Recent decline in carbon monoxide levels observed at an urban site in Ahmedabad, India.

Carbon monoxide (CO) is a prominent air pollutant in cities, with far-reaching implications for both local air quality and global atmospheric chemistry. The long-term change in atmospheric CO levels at a specific location is influenced by a complex interplay of local emissions, atmospheric transport, and photochemical processes, making it a subject of considerable interest. This study presents an 8-year analysis (2014-2021) of in situ CO observations using a cutting-edge laser-based analyzer at an urban site in Ahmedabad, western India. The long-term observations reveal a subtle trend in CO levels, masked by contrasting year-to-year variations, particular after 2018, across distinct diurnal time windows. Mid-afternoon (12:00-16:00 h) CO levels, reflecting background and regional conditions, remained relatively stable over the study period. In contrast, evening (18:00-21:00 h) CO levels, influenced by local emissions, exhibited substantial inter-annual variability without discernible trends from 2014 to 2018. However, post-2018, evening CO levels showed a consistent decline, predating COVID-19 lockdown measures. This decline coincided with the nationwide adoption of Bharat stage IV emission standards and other measures aimed at reducing vehicular emissions. The COVID-19 lockdown in 2020 further resulted in a noteworthy 29% reduction in evening CO levels compared to the pre-lockdown (2014-2019) period, highlighting the potential for substantial CO reduction through stringent vehicular emission controls. The observed long-term changes in CO levels do not align with the decreasing emission estimated by various inventories from 2014 to 2018, suggesting a need for improved emission statistics in Indian urban regions. This study underscores the importance of ongoing continuous CO measurements in urban areas to inform policy efforts aimed at controlling atmospheric pollutants.

Isolated adult ileocecal intussusception in sigmoid colon due to non-specific inflammation.

Adult ileocecal intussusception due to non-specific inflammation is a rare condition. Intussusception is the intestinal segment telescoping into the adjacent intestinal lumen. Typically, a pathological lesion is discovered with a high percentage of malignancy. Intussusception of the most common ileocolic kind includes the appendix, but it is uncommon for an appendix to serve as the lead point. The patient was admitted to the emergency department with a complaint of acute intestinal obstruction. After getting a diagnostic workup, an exploratory laparotomy was done, and the ileocecal and ascending colon segment was intussuscepted directly into the sigmoid colon. Transverse and descending colon were normal, and resection of necrosed intussuscepted bowel, primary repair of sigmoid colon with ileostomy with transverse colon as distal mucus fistula done, after the 3-month restoration of bowel continuity done, patient discharged and doing well. After the diagnosis of intussusception, the best surgical choice is in the hands of an experienced surgeon.

TC-SegNet: robust deep learning network for fully automatic two-chamber segmentation of two-dimensional echocardiography.

Heart chamber quantification is an essential clinical task to analyze heart abnormalities by evaluating the heart volume estimated through the endocardial border of the chambers. A precise heart chamber segmentation algorithm using echocardiography is essential for improving the diagnosis of cardiac disease. This paper proposes a robust two chamber segmentation network (TC-SegNet) for echocardiography which follows a U-Net architecture and effectively incorporates the proposed modified skip connection, Atrous Spatial Pyramid Pooling (ASPP) modules and squeeze and excitation modules. The TC-SegNet is evaluated on the open-source fully annotated dataset of cardiac acquisitions for multi-structure ultrasound segmentation (CAMUS). The proposed TC-SegNet obtained an average value of F1-score of 0.91, an average Dice score of 0.9284 and an IoU score of 0.8322 which are higher than the reference models used here for comparison. Further, Pixel error (PE) of 1.5109 which are significantly less than the comparison models. The segmentation results and metrics show that the proposed model outperforms the state-of-the-art segmentation methods.

A novel dataset and efficient deep learning framework for automated grading of renal cell carcinoma from kidney histopathology images.

Trends of kidney cancer cases worldwide are expected to increase persistently and this inspires the modification of the traditional diagnosis system to respond to future challenges. Renal Cell Carcinoma (RCC) is the most common kidney cancer and responsible for 80-85% of all renal tumors. This study proposed a robust and computationally efficient fully automated Renal Cell Carcinoma Grading Network (RCCGNet) from kidney histopathology images. The proposed RCCGNet contains a shared channel residual (SCR) block which allows the network to learn feature maps associated with different versions of the input with two parallel paths. The SCR block shares the information between two different layers and operates the shared data separately by providing beneficial supplements to each other. As a part of this study, we also introduced a new dataset for the grading of RCC with five different grades. We obtained 722 Hematoxylin & Eosin (H &E) stained slides of different patients and associated grades from the Department of Pathology, Kasturba Medical College (KMC), Mangalore, India. We performed comparable experiments which include deep learning models trained from scratch as well as transfer learning techniques using pre-trained weights of the ImageNet. To show the proposed model is generalized and independent of the dataset, we experimented with one additional well-established data called BreakHis dataset for eight class-classification. The experimental result shows that proposed RCCGNet is superior in comparison with the eight most recent classification methods on the proposed dataset as well as BreakHis dataset in terms of prediction accuracy and computational complexity.

Giant cell tumor of soft tissue: A case report.

INTRODUCTION AND IMPORTANCE: Giant cell tumor of soft tissue (GCT-ST) is a rare primary neoplasm of soft tissues. It usually involves superficial and deeper soft tissues of upper and lower extremities, followed by trunk. CASE PRESENTATION: A 28-year-old female, presented with a painful mass in left abdominal wall for three months. On examination, it measured 4 × 4 cm with ill-defined margins. CECT showed ill-defined enhancing lesion deep to muscle planes with possible invasion of peritoneal layer. Histopathology showed multinodular architecture with intervening fibrous septa and metaplastic bony tissue encasing the tumor. Tumor composed of round to oval mononuclear cells and osteoclast like multinucleated giant cells. Mitotic figures were eight per hpf. A diagnosis GCT-ST of anterior abdominal wall was made. Patient was treated with surgery followed by adjuvant radiotherapy. Patient is disease free at one year follow up. CLINICAL DISCUSSION: These tumors mostly involve extremities and trunk and usually presents as a painless mass. Clinical features depend upon the exact location of the tumor. Common differential diagnosis includes tenosynovial giant cell tumors and malignant giant cell tumors of soft tissue and GCT of Bone. CONCLUSION: Diagnosis of GCT-ST is difficult on cytopathology and radiology alone. Histopathological diagnosis should be done to rule out the malignant lesions. Complete surgical resection with clear resection margins is the mainstay of treatment. Adjuvant radiotherapy should be considered in case of incomplete resection. Long follow-up is necessary for these tumors as local recurrence and risk of metastasis cannot be predicted.

Atmospheric Boundary Layer Over Ahmedabad, Western Indian Region: Impact of COVID-19 Nationwide Lockdown.

The government of India imposed a nationwide lockdown to tackle the outbreak of COVID-19 in 2020. This period witnessed record low anthropogenic activity, which had severe socio-economic impacts but also had orthogonal effects on the ambient air quality of the atmosphere. This study focuses on the variations in the atmospheric boundary layer (ABL) over a western Indian urban region in the light of COVID-19. Continuous backscatter recorded by a ceilometer, stationed at Ahmedabad, was used in this study to monitor the ABL during the national lockdown (NLD) in 2020 and state restrictions in 2021, and compared with the control year of 2019. In parallel, improvement in air quality during the NLD was observed by the SAFAR air quality station at Ahmedabad, with decreased particulate matter concentrations. The ground-based observations were substantiated by the ERA5 reanalysis dataset. A decline in the ABL height was recorded during the NLD, which showed improvement in 2021 but which was shy of the ABL in 2019. This was correlated with rain events during the observational period, recorded by an automatic weather station.

Deep structured residual encoder-decoder network with a novel loss function for nuclei segmentation of kidney and breast histopathology images.

To improve the process of diagnosis and treatment of cancer disease, automatic segmentation of haematoxylin and eosin (H & E) stained cell nuclei from histopathology images is the first step in digital pathology. The proposed deep structured residual encoder-decoder network (DSREDN) focuses on two aspects: first, it effectively utilized residual connections throughout the network and provides a wide and deep encoder-decoder path, which results to capture relevant context and more localized features. Second, vanished boundary of detected nuclei is addressed by proposing an efficient loss function that better train our proposed model and reduces the false prediction which is undesirable especially in healthcare applications. The proposed architecture experimented on three different publicly available H&E stained histopathological datasets namely: (I) Kidney (RCC) (II) Triple Negative Breast Cancer (TNBC) (III) MoNuSeg-2018. We have considered F1-score, Aggregated Jaccard Index (AJI), the total number of parameters, and FLOPs (Floating point operations), which are mostly preferred performance measure metrics for comparison of nuclei segmentation. The evaluated score of nuclei segmentation indicated that the proposed architecture achieved a considerable margin over five state-of-the-art deep learning models on three different histopathology datasets. Visual segmentation results show that the proposed DSREDN model accurately segment the nuclear regions than those of the state-of-the-art methods.

High-resolution deep transferred ASPPU-Net for nuclei segmentation of histopathology images.

PURPOSE: Increasing cancer disease incidence worldwide has become a major public health issue. Manual histopathological analysis is a common diagnostic method for cancer detection. Due to the complex structure and wide variability in the texture of histopathology images, it has been challenging for pathologists to diagnose manually those images. Automatic segmentation of histopathology images to diagnose cancer disease is a continuous exploration field in recent times. Segmentation and analysis for diagnosis of histopathology images by using an efficient deep learning algorithm are the purpose of the proposed method. METHOD: To improve the segmentation performance, we proposed a deep learning framework that consists of a high-resolution encoder path, an atrous spatial pyramid pooling bottleneck module, and a powerful decoder. Compared to the benchmark segmentation models having a deep and thin path, our network is wide and deep that effectively leverages the strength of residual learning as well as encoder-decoder architecture. RESULTS: We performed careful experimentation and analysis on three publically available datasets namely kidney dataset, Triple Negative Breast Cancer (TNBC) dataset, and MoNuSeg histopathology image dataset. We have used the two most preferred performance metrics called F1 score and aggregated Jaccard index (AJI) to evaluate the performance of the proposed model. The measured values of F1 score and AJI score are (0.9684, 0.9394), (0.8419, 0.7282), and (0.8344, 0.7169) on the kidney dataset, TNBC histopathology dataset, and MoNuSeg dataset, respectively. CONCLUSION: Our proposed method yields better results as compared to benchmark segmentation methods on three histopathology datasets. Visual segmentation results justify the high value of the F1 score and AJI scores which indicated that it is a very good prediction by our proposed model.

Efficient deep learning architecture with dimension-wise pyramid pooling for nuclei segmentation of histopathology images.

Image segmentation remains to be one of the most vital tasks in the area of computer vision and more so in the case of medical image processing. Image segmentation quality is the main metric that is often considered with memory and computation efficiency overlooked, limiting the use of power hungry models for practical use. In this paper, we propose a novel framework (Kidney-SegNet) that combines the effectiveness of an attention based encoder-decoder architecture with atrous spatial pyramid pooling with highly efficient dimension-wise convolutions. The segmentation results of the proposed Kidney-SegNet architecture have been shown to outperform existing state-of-the-art deep learning methods by evaluating them on two publicly available kidney and TNBC breast H&E stained histopathology image datasets. Further, our simulation experiments also reveal that the computational complexity and memory requirement of our proposed architecture is very efficient compared to existing deep learning state-of-the-art methods for the task of nuclei segmentation of H&E stained histopathology images. The source code of our implementation will be available at https://github.com/Aaatresh/Kidney-SegNet.

LiverNet: efficient and robust deep learning model for automatic diagnosis of sub-types of liver hepatocellular carcinoma cancer from H&E stained liver histopathology images.

PURPOSE: Liver cancer is one of the most common types of cancers in Asia with a high mortality rate. A common method for liver cancer diagnosis is the manual examination of histopathology images. Due to its laborious nature, we focus on alternate deep learning methods for automatic diagnosis, providing significant advantages over manual methods. In this paper, we propose a novel deep learning framework to perform multi-class cancer classification of liver hepatocellular carcinoma (HCC) tumor histopathology images which shows improvements in inference speed and classification quality over other competitive methods. METHOD: The BreastNet architecture proposed by Togacar et al. shows great promise in using convolutional block attention modules (CBAM) for effective cancer classification in H&E stained breast histopathology images. As part of our experiments with this framework, we have studied the addition of atrous spatial pyramid pooling (ASPP) blocks to effectively capture multi-scale features in H&E stained liver histopathology data. We classify liver histopathology data into four classes, namely the non-cancerous class, low sub-type liver HCC tumor, medium sub-type liver HCC tumor, and high sub-type liver HCC tumor. To prove the robustness and efficacy of our models, we have shown results for two liver histopathology datasets-a novel KMC dataset and the TCGA dataset. RESULTS: Our proposed architecture outperforms state-of-the-art architectures for multi-class cancer classification of HCC histopathology images, not just in terms of quality of classification, but also in computational efficiency on the novel proposed KMC liver data and the publicly available TCGA-LIHC dataset. We have considered precision, recall, F1-score, intersection over union (IoU), accuracy, number of parameters, and FLOPs as metrics for comparison. The results of our meticulous experiments have shown improved classification performance along with added efficiency. LiverNet has been observed to outperform all other frameworks in all metrics under comparison with an approximate improvement of [Formula: see text] in accuracy and F1-score on the KMC and TCGA-LIHC datasets. CONCLUSION: To the best of our knowledge, our work is among the first to provide concrete proof and demonstrate results for a successful deep learning architecture to handle multi-class HCC histopathology image classification among various sub-types of liver HCC tumor. Our method shows a high accuracy of [Formula: see text] on the proposed KMC liver dataset requiring only 0.5739 million parameters and 1.1934 million floating point operations per second.

NucleiSegNet: Robust deep learning architecture for the nuclei segmentation of liver cancer histopathology images.

The nuclei segmentation of hematoxylin and eosin (H&E) stained histopathology images is an important prerequisite in designing a computer-aided diagnostics (CAD) system for cancer diagnosis and prognosis. Automated nuclei segmentation methods enable the qualitative and quantitative analysis of tens of thousands of nuclei within H&E stained histopathology images. However, a major challenge during nuclei segmentation is the segmentation of variable sized, touching nuclei. To address this challenge, we present NucleiSegNet - a robust deep learning network architecture for the nuclei segmentation of H&E stained liver cancer histopathology images. Our proposed architecture includes three blocks: a robust residual block, a bottleneck block, and an attention decoder block. The robust residual block is a newly proposed block for the efficient extraction of high-level semantic maps. The attention decoder block uses a new attention mechanism for efficient object localization, and it improves the proposed architecture's performance by reducing false positives. When applied to nuclei segmentation tasks, the proposed deep-learning architecture yielded superior results compared to state-of-the-art nuclei segmentation methods. We applied our proposed deep learning architecture for nuclei segmentation to a set of H&E stained histopathology images from two datasets, and our comprehensive results show that our proposed architecture outperforms state-of-the-art methods. As part of this work, we also introduced a new liver dataset (KMC liver dataset) of H&E stained liver cancer histopathology image tiles, containing 80 images with annotated nuclei procured from Kasturba Medical College (KMC), Mangalore, Manipal Academy of Higher Education (MAHE), Manipal, Karnataka, India. The proposed model's source code is available at https://github.com/shyamfec/NucleiSegNet.

Adsorption of Rhodamine 6G Dye on Binary System of Nanoarchitectonics Composite Magnetic Graphene Oxide Material.

Magnetic Graphene Oxide nanocomposite prepared by the co-precipitation method based on concept of nanoarchitectonics. In co-precipitation method, Graphene oxide converts into Magnetic graphene oxide nanocomposite with uniform deposition of Fe3O4 nano particles on the surface of Graphene oxide. Field Emission Scanning Electron Microscopy spectroscopy technique reveals the size (~2.5 nm) and uniformity of Fe3O4 nano particles on Graphene oxide surface. The other properties characterized by Scanning electron microscopy, Raman spectroscopy, X-ray powder diffraction, X-ray photoelectron spectroscopy and vibrating-sample magnetometer. For Adsorption process, time, temperature, dose of adsorbent, initial concentration of dye solution and pH factors are optimize for Rhodamine 6G dye. Kinetic data expressed by Pseudo first order model and Pseudo second order model. Langmuir, Freundlich and Temkin isotherms used to evaluate the adsorption isotherm of Rhodamine 6G onto the surface of Magnetic graphene oxide nanocomposite and thermodynamic parameters tell us about the nature of reaction.

Assessment of system characteristics of Gangotri glacier headwater stream.

The study examine the major ion chemistry and p(CO2) variations of Ganga headwater stream for a stretch of 18 km from Gangotri glacier snout at Gomukh to Gangotri for characterising the open and closed system conditions and its temporal variations. The study has been carried out at three locations along the stream continuum, at the glacier snout (0 km), Bhojwasa (4 km) and Gangotri (18 km) covering three consecutive melt seasons from the year 2014 to 2016 and reveals the persistence of closed system conditions along the stream stretch. The year 2014 and 2016 melt seasons experience high p(CO2) closed system conditions associated with high suspended sediment flux, whereas the year 2015 experienced low p(CO2) closed system condition associated with low sediment flux suggesting in-stream sulphide oxidation during high sediment flux years and results into low values of the C-ratio. On the other hand, the melt season with low sediment flux such as the year 2015 showed dominance of HCO3- over SO42- and higher C-ratio. The study shows that the headwater reach of River Bhagirathi from Gomukh to Gangotri has prevalence of high p(CO2) closed system characteristics associated with high sediment flux and dominance of SO42- during the seasonal peak flow. This is suggested as a unique characteristic of the meltwaters of the upper Bhagirathi basin.

A study about color normalization methods for histopathology images.

Histopathology images are used for the diagnosis of the cancerous disease by the examination of tissue with the help of Whole Slide Imaging (WSI) scanner. A decision support system works well by the analysis of the histopathology images but a lot of problems arise in its decision. Color variation in the histopathology images is occurring due to use of the different scanner, use of various equipments, different stain coloring and reactivity from a different manufacturer. In this paper, detailed study and performance evaluation of color normalization methods on histopathology image datasets are presented. Color normalization of the source image by transferring the mean color of the target image in the source image and also to separate stain present in the source image. Stain separation and color normalization of the histopathology images can be helped for both pathology and computerized decision support system. Quality performances of different color normalization methods are evaluated and compared in terms of quaternion structure similarity index matrix (QSSIM), structure similarity index matrix (SSIM) and Pearson correlation coefficient (PCC) on various histopathology image datasets. Our experimental analysis suggests that structure-preserving color normalization (SPCN) provides better qualitatively and qualitatively results in comparison to the all the presented methods for breast and colorectal cancer histopathology image datasets.

Loss of crop yields in India due to surface ozone: an estimation based on a network of observations.

Surface ozone is mainly produced by photochemical reactions involving various anthropogenic pollutants, whose emissions are increasing rapidly in India due to fast-growing anthropogenic activities. This study estimates the losses of wheat and rice crop yields using surface ozone observations from a group of 17 sites, for the first time, covering different parts of India. We used the mean ozone for 7 h during the day (M7) and accumulated ozone over a threshold of 40 ppbv (AOT40) metrics for the calculation of crop losses for the northern, eastern, western and southern regions of India. Our estimates show the highest annual loss of wheat (about 9 million ton) in the northern India, one of the most polluted regions in India, and that of rice (about 2.6 million ton) in the eastern region. The total all India annual loss of 4.0-14.2 million ton (4.2-15.0%) for wheat and 0.3-6.7 million ton (0.3-6.3%) for rice are estimated. The results show lower crop loss for rice than that of wheat mainly due to lower surface ozone levels during the cropping season after the Indian summer monsoon. These estimates based on a network of observation sites show lower losses than earlier estimates based on limited observations and much lower losses compared to global model estimates. However, these losses are slightly higher compared to a regional model estimate. Further, the results show large differences in the loss rates of both the two crops using the M7 and AOT40 metrics. This study also confirms that AOT40 cannot be fit with a linear relation over the Indian region and suggests for the need of new metrics that are based on factors suitable for this region.

Variability of atmospheric carbonyl sulfide at a semi-arid urban site in western India.

Atmospheric carbonyl sulfide (COS) is a major precursor for sulfate aerosols that play a critical role in climate regulation. Recent studies have highlighted the importance of COS measurements as a reliable means to constrain biospheric carbon assimilation. In a scenario of limited availability of COS data around the globe, we present gas-chromatographic measurements of atmospheric COS mixing ratios over Ahmedabad, a semi-arid, urban region in western India. These measurements, being reported for the first time over an Indian site, enable us to understand the diurnal and seasonal variation in atmospheric COS with respect to its natural, anthropogenic and photochemical sources and sinks. The annual mean COS mixing ratio over Ahmedabad is found to be 0.83±0.43ppbv, which is substantially higher than free tropospheric values for the northern hemisphere. Inverse correlation of COS with soil and skin temperature, suggests that the dry soil of the semi-arid study region is a potential sink for atmospheric COS. Positive correlations of COS with NO2 and CO during post-monsoon and the COS/CO slope of 0.78pptv/ppbv reveals influence of diesel combustion and tire wear. The highest concentrations of COS are observed during pre-monsoon; COS/CO2 slope of 44.75pptv/ppmv combined with information from air mass back-trajectories reveal marshy wetlands spanning over 7500km(2) as an important source of COS in Ahmedabad. COS/CO2 slopes decrease drastically (8.28pptv/ppmv) during post-monsoon due to combined impact of biospheric uptake and anthropogenic emissions.

Efficacy of nifedipine and alfuzosin in the management of distal ureteric stones: A randomized, controlled study.

INTRODUCTION: Stone disease is a significant and world-wide health problem. Recently, certain drugs have been used as a supplement to observation alone in an effort to improve spontaneous stone expulsion. We evaluated the efficacy of nifedipine and alfuzosin in the medical treatment of symptomatic, uncomplicated distal ureteral stones. MATERIALS AND METHODS: This was a randomized controlled prospective study to determine the efficacy of alfuzosin and nifedipine as an adjunctive medical therapy, to increases the stone-expulsion rates in distal ureteric calculus of size ≤10 mm. Investigators and patients were blinded to the randomization scheme. Patients were randomly divided into three equal groups of 35 patients each. Patients in Group I received tablet nifedipine 30 mg/day, Group II received alfuzosin 10 mg/day and Group III was the control group received tablet diclofenac sodium. The patient blood pressure, stone position on imaging, number of pain attacks, time of stone-expulsion, hospital re-admission and any adverse events were assessed. Patients were followed-up weekly and continued until the patient was rendered stone free or up to 28 days. Statistical analysis was performed and P < 0.05 was considered to be significant. RESULTS: Stone-expulsion was observed in 60%, 85.7% and 20% patients in Group I, II and III respectively. A statistically significant difference was noted in between Groups I versus III, Groups II versus III and Groups I versus II (P < 0.0001, P < 0.0001, and P < 0.0315 respectively). The mean number of pain attacks was 2.91 ± 1.01 for Group I, 1.8 ± 0.83 for Group II, and 2.82 ± 1.12 for Group III, which is statistical significant in Groups II versus III, and Groups I versus II (P < 0.001 and P < 0.001). Hospital re-admission rate was less in treatment groups when compare to control group (P < 0.0001). CONCLUSION: The use of alfuzosin and nifedipine as a medical expulsive therapy for distal ureteric stones proved to be safe and effective in term of increased stone-expulsion rate, reduced pain attacks and decrease hospital re-admissions.

Penile incarceration with encircling metallic objects: a study of successful removal.

OBJECTIVE: This article aimed to study the various treatment options according to the grading scale for penile incarceration. MATERIALS AND METHODS: A retrospective review, of all the case files of patients presented with penile incarceration with encircling metallic object was performed. The patients were analyzed for age, marital status, motive, object used, who applied it, trauma grade, duration of incarceration, removal technique, removal time, anesthesia used and recovery time. RESULT: A total of seven patients were identified. The average age was 46.71 years. Self-sexual gratification was the most common motive (five patients). Six patients presented within 24 hours. Grade II of injury was commonest type of injury seen in five patients.The technique of removal chosen was according to grade of penile injury, duration of incarceration and type of object used. Spinal anesthesia was used in most of the cases (five patients). CONCLUSION: Penile incarceration with encircling metallic objects is a rare presentation and requires urgent intervention according to trauma grade to prevent complications.

Variability of SO2, CO, and light hydrocarbons over a megacity in Eastern India: effects of emissions and transport.

The Indo-Gangetic plain (IGP) has received extensive attention of the global scientific community due to higher levels of trace gases and aerosols over this region. Satellite retrievals and model simulations show that, in particular, the eastern part IGP is highly polluted. Despite this attention, in situ measurements of trace gases are very limited over this region. This paper presents measurements of SO2, CO, CH4, and C2-C5 NMHCs during March 2012-February 2013 over Kolkata, a megacity in the eastern IGP, with a focus on processes impacting their levels. The mean SO2 and C2H6 concentrations during winter and post-monsoon periods were eight and three times higher compared to pre-monsoon and monsoon. Early morning enhancements in SO2 and several NMHCs during winter connote boundary layer effects. Daytime elevations in SO2 during pre-monsoon and monsoon suggest impacts of photo-oxidation. Inter-species correlations and trajectory analysis evince transport of SO2 from regional combustion sources (e.g., coal burning in power plants, industries) along the east of the Indo-Gangetic plain impacting SO2 levels at the site. However, C2H2 to CO ratio over Kolkata, which are comparable to other urban regions in India, show impacts of local biofuel combustions. Further, high levels of C3H8 and C4H10 evince the dominance of LPG/petrochemicals over the study location. The suite of trace gases measured during this study helps to decipher between impacts of local emissions and influence of transport on their levels.

Decadorial of a burn center in Central India.

INTRODUCTION: Burn injuries are a serious public health problem. In our study we have identified different epidemiological factors based on 10 years of our experience at a burn unit in central India and recommend some strategies to prevent burn injuries. MATERIALS AND METHODS: This is a retrospective analysis (2001-2010) of database from burn unit of S.S. Medical College, Rewa, India. RESULTS: 2499 patients with burn injury were analysed. 66.8% and 38.2% patients were females and males respectively, with a median age of 25 years. Flame (80.1%) was most common cause, home (96%) was most common place, traditional Indian stove (28.8%), kerosene lamp (26.7%), hot liquid (12.2%) and kerosene stove (10.4%) were common causes. Median Total Body Surface Area (TBSA) burn was 40.0%; females had significantly greater (P < 0.001) burn than males (median 50% vs 26.0%). High mortality (40.3%) seen; female sex (OR 3.22, 95% CI 2.65-3.92); young age (15-29 year) (OR 3.48, 95% CI 2.45-4.94); flame burn (OR 12.9, 95% CI 1.69-98.32); suicidal burn OR 6.82 95%CI 4.44-10.48) and TBSA > 76% (OR 3099, 95%CI 1302-7380) were significant risk factors for death. Median hospital stays was 8 days; shorter hospital stays seen among TBSA burn > 76% (2 days), suicidal intent (4 days), and those who expired (4 days). Septicemia (45.8%) and burn shock (41%) were the major cause for death. CONCLUSIONS: Cooking and lighting equipments are major cause of burn injury among females and young age group. Equipment modification to improve safety features and public awareness programs are necessary to reduce burn incidents.