Heavy metals and trace minerals in commonly available shark species from North East Arabian Sea: A human health risk perspective.

Shark is a seafood commodity that is a good source of minerals and accumulates heavy metals and trace elements through biomagnification, which can pose health risk if taken above the permissible limit. A study was conducted on commonly landed eleven shark species (Scoliodon laticaudus, Rhizopriodon oligolinx, Sphyrna lewini (CR), Carcharhinus macloti, Carcharinus limbatus, Carcharhinus amblyrhynchoides, Carcharhinus sorrah, Carcharinus falciformes(VU), Glaucostegus granulatus, Chiloscyllium arabicum, Loxodon macrorhinus) and analyzed for their heavy metal content, Hazard Index, Total Hazard Quotient, Metal Pollution Index, and also calculated the health risk associated with the consumption. Most of the heavy metals and trace minerals were found to be within the acceptable limit. The Targeted Hazard Quotient (THQ) and the Hazard Index (HI) of all the species except two were less than 1 (HI ≤ 1.0). The Metal Pollution Index (MPI) is showing either no impact or very low contamination. An overall study on hazard identification and health risk characterization in terms of heavy metals shows contamination of some heavy metals in sharks, but there is no potential human health risk associated with consumption.

External Validation of Robust Radiomic Signature to Predict 2-Year Overall Survival in Non-Small-Cell Lung Cancer.

Lung cancer is the second most fatal disease worldwide. In the last few years, radiomics is being explored to develop prediction models for various clinical endpoints in lung cancer. However, the robustness of radiomic features is under question and has been identified as one of the roadblocks in the implementation of a radiomic-based prediction model in the clinic. Many past studies have suggested identifying the robust radiomic feature to develop a prediction model. In our earlier study, we identified robust radiomic features for prediction model development. The objective of this study was to develop and validate the robust radiomic signatures for predicting 2-year overall survival in non-small cell lung cancer (NSCLC). This retrospective study included a cohort of 300 stage I-IV NSCLC patients. Institutional 200 patients' data were included for training and internal validation and 100 patients' data from The Cancer Image Archive (TCIA) open-source image repository for external validation. Radiomic features were extracted from the CT images of both cohorts. The feature selection was performed using hierarchical clustering, a Chi-squared test, and recursive feature elimination (RFE). In total, six prediction models were developed using random forest (RF-Model-O, RF-Model-B), gradient boosting (GB-Model-O, GB-Model-B), and support vector(SV-Model-O, SV-Model-B) classifiers to predict 2-year overall survival (OS) on original data as well as balanced data. Model validation was performed using 10-fold cross-validation, internal validation, and external validation. Using a multistep feature selection method, the overall top 10 features were chosen. On internal validation, the two random forest models (RF-Model-O, RF-Model-B) displayed the highest accuracy; their scores on the original and balanced datasets were 0.81 and 0.77 respectively. During external validation, both the random forest models' accuracy was 0.68. In our study, robust radiomic features showed promising predictive performance to predict 2-year overall survival in NSCLC.

Clinical Concept-Based Radiology Reports Classification Pipeline for Lung Carcinoma.

Rising incidence and mortality of cancer have led to an incremental amount of research in the field. To learn from preexisting data, it has become important to capture maximum information related to disease type, stage, treatment, and outcomes. Medical imaging reports are rich in this kind of information but are only present as free text. The extraction of information from such unstructured text reports is labor-intensive. The use of Natural Language Processing (NLP) tools to extract information from radiology reports can make it less time-consuming as well as more effective. In this study, we have developed and compared different models for the classification of lung carcinoma reports using clinical concepts. This study was approved by the institutional ethics committee as a retrospective study with a waiver of informed consent. A clinical concept-based classification pipeline for lung carcinoma radiology reports was developed using rule-based as well as machine learning models and compared. The machine learning models used were XGBoost and two more deep learning model architectures with bidirectional long short-term neural networks. A corpus consisting of 1700 radiology reports including computed tomography (CT) and positron emission tomography/computed tomography (PET/CT) reports were used for development and testing. Five hundred one radiology reports from MIMIC-III Clinical Database version 1.4 was used for external validation. The pipeline achieved an overall F1 score of 0.94 on the internal set and 0.74 on external validation with the rule-based algorithm using expert input giving the best performance. Among the machine learning models, the Bi-LSTM_dropout model performed better than the ML model using XGBoost and the Bi-LSTM_simple model on internal set, whereas on external validation, the Bi-LSTM_simple model performed relatively better than other 2. This pipeline can be used for clinical concept-based classification of radiology reports related to lung carcinoma from a huge corpus and also for automated annotation of these reports.

Dietary L-Tryptophan potentiates non-specific immunity in Labeo rohita fingerlings reared under elevated temperature.

At present environmental scenario global climate change is a reality and its affect all living organism including fish. The aquatic ecosystem is the most affected system as it is the biggest sink for global warming and elevated temperature and obviously affects all the aquatic life forms. With this hypothesis an experiment was conducted to evaluate the effect of elevated temperature on Labeo rohita fingerlings and potential of dietary L-tryptophan (TRP) in mitigating the effects elevated temperature and enhancing the non-specific immunity. Seven hundred and twenty fishes were randomly distributed in three different thermal groups each with three replicates. The thermal groups were ambient temperature (26 °C), 34 and 38 °C. Then each thermal groups were fed with four different formulated diets containing 0. 0.36%, 0.72% and 1.44% TRP. The effect of dietary TRP supplementation was studied on stress responses, such as cortisol, blood glucose, histopathological changes in liver and kidney and immuno-hematological changes such as red blood cell count (RBC), haemoglobin (Hb), white blood cell count (WBC), lysozyme, nitroblue tetrazolium (NBT), total serum protein, albumin, globulin and albumin-globulin ratio. Subsequently the treated fish were subjected to challenge test with Aeromonas hydrophila. In the present study, primary stress markers were noticeably (p < 0.01) elevated with temperature stress and levels were reduced with nutritional supplementation of TRP. Similarly, immuno-hematological parameters were altered with the exposure of temeparture stress and got improved with dietary TRP supplementation. Results of the present study suggest that dietary supplementation of 1.44% tryptophan has definitive role in the mitigation of temperature stress and gives protection against bacterial infection to L. rohita.

Dietary zinc promotes immuno-biochemical plasticity and protects fish against multiple stresses.

The abiotic and biotic stress is an episode that effect on regulatory, neuro-endocrine and immune systems of animals including fish. The stress creates stimulatory and suppressive of immune system resulting in increases the incidence of infection. In view of these points, we have conducted an experiment to mitigate the stress through a nutritional approach through Zinc (Zn) supplementation in Pangasius hypophthalmus (initial weight-3.65 ± 0.75 g). Three isocaloric and isonitrogenous diets with graded levels of zinc 0, 10 and 20 mg/kg were prepared and fed to seven different groups with each in triplicate. The experimental group as follows as normal water with control diet (Ctr/Ctr), lead (Pb) exposed and fed with control diet (Ctr/Pb), control diet and exposed to Pb and temperature (Ctr/Pb-T), Zn 10 mg/kg fed without stressors (Zn- 10 mg/kg), Zn 20 mg/kg fed without stressors (Zn-20 mg/kg), Zn 10 mg/kg fed and Pb and temperature exposed (Pb-T/Zn 10 mg/kg) and Zn 20 mg/kg fed and exposed to Pb and temperature (Pb-T/Zn 20 mg/kg). The Pb in treated water was maintained at the level of 1/20th of LC50 (4 ppm) and temperature at 34 °C in exposure groups. The neutraceuticals role of dietary Zn was studied in terms of antioxidative enzymes (catalase, superoxide dismutase, glutathione-S-transferase), stress markers (Heat shock protein 70, cortisol, acetylcholine esterase, blood glucose, Vitamin C), immunological parameters (Total protein, albumin, globulin, A/G ratio and NBT) and subsequent challenge with Aeromonas veronii biovar sobria. The antioxidative enzymes, stress markers, albumin were significantly (p < 0.01) elevated, brain AChE and immuno-hematological parameters were significantly (p < 0.01) decreased due to lead (Pb) and temperature exposure. The relative survival (%) was reduced due to the concurrent effect of Pb, high temperature stress and bacterial challenge. Zinc at the rate of 10 and 20 mg/kg was found to be restore the biochemical and immunological parameters against concurrent exposure to lead (Pb), temperature and pathogenic infection. Results obtained in the present study indicate that supplementation of 10 and 20 mg/kg of Zn in the diet has a definitive role in the mitigation of lead (Pb) and temperature exposure along with pathogenic infection in P. hypophthalmus.

Assessment of microbial quality of fish processing industrial effluent in bar-mouth at Bhidia landing site, Veraval, Gujarat, India.

The present study was carried out to assess the microbial quality of fish processing industries effluent at Bhidia bar-mouth, Veraval, Gujarat during April, 2012 to March 2013. The total viable bacterial count (TVBC), total Enterobacteriaceae count, E. coli count (EC), Staphylococcus aureus and Fecal Streptococcal count in effluent ranged from 3.0 x 10(-1) to 6.8 x 10(6), 9.0 x 10(1) to 2.9 x 10(4), 0 to 0. 5 x 10(4), 0 to 0. 4 x 102 and 0.3 x 10(1) to 0. 1 x 10(4) cfu.(-1)respectively. Significantly higher load of TEC, E. coli, S.aureus, Fecal Streptococci, Total coliforms and Fecal coliforms were higher during summer whereas, TVBC was higher in the month of Sept.-Oct. Furthermore, the total coliform and fecal coliform counts were found to be higher with 1400+ /100 ml MPN value throughout the year of the study, except in the month of August. Overall occurrence of pathogenic strains of E. coli, S. aureus and Fecal streptococci were 41.67%, 25.00% and 66.67% respectively during this period. The antibiogram of the isolated E. coli isolates show that almost 50% were resistant to Cefazidime/Clavulanic acid (CAC), Amoxyclav (AMC), Ciprofloxacin (CIF) and Ampicillin (AMP). The present study indicated that the effluent of fish processing industry was heavily contaminated with E. coli, S. aureus and Fecal Streptococci which confirmed improper treatment of fish processing effluent. Moreover, the precedence of antibiotic resistant E. coli may pose threat to public health safety.

Chronic toxicity of sediment-bound triclosan on freshwater walking catfish Clarias magur: Organ level accumulation and selected enzyme biomarker responses.

Triclosan (TCS) is a biocide widely used in personal care and medicinal products. TCS persists in sediments and has been detected worldwide, making sediments a vital route of TCS exposure to aquatic organisms. This experiment explored the bioaccumulation and toxicological effects of TCS-contaminated sediment. The study revealed that the half-life of TCS in the sediment-water system was 21.52 days. Exposure of Clarias magur juveniles to 0.4 and 0.8 mg kg-1 TCS-spiked sediment resulted in high Biota-Sediment Accumulation Factor (BSAF) with the highest bioaccumulation in the liver (29.62-73.61 mg kg-1), followed by gill (9.22-17.57 mg kg-1), kidney (5.04-9.76 mg kg-1), muscle (2.63-4.87 mg kg-1) and brain (1.53-3.20 mg kg-1). Furthermore, a concentration-dependent increase in oxidative stress biomarkers such as superoxide dismutase (SOD), catalase (CAT) and glutathione-S-transferase (GST) was documented during 45 days of exposure in gill, liver, kidney, muscle, and brain tissues of exposed fish. A similar increasing trend was also recorded for liver transaminase enzymes such as glutamic oxaloacetic transaminase (GOT) and glutamic pyruvic transaminase (GPT) during the experimental period compared to control. Serum biochemical analysis revealed a significant time and concentration-dependent increase in serum glucose, serum GOT, and serum GPT, while serum total protein and albumin decreased significantly during exposure. These findings demonstrate high bioaccumulative and toxic nature of TCS in fish, promoting multiple physiological and biochemical dysfunctions through sediment exposure. The study underscores the urgent need for strengthened regulations and robust monitoring of triclosan across various environmental matrices, including sediment, to mitigate the detrimental impacts of TCS effectively.

Ecotoxicological risk assessment of triclosan, an emerging pollutant in a riverine and estuarine ecosystems: A comparative study.

Triclosan (TCS), an antibacterial biocide, pervades water and sediment matrices globally, posing a threat to aquatic life. In densely populated cities like Mumbai, rivers and coastal bodies demand baseline TCS data for ecotoxicological assessment due to the excessive use of personal care products comprising TCS. This pioneering study compares spatiotemporal TCS variations and risks in freshwater and marine ecosystems employing multivariate analysis of physicochemical parameters. Over five months (January to May 2022), Mithi River exhibited higher TCS concentrations (water: 1.68 µg/L, sediment: 3.19 µg/kg) than Versova Creek (water: 0.49 µg/L, sediment: 0.69 µg/kg). Principal component analysis revealed positive correlations between TCS and physicochemical parameters. High-risk quotients (>1) underscore TCS threats in both water bodies. This study furnishes crucial baseline data, emphasizing the need for effective treatment plans for TCS in effluent waters released into the adjacent aquatic systems.

PRRT with Lu-177 DOTATATE in Treatment-Refractory Progressive Meningioma: Initial Experience from a Tertiary-Care Neuro-Oncology Center.

PURPOSE: Refractory and/or recurrent meningiomas have poor outcomes, and the treatment options are limited. Peptide receptor radionuclide therapy (PRRT) has been used in this setting with promising results. We have documented our experience of using intravenous (IV) and intra-arterial (IA) approaches of Lu-177 DOTATATE PRRT. METHODS: Eight patients with relapsed/refractory high-grade meningioma received PRRT with Lu-177 DOTATATE by IV and an IA route. At least 2 cycles were administered. Time to progression was calculated from the first PRRT session to progression. The response was assessed on MRI using RANO criteria, and visual analysis of uptake was done on Ga-68 DOTANOC PET/CT. Post-therapy dosimetry calculations for estimating the absorbed dose were performed. RESULTS: Median time to progression was 8.9 months. One patient showed disease progression, whereas seven patients showed stable disease at 4 weeks following 2 cycles of PRRT. Dosimetric analysis showed higher dose and retention time by IA approach. No significant peri-procedural or PRRT associated toxicity was seen. CONCLUSION: PRRT is a safe and effective therapeutic option for relapsed/refractory meningioma. The IA approach yields better dose delivery and should be routinely practised.

Large Anorectal Venous Malformation Treated With Microfoam Therapy.

Deficiency of smooth muscle cells can lead to dysfunction and engorgement of blood vessels termed as hemangioma, arteriovenous malformations, and venous malformations (VMs). Anorectal VM is a rare disease. It can present with massive hematochezia. An optimal treatment of anorectal VMs has not been defined. Surgery is an option if the lesion can be resected completely. Endoscopic injection sclerotherapy has been reported to be effective in treating small colorectal VMs. However, it has rarely been described in the treatment of large VMs. In this study, we describe a rare case of large anorectal VMs treated with microfoam sclerotherapy.

Analysis of image quality by regulating beta function of BSREM reconstruction algorithm and comparison with conventional reconstructions in carcinoma breast studies of PET CT with BGO detector.

BACKGROUND: The study aimed to evaluate the beta penalization factor of the BSREM reconstruction algorithm on a five-ring BGO-based PET CT system and compared it with conventional reconstructions. METHODS: Retrospective study involves 30 breast cancer patient data of 18F-fluorodeoxyglucose ( 18 F-FDG) PET CT for reconstruction with OSEM, OSEM + PSF, and BSREM under variable ß factors ranging from 200 to 600 in the steps of 50. Liver noise, lesion SUVmax, SBR, and SNR for each reconstruction were calculated. Quantitative parameters of each beta factor of BSREM were compared with OSEM and OSEM + PSF, using the Wilcoxon sign rank test with Bonferroni correction, a value of P < 0.002 was considered statistically significant. Visual scoring by two readers was also evaluated. RESULTS: Thirty lesions of mean size 1.91 ± 0.58 cm range (0.7-3.6 cm) were identified. Liver noise and SBR were reduced, whereas SNR was increased with an increasing ß value of BSREM. In comparison with OSEM, liver noise was not significantly different from ß200 and ß250. SNR of OSEM was significantly lower than any other ß factors and SBR of ß factor less than 500 was significantly higher than OSEM. In comparison with OSEM + PSF, liver noise was not significantly different from ß400 and ß350-500 do not show a significant difference in SNR and SBR compared with OSEM + PSF. ß350 scored highest under visual scoring with a moderate agreement. CONCLUSION: The study quantitatively indicates the optimum beta range of ß250-450 and the qualitative evaluation indicates that ß350 is an optimum beta factor of BSREM in breast cancer cases for 18 F-FDG WB-PET CT.

Emerging role of quantitative imaging (radiomics) and artificial intelligence in precision oncology.

Cancer is a fatal disease and the second most cause of death worldwide. Treatment of cancer is a complex process and requires a multi-modality-based approach. Cancer detection and treatment starts with screening/diagnosis and continues till the patient is alive. Screening/diagnosis of the disease is the beginning of cancer management and continued with the staging of the disease, planning and delivery of treatment, treatment monitoring, and ongoing monitoring and follow-up. Imaging plays an important role in all stages of cancer management. Conventional oncology practice considers that all patients are similar in a disease type, whereas biomarkers subgroup the patients in a disease type which leads to the development of precision oncology. The utilization of the radiomic process has facilitated the advancement of diverse imaging biomarkers that find application in precision oncology. The role of imaging biomarkers and artificial intelligence (AI) in oncology has been investigated by many researchers in the past. The existing literature is suggestive of the increasing role of imaging biomarkers and AI in oncology. However, the stability of radiomic features has also been questioned. The radiomic community has recognized that the instability of radiomic features poses a danger to the global generalization of radiomic-based prediction models. In order to establish radiomic-based imaging biomarkers in oncology, the robustness of radiomic features needs to be established on a priority basis. This is because radiomic models developed in one institution frequently perform poorly in other institutions, most likely due to radiomic feature instability. To generalize radiomic-based prediction models in oncology, a number of initiatives, including Quantitative Imaging Network (QIN), Quantitative Imaging Biomarkers Alliance (QIBA), and Image Biomarker Standardisation Initiative (IBSI), have been launched to stabilize the radiomic features.

Systematic review and meta-analysis of prediction models used in cervical cancer.

BACKGROUND: Cervical cancer is one of the most common cancers in women with an incidence of around 6.5 % of all the cancer in women worldwide. Early detection and adequate treatment according to staging improve the patient's life expectancy. Outcome prediction models might aid treatment decisions, but a systematic review on prediction models for cervical cancer patients is not available. DESIGN: We performed a systematic review for prediction models in cervical cancer following PRISMA guidelines. Key features that were used for model training and validation, the endpoints were extracted from the article and data were analyzed. Selected articles were grouped based on prediction endpoints i.e. Group1: Overall survival, Group2: progression-free survival; Group3: recurrence or distant metastasis; Group4: treatment response; Group5: toxicity or quality of life. We developed a scoring system to evaluate the manuscript. As per our criteria, studies were divided into four groups based on scores obtained in our scoring system, the Most significant study (Score > 60 %); Significant study (60 % > Score > 50 %); Moderately Significant study (50 % > Score > 40 %); least significant study (score < 40 %). A meta-analysis was performed for all the groups separately. RESULTS: The first line of search selected 1358 articles and finally 39 articles were selected as eligible for inclusion in the review. As per our assessment criteria, 16, 13 and 10 studies were found to be the most significant, significant and moderately significant respectively. The intra-group pooled correlation coefficient for Group1, Group2, Group3, Group4, and Group5 were 0.76 [0.72, 0.79], 0.80 [0.73, 0.86], 0.87 [0.83, 0.90], 0.85 [0.77, 0.90], 0.88 [0.85, 0.90] respectively. All the models were found to be good (prediction accuracy [c-index/AUC/R2] >0.7) in endpoint prediction. CONCLUSIONS: Prediction models of cervical cancer toxicity, local or distant recurrence and survival prediction show promising results with reasonable prediction accuracy [c-index/AUC/R2 > 0.7]. These models should also be validated on external data and evaluated in prospective clinical studies.

Machine-Learning-Based Radiomics for Classifying Glioma Grade from Magnetic Resonance Images of the Brain.

Grading of gliomas is a piece of critical information related to prognosis and survival. Classifying glioma grade by semantic radiological features is subjective, requires multiple MRI sequences, is quite complex and clinically demanding, and can very often result in erroneous radiological diagnosis. We used a radiomics approach with machine learning classifiers to determine the grade of gliomas. Eighty-three patients with histopathologically proven gliomas underwent MRI of the brain. Whenever available, immunohistochemistry was additionally used to augment the histopathological diagnosis. Segmentation was performed manually on the T2W MR sequence using the TexRad texture analysis softwareTM, Version 3.10. Forty-two radiomics features, which included first-order features and shape features, were derived and compared between high-grade and low-grade gliomas. Features were selected by recursive feature elimination using a random forest algorithm method. The classification performance of the models was measured using accuracy, precision, recall, f1 score, and area under the curve (AUC) of the receiver operating characteristic curve. A 10-fold cross-validation was adopted to separate the training and the test data. The selected features were used to build five classifier models: support vector machine, random forest, gradient boost, naive Bayes, and AdaBoost classifiers. The random forest model performed the best, achieving an AUC of 0.81, an accuracy of 0.83, f1 score of 0.88, a recall of 0.93, and a precision of 0.85 for the test cohort. The results suggest that machine-learning-based radiomics features extracted from multiparametric MRI images can provide a non-invasive method for predicting glioma grades preoperatively. In the present study, we extracted the radiomics features from a single cross-sectional image of the T2W MRI sequence and utilized these features to build a fairly robust model to classify low-grade gliomas from high-grade gliomas (grade 4 gliomas).

Glue for sealing internal pancreatic fistula in a patient with liver cirrhosis: a useful technique.

CONTEXT: Pancreatic fistulae are uncommon and usually follow acute or chronic pancreatitis. While most of these are treated conservatively, some require surgery. Recently endoscopic therapy has emerged as an effective alternative treatment modality. CASE REPORT: We present a patient with internal pancreatic fistula due to alcohol related chronic pancreatitis. Endotherapy using glue resulted in resolution of the fistula. CONCLUSION: The use of endoscopic glue injection may be a safe and effective method for the successful therapy of internal pancreatic fistula.

Radiomics: a quantitative imaging biomarker in precision oncology.

Cancer treatment is heading towards precision medicine driven by genetic and biochemical markers. Various genetic and biochemical markers are utilized to render personalized treatment in cancer. In the last decade, noninvasive imaging biomarkers have also been developed to assist personalized decision support systems in oncology. The imaging biomarkers i.e., radiomics is being researched to develop specific digital phenotype of tumor in cancer. Radiomics is a process to extract high throughput data from medical images by using advanced mathematical and statistical algorithms. The radiomics process involves various steps i.e., image generation, segmentation of region of interest (e.g. a tumor), image preprocessing, radiomic feature extraction, feature analysis and selection and finally prediction model development. Radiomics process explores the heterogeneity, irregularity and size parameters of the tumor to calculate thousands of advanced features. Our study investigates the role of radiomics in precision oncology. Radiomics research has witnessed a rapid growth in the last decade with several studies published that show the potential of radiomics in diagnosis and treatment outcome prediction in oncology. Several radiomics based prediction models have been developed and reported in the literature to predict various prediction endpoints i.e., overall survival, progression-free survival and recurrence in various cancer i.e., brain tumor, head and neck cancer, lung cancer and several other cancer types. Radiomics based digital phenotypes have shown promising results in diagnosis and treatment outcome prediction in oncology. In the coming years, radiomics is going to play a significant role in precision oncology.

Assessment of aeolian dust concentration, elemental composition, and their wet and dry deposition fluxes over the Northeast Arabian Sea.

Atmospheric aerosol over the Arabian Sea is significantly impacted by the long-range transported mineral dust from the surrounding continents. This transported mineral dust is hypothesized and tested during several studies to see the impacts on the surface ocean biogeochemical processes and subsequently to the Carbon cycle. It is, thus important to quantify dust contributions and their fluxes to the Arabian Sea. Here we assess temporal variability of dust concentration, their elemental characteristics as well as quantify their dry and wet deposition fluxes over the North-eastern Arabian Sea. The dust concentrations were found to vary from 59 to 132 µg m-3 which accounts for 50% to 90% of total mass during dusty days. However, its contribution during pre and post dust storms ranges between 6% and 60%. Relatively higher dust dry deposition flux of 28 ± 7 mg m-2 day-1 (range: 20-44) is estimated for dusty days compared to pre and post dusty days (range: 0.4-22 mg m-2 day-1). In contrast to dry deposition fluxes, significantly higher fluxes are estimated from wet deposition, averaging around 240 ± 220 mg m-2 day-1. These values are five times higher than those reported from cruise samples collected over the Arabian Sea. The contribution of dust to aerosol mass is further ascertained using elemental composition, wherein a significant correlation was observed between Fe and Al (r2 = 0.77) for samples collected during the dusty period, highlighting their similar crustal sources. Our estimation of dust flux over this region has implications for the supply of nutrients associated with natural dust to the surface water of the Arabian Sea.Implications: The Arabian Sea, one of the productive oceanic regions among the global oceans, has been identified as a perennial source of atmospheric CO2. This basin is heavily impacted by atmospheric dust deposition/inputs owing to its geographical location being surrounded by arid and semi-arid regions. It has been hypothesized that aeolian dust plays a significant role in modulating surface water biogeochemical processes including primary productivity, in the Arabian Sea. Furthermore, modelling studies have highlighted on the role of dust (containing Fe) in fueling and enhancing primary productivity in the Arabian Sea. However, quantification of dust deposition fluxes (wet and dry) on seasonal time scale is missing in the literature. This paper aims to partially fulfil this research gap by providing a long-term data of wet and dry deposition fluxes over the northeastern Arabian Sea. We have also discussed their seasonal variability and factors affecting this flux. Thus, this study will be valuable contribution to the aeolian research community and have significant implication toward the role of aeolian deposition to the surface water biogeochemical processes in the Arabian Sea.

Endoscopic Mucosectomy: A Novel Technique for Management of Polypoidal Solitary Rectal Ulcer Syndrome.

Solitary rectal ulcer syndrome (SRUS) is an uncommon disorder often challenging to treat. Surgical treatment is associated with suboptimal outcomes and postoperative complications. Argon plasma coagulation helps control rectal bleeding and healing of ulcers, but more extended follow-up data are not available. The macroscopic appearance of SRUS can be polypoid in 17%-25% of cases. Here, we describe a novel endoscopic technique for treating symptomatic patients with polypoidal variant of SRUS after failed medical and endoscopic argon plasma coagulation treatments.

Torsion of spleen and portal hypertension: Pathophysiology and clinical implications.

The displacement of spleen from its normal location to other places is known as wandering spleen (WS) and is a rare disease. The repeated torsion of WS is due to the presence of long pedicle and absence/laxity of anchoring ligaments. A WS is an extremely rare cause of left-sided portal hypertension (PHT) and severe gastric variceal bleeding. Left-sided PHT usually occurs as a result of splenic vein occlusion caused by splenic torsion, extrinsic compression of the splenic pedicle by enlarged spleen, and splenic vein thrombosis. There is a paucity of data on WS-related PHT, and these data are mostly in the form of case reports. In this review, we have analyzed the data of 20 reported cases of WS-related PHT. The mechanisms of pathogenesis, clinico-demographic profile, and clinical implications are described in this article. The majority of patients were diagnosed in the second to third decade of life (mean age: 26 years), with a strong female preponderance (M:F = 1:9). Eleven of the 20 WS patients with left-sided PHT presented with abdominal pain and mass. In 6 of the 11 patients, varices were detected incidentally on preoperative imaging studies or discovered intraoperatively. Therefore, pre-operative search for varices is required in patients with splenic torsion.