the hydrologic community to derive synthetic river cross-sections from public domain DEMs, and simulate streamflow regimes and water levels under data-scarce conditions. This modelling framework can be easily replicated in other river systems of the world under varying topographic and hydro-climatic conditions.

Efficacy of machine learning techniques in predicting groundwater fluctuations in agro-ecological zones of India.

In the 21st century, groundwater depletion is posing a serious threat to humanity throughout the world, particularly in developing nations. India being the largest consumer of groundwater in the world, dwindling groundwater storage has emerged as a serious concern in recent years. Consequently, the judicious and efficient management of vital groundwater resources is one of the grand challenges in India. Groundwater modeling is a promising tool to develop sustainable management strategies for the efficient utilization of this treasured resource. This study demonstrates a pragmatic framework for predicting seasonal groundwater levels at a large scale using real-world data. Three relatively powerful Machine Learning (ML) techniques viz., ANFIS (Adaptive Neuro-Fuzzy Inference System), Deep Neural Network (DNN) and Support Vector Machine (SVM) were employed for predicting seasonal groundwater levels at the country scale using in situ groundwater-level and pertinent meteorological data of 1996-2016. ANFIS, DNN and SVM models were developed for 18 Agro-Ecological Zones (AEZs) of India and their efficacy was evaluated using suitable statistical and graphical indicators. The findings of this study revealed that the DNN model is the most proficient in predicting seasonal groundwater levels in most AEZs, followed by the ANFIS model. However, the prediction ability of the three models is 'moderate' to 'very poor' in 3 AEZs ['Western Plain and Kutch Peninsula' in Western India, and 'Deccan Plateau (Arid)' and 'Eastern Ghats and Deccan Plateau' in Southern India]. It is recommended that groundwater-monitoring network and data acquisition systems be strengthened in India in order to ensure efficient use of modeling techniques for the sustainable management of groundwater resources.

Planning rainwater conservation measures using geospatial and multi-criteria decision making tools.

The assessment of rainwater-harvesting demand (RWHD) map and the identification of appropriate priority-based locations for rainwater-harvesting (RWH) and groundwater recharge structures are very crucial for the water managers, particularly in irrigation commands. This study addresses this challenge by using multi-criteria decision-making (MCDM) and geospatial techniques to present a novel and robust approach for generating RWHD map and identifying sites/zones for distinct RWH and groundwater recharge on a priority basis. Primary thematic layers such as existing irrigation water supply, irrigation demand, and groundwater potential were considered in this study for delineating RWHD zones. Further, sites suitable for RWH and groundwater recharge were identified using soil, slope, drainage network, and lineament thematic layers of the study area and they were prioritized. Four zones of rainwater demand were identified for the prioritization of RWH and groundwater structures: (a) "low" rainwater-harvesting demand zone (covering 3% of the total study area), (b) "moderate" rainwater-harvesting demand zone (40%), (c) "high" rainwater-harvesting demand zone (42%), and (d) "very high" rainwater-harvesting demand zone (15%). Moreover, 46 sites for check dams and 145 suitable sites for percolation tanks were identified, together with 253 ha area for groundwater recharge based on the priority of rainwater-harvesting demand. Integration of geospatial and MCDM techniques in conjunction with suitable thematic layers provides a helpful and realistic tool for large-scale planning and management of rainwater conservation measures.

Insight into the precipitation behavior of gridded precipitation data in the Sina basin.

This paper examines the performance of three gridded precipitation data sets, namely, Global Precipitation Climatology Centre (GPCC), Tropical Precipitation Measuring Mission (TRMM), and the Modern-Era Retrospective Analysis for Research and Applications (MERRA), for the duration of 25 years using 9 rain gauge data sets of the Sina basin, India. Statistical measures were employed to measure the performance in reproducing the rainfall and to assess its ability to detect the rainfall/no rainfall events, its structure, pattern, and spatio-temporal variations in the monthly and annual time scales. Compromise programming (CP) is used to rank the statistical performances of selected gridded precipitation data sets and found that TRMM attained first rank for the 8 stations followed by MERRA. The precipitation concentration index (PCI) checks the pattern and distribution of rainfall and found that observed data shows a uniform distribution in the basin; however, all the three gridded data sets failed to demonstrate uniform distribution. Categorical metrics like Probability of Detection (POD) and False Alarm Ratio (FAR) revealed that TRMM followed by MERRA and GPCC have good capabilities to detect rainfall/no rainfall events at different thresholds. All the trends drawn between observed data set and gridded precipitation data sets revealed that the MERRA data tend to underestimate and the TRMM and GPCC data tend to overestimate the values and intensities of rainfall data sets at most of the stations for both monthly and annual time scales. The data analysis of extreme rainfall points at monthly and annual time scales exhibits better performance of TRMM data sets. Overall, the TRMM data set is capable in replicating different characteristics of the observed data in the study area and could be used for hydro-meteorological and climatic studies when continuous observed data set is not available.

Identification of critical areas and evaluation of best management practices using SWAT for sustainable watershed management.

Identification of critical erosion-prone areas and selection of best management practices (BMPs) for watersheds are necessary to control their degradation by reducing sediment yields. The current research assesses and proposes a combination of potential BMPs for the Baitarani catchment in India using the Soil and Water Assessment Tool (SWAT). After the successful calibration and validation of the SWAT model developed for this catchment, the model was applied to evaluate the efficacy of eight agricultural and structural management practices and their combinations (three scenarios) for controlling sediment yields at watershed and sub-watershed levels as well as to assess the impacts of combined BMPs on water balance components. A combination of BMPs was found more effective in reducing sediment yields than individual BMPs. Comparative evaluation revealed that structural BMPs (0.66-70%) are better than agricultural BMPs (2-7%) in minimizing sediment yields at watershed level. The costly measures like grade and streambank stabilization structures can reduce the sediment yield up to 70% at the watershed level. The modeling results of the impacts of different combinations of BMPs (three scenarios) indicated that if all the eight BMPs are implemented, the reduction of sediment yields is increased by 76% and 80% at sub-watershed and watershed levels, respectively compared to the Base Scenario. Based on funds availability, a suitable combination of BMPs can be adopted by the concerned decision-makers to effectively reduce sediment yields in the study area. Further, the simulation results of BMPs impacts on water balance components revealed that the annual average surface runoff reduces by 4-14% in the three scenarios, while aquifer recharge (6.8-8.7%), baseflow (8-10.5%), and percolation (1.2-3.9%) increase due to implementation of BMPs. Overall, the findings of this study are very useful for ensuring sustainable management of land and other resources at a catchment scale.

Role and Outcome of Laparoscopic/Minimally Invasive Surgery for Variety of Colorectal Emergencies.

BACKGROUND: Recently, the laparoscopic or minimally invasive approach has become common practice for planned colorectal malignancies. Its use in the emergency setting is limited by various factors, including resource availability and surgical expertise. However, more recent evidence suggests a laparoscopic approach to colorectal emergencies, which is comparable with laparoscopic routine work, and often promising. In this study, authors have investigated the outcome of the laparoscopic approach in both benign and malignant colorectal emergencies. METHOD: Retrospective analysis of prospectively collected data (theater records, histology database, and discharge records) over the course of 9 years. The standard surgical approach included conventional laparoscopic and single-port technique (single-incision laparoscopic surgery). The outcome variables included in the final analysis were: success of the minimally invasive approach, conversion rate, postoperative complications, return to theater, and mortality. RESULTS: A total of 202 (males, 110 and females, 92) emergency patients with a median age of 59 years underwent surgery between December 2009 and 2019. The mean operating time was 169 minutes and median American Society of Anesthesiology grade III. Single-incision laparoscopic surgery was used in 19 patients (9.4%). The conversion to open surgery was 12.3% (n=25). The majority of them had primary anastomosis (n= 132, 65.3%).The complications from most to least frequent were: CONCLUSION:: The favorable results obtained in this study underline the theme that with the availability of resources and expertise, it is possible to offer minimal invasive approach to emergency colonic pathology.

Assessing groundwater quality for drinking water supply using hybrid fuzzy-GIS-based water quality index.

Groundwater is a vital source of freshwater in both urban and rural regions of the world. However, its injudicious abstraction and rapidly increasing contamination are posing a severe threat for sustainable water supply worldwide. Geographical Information System (GIS)-based groundwater quality evaluation using Groundwater Quality Index (GQI) has been proved to be a cost-effective tool for assessing groundwater quality and its variability at a larger scale. However, the conventional GQI approach is unable to deal with uncertainties involved in the assessment of environmental problems. To overcome this limitation, a novel hybrid framework integrating Fuzzy Logic with the GIS-based GQI is proposed in this study for assessing groundwater quality and its spatial variability. The proposed hybrid framework is demonstrated through a case study in a hard-rock terrain of Southern India using ten prominent groundwater-quality parameters measured during pre-monsoon and post-monsoon seasons. Two conventional GIS-based GQI models GQI-10 (using all the ten groundwater-quality parameters) and GQI-7 (using seven 'concerned/critical' groundwater-quality parameters) as well as hybrid Fuzzy-GIS-based GQI (FGQI) models (using seven critical parameters) were developed for the two seasons and the results were compared. The Trapezoidal membership functions classified the model input parameters into 'desirable', 'acceptable' and 'unacceptable' classes based on the experts' knowledge and water quality standards for drinking purposes. The concentrations of Ca2+, Mg2+, and SO42- in groundwater were found within the WHO desirable limits for drinking water throughout the year, while the concentrations of seven parameters (TDS, NO3--N, Na+, Cl-, K+, F- and Hardness) exceed their permissible limits during pre-monsoon and post-monsoon seasons. A comparative evaluation of GQI models revealed that the FGQI model predicts groundwater quality better than the conventional GQI-10 and GQI-7 models. GQI modeling results suggest that the groundwater of most of eastern and southern parts (∼60% in pre-monsoon season; ∼90% in post-monsoon season) of the study area is unsuitable for drinking. Further, the groundwater quality deteriorates during post-monsoon seasons compared to pre-monsoon seasons, which indicates an increased influx of contaminants from different industries, mining areas, waste disposal sites and agricultural fields during monsoon seasons. This finding calls for the strict enforcement of regulations for proper handling of effluents from various contamination sources in the study area. It is concluded that the fuzzy logic-based decision-making approach (FGQI) is more reliable and pragmatic for groundwater-quality assessment and analysis at a larger scale. It can serve as a useful tool for the water planners and decision makers in efficiently monitoring and managing groundwater quality at watershed or basin scales.

Simulation-Optimization for Conjunctive Water Resources Management and Optimal Crop Planning in Kushabhadra-Bhargavi River Delta of Eastern India.

Water resources sustainability is a worldwide concern because of climate variability, growing population, and excessive groundwater exploitation in order to meet freshwater demand. Addressing these conflicting challenges sometimes can be aided by using both simulation and mathematical optimization tools. This study combines a groundwater-flow simulation model and two optimization models to develop optimal reconnaissance-level water management strategies. For a given set of hydrologic and management constraints, both of the optimization models are applied to part of the Mahanadi River basin groundwater system, which is an important source of water supply in Odisha State, India. The first optimization model employs a calibrated groundwater simulation model (MODFLOW-2005, the U.S. Geological Survey modular ground-water model) within the Simulation-Optimization MOdeling System (SOMOS) module number 1 (SOMO1) to estimate maximum permissible groundwater extraction, subject to suitable constraints that protect the aquifer from seawater intrusion. The second optimization model uses linear programming optimization to: (a) optimize conjunctive allocation of surface water and groundwater and (b) to determine a cropping pattern that maximizes net annual returns from crop yields, without causing seawater intrusion. Together, the optimization models consider the weather seasons, and the suitability and variability of existing cultivable land, crops, and the hydrogeologic system better than the models that do not employ the distributed maximum groundwater pumping rates that will not induce seawater intrusion. The optimization outcomes suggest that minimizing agricultural rice cultivation (especially during the non-monsoon season) and increasing crop diversification would improve farmers' livelihoods and aid sustainable use of water resources.

Assessing Variability of Infiltration Characteristics and Reliability of Infiltration Models in a Tropical Sub-humid Region of India.

Infiltration process, which plays a paramount role in irrigation and drainage systems design, groundwater recharge and contamination evaluation, flood and drought management etc. is often controlled by several factors, among which land use/land cover (LULC) and soil physical properties are the prime factors. These factors lead to significant spatial variability of infiltration process, which poses a serious challenge for hydrologists and water managers. However, studies analyzing spatial variability and influence of both LULC and soil physical properties are scarce. To this end, grid-based infiltration experiments were carried out in a tropical sub-humid region of India to investigate spatial variability of infiltration characteristics, saturated hydraulic conductivity (Ksat) as well as to evaluate reliability of seven infiltration models in predicting infiltration behaviour and estimating Ksat. Additionally, uncertainty analysis was performed to quantify uncertainties associated with estimated Ksat for different LULC and soils. Results indicated that quasi-steady infiltration rate over the study area vary considerably with a majority of the area falling under 'low' and 'medium' infiltration categories. The infiltration process is greatly influenced by macro-pores and relatively low-permeable layers present at varying depths, typical features of lateritic vadose zones in tropical sub-humid regions, rather than its sole dependence on texture and LULC. Further, the Brutsaert model estimates Ksat with the highest accuracy and least uncertainty followed by Swartzendruber and Horton models. Except the Brutsaert model, other models are sensitive to a particular LULC. Overall, it is inferred that the Brutsaert and Swartzendruber models are robust and more reliable in predicting infiltration behavior and Ksat for the area. Findings of this study including quantification of spatial variability of important soil properties are useful for understanding detailed hydrological processes in the region and thereby, ensuring better planning and management of recurring floods and drought problems of the region.

Sensitivity and Specificity of the Neutrophil-lymphocyte Ratio in the Diagnosis of Acute Appendicitis.

OBJECTIVE: Acute appendicitis is a common surgical emergency; however, its misdiagnosis resulting in negative appendicectomy is not uncommon. Novel diagnostic methods will help reduce the rate of negative appendicectomy. We hypothesise that the neutrophil-lymphocyte ratio (NLR) will increase upon peritoneal involvement by inflammation, as it does in severe disease. METHODS: We conducted a retrospective analysis on prospectively collected data for all emergency appendicectomy patients during the study period. We studied blood results at the time of presentation, and histology of the removed appendices. Receiver Operating Characteristics were calculated to classify histologically normal versus inflamed appendices and moderate versus severe disease. Moderate disease was that confined to the sub-serosal layers, while severe disease involved the serosa and beyond. RESULTS: A total of 372 patients underwent emergency appendicectomy, 254 (78.4%) of which subsequently had acute appendicitis on histology. Sixty-five (25.6%) and 189 (74.4%) patients had moderate and severe disease, respectively. The median age was 27 years (range 16-84). In diagnosing acute appendicitis, the cut-off value of the NLR was 4.2, while the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were 79.5%, 67.0%, 89.8%, and 47.5%, respectively. In identifying the moderate versus severe disease, the cut-off value was 9.7, while the sensitivity, specificity, PPV and NPV were 71.4%, 53.8%, 85.4% and 43.7%, respectively. CONCLUSIONS: Inflammatory markers are useful adjuncts to history, examination, and radiological investigations in making the diagnosis of appendicitis. However, due to low sensitivity and specificity, they cannot be used alone. Calculation of the NLR has no additional benefit over the neutrophil count in diagnosis, or in distinguishing moderate and severe disease.

Simulation of regional irrigation requirement with SWAT in different agro-climatic zones driven by observed climate and two reanalysis datasets.

Irrigation water is one of the most substantial water uses worldwide. Thus, global simulation studies about water availability and demand typically include irrigation. Nowadays, regional scale is of major interest for water resources management but irrigation lacks attention in many catchment modelling studies. This study evaluated the performance of the agro-hydrological model SWAT (Soil and Water Assessment Tool) for simulating streamflow, evapotranspiration and irrigation in four catchments of different agro-climatic zones at meso-scale (Baitarani/India: Subtropical monsoon; Ilmenau/Germany: Humid; Itata/Chile: Mediterranean; Thubon/Vietnam: Tropical). The models were calibrated well with Kling-Gupta Efficiency (KGE) varying from 0.74-0.89 and percentage bias (PBIAS) from 5.66-6.43%. The simulated irrigation is higher when irrigation is triggered by soil-water deficit compared to plant-water stress. The simulated irrigation scheduling scenarios showed that a significant amount of water can be saved by applying deficit irrigation (25-48%) with a small reduction in annual average crop yield (0-3.3%) in all climatic zones. Many catchments with a high share of irrigated agriculture are located in developing countries with a low availability of input data. For that reason, the application of uncorrected and bias-corrected National Centers for Environmental Prediction (NCEP) and ERA-interim (ERA) reanalysis data was evaluated for all model scenarios. The simulated streamflow under bias-corrected climate variables is close to the observed streamflow with ERA performing better than NCEP. However, the deviation in simulated irrigation between observed and reanalysis climate varies from -25.5-45.3%, whereas the relative irrigation water savings by deficit irrigation could be shown by all climate input data. The overall variability in simulated irrigation requirement depends mainly on the climate input data. Studies about irrigation requirement in data scarce areas must address this in particular when using reanalysis data.

Unnecessary surgery can be avoided by judicious use of PET/CT scanning in colorectal cancer patients.

AIM: This study aims to determine the role of positron emission tomography (PET)/computed tomography (CT) in changing the management plan in patients with metastatic or recurrent colorectal cancer (CRC) and to evaluate the role of PET/CT in patients with an unexplained rise in carcinoembryonic antigen (CEA). MATERIALS AND METHODS: A total of 60 consecutive patients with CRC, who had PET/CT, were identified between 2008 and 2010. All patients had CT scans prior to the PET/CT. Data were collected from clinic letters, CT and PET CT reports and pathology results and cross-checked with the patient's notes. RESULTS: Patients were aged between 43 and 85 years [33 males, 27 females]. CEA was raised in 37 patients and normal in 23. Results of PET/CT were compared with that of CT scan and 33 out of the 60 patients (55%) had PET/CT results which were different to that of CT scan and 27 patients (45%) had similar PET/CT and CT results. PET scan appropriately altered the management in 23/60 patients (38%) and avoided unnecessary surgery in 14 patients. PET/CT had a sensitivity of 86% and specificity of 84%. In patients with an unexplained rise in CEA, PET/CT was positive in only one out of ten (10%) patients. CONCLUSION: PET/CT is valuable in deciding the management outcome in patients with metastatic or recurrent colorectal cancer. Unnecessary surgery might be avoided by careful use of PET/CT scanning in colorectal cancer patients. PET/CT might not be of value in patients with an unexplained rise in CEA.

Dukes C colorectal cancer: is the metastatic lymph node ratio important?

PURPOSE: Although the regional lymph node status is essential for staging of colorectal cancer, the importance of the total number of collected nodes remains controversial. Our aim was to examine the impact of the metastatic lymph node ratio (LNR) on the survival of patients with Dukes C colorectal cancer. METHODS: All patients with Dukes C histology were selected from a prospectively collected database of all colorectal cancers resected between 1997 and 2007 at our institution. Demographic, histopathological and adjuvant treatment data were collected. The total number of positive lymph nodes was divided by the total number of lymph nodes examined to calculate the LNR. Patients were categorised into LNR groups 1 to 5 according to cut-off points: ≤0.1, 0.21, 0.36, 0.6 and ≥0.61. Survival from the date of operation was calculated using Kaplan-Meier estimates. Multivariate analysis was performed to identify those factors influencing survival. RESULTS: Of 1,098 patients who underwent colorectal cancer resections, 41% were staged as Dukes C. Sixty-four percent of patients received chemotherapy. The median number of lymph nodes harvested and positive for tumour were 11 (range 1-52) and 4 (range 1-28), respectively. In patients who received chemotherapy, 5-year survival was 69.3% for LNR 1 and 23.6% for LNR 5. When no chemotherapy was given, the 5-year survival was 43.1% for LNR 1 and 8.7% for LNR 5. CONCLUSIONS: Current evaluation of positive lymph nodes may not accurately stage Dukes C colorectal cancer. The assessment of the LNR is a useful prognostic method in this heterogenous group of patients.

Lower lip numbness in a patient with colorectal cancer.

Metastatic lesions to the mandible are rare, comprising less than 1% of all malignancies. A 75-year-old gentleman presented to ENT outpatient with a 3-week history of numbness over his lower lip on the right side followed by a rapidly growing swelling in his right mandibular region. The patient was diagnosed with an obstructing sigmoid tumour with metastasis to the liver and retroperitoneal adenopathy, 5 months ago. A colonic stent was inserted for the sigmoid tumour and patient was undergoing palliative chemotherapy. CT scan of the mandibular region showed mass lesion invading the ascending ramus of mandible and involving the right inferior alveolar nerve. Trucut biopsy confirmed metastatic adenocarcinoma.

GIS-based assessment and characterization of groundwater quality in a hard-rock hilly terrain of Western India.

The growing population, pollution, and misuse of freshwater worldwide necessitate developing innovative methods and efficient strategies to protect vital groundwater resources. This need becomes more critical for arid/semi-arid regions of the world. The present study focuses on a GIS-based assessment and characterization of groundwater quality in a semi-arid hard-rock terrain of Rajasthan, western India using long-term and multi-site post-monsoon groundwater quality data. Spatio-temporal variations of water quality parameters in the study area were analyzed by GIS techniques. Groundwater quality was evaluated based on a GIS-based Groundwater Quality Index (GWQI). A Potential GWQI map was also generated for the study area following the Optimum Index Factor concept. The most-influential water quality parameters were identified by performing a map removal sensitivity analysis among the groundwater quality parameters. Mean annual concentration maps revealed that hardness is the only parameter that exceeds its maximum permissible limit for drinking water. GIS analysis revealed that sulfate and nitrate ions exhibit the highest (CV > 30%) temporal variation, but groundwater pH is stable. Hardness, EC, TDS, and magnesium govern the spatial pattern of the GWQI map. The groundwater quality of the study area is generally suitable for drinking and irrigation (median GWQI > 74). The GWQI map indicated that relatively high-quality groundwater exists in northwest and southeast portions of the study area. The groundwater quality parameter group of Ca, Cl, and pH were found to have the maximum value (6.44) of Optimum Index factor. It is concluded that Ca, Cl, and pH are three prominent parameters for cost-effective and long-term water quality monitoring in the study area. Hardness, Na, and SO(4), being the most-sensitive water quality parameters, need to be monitored regularly and more precisely.

Rectal metastasis from breast cancer: an interval of 17 years.

Metastasis to gastrointestinal (GI) tract from breast cancer is rare. Commonly affected organ in GI tract is stomach, followed by colon and then rectum. The authors report a case of a 61-year-old woman who had a mastectomy for lobular carcinoma of the breast 17 years ago and was referred to colorectal clinic with increased frequency of stools. Colonoscopy showed a stricture in the rectum, but biopsy was inconclusive. As she was symptomatic, she had a Hartmann's resection 5 months after she initially presented to the clinic. Histopathology of the resected specimen showed it to be metastasis from lobular carcinoma of the breast. Awareness of potential long delays in the presentation of metastatic breast cancer especially lobular carcinoma helps in the earlier diagnosis and clinical management.