Cytotoxic and genotoxic assessment of wastewater on HEK293 cell line

558-564The increasing industrialisation and urbanisation have deteriorated the quality and quantity of water bodies, harming the surrounding flora and fauna. Therefore, in our studies, we have chosen the HEK293 cell line to examine further the level of wastewater toxicity to which living beings are exposed. The water samples were collected from various sites around the Agra Canal in the Faridabad region of Haryana. Furthermore, cytotoxicity and genotoxicity confirmation of wastewater samples were done by MTT and comet assay, respectively. The water quality of the Agra canal is heavily influenced by agricultural, domestic, and industrial waste, which may affect the genetic material of species exposed to contaminated water and the sustainability of the local environment. As a result, continuous environmental monitoring and proper policy formulation are required to minimise the adverse effects of pollutants in waste, which would further enrich India’s preparation to take India a step ahead, and that could be the best possible way to commemorate India’s 75th year of Independence with the Azadi Ka Amrit Mahotsav

Improving Automatic Melanoma Diagnosis using Deep Learning-Based Segmentation of Irregular Networks

Deep Learning Has Achieved Significant Success in Malignant Melanoma Diagnosis. These Diagnostic Models Are Undergoing a Transition into Clinical Use. However, with Melanoma Diagnostic Accuracy in the Range of Ninety Percent, a Significant Minority of Melanomas Are Missed by Deep Learning. Many of the Melanomas Missed Have Irregular Pigment Networks Visible using Dermoscopy. This Research Presents an Annotated Irregular Network Database and Develops a Classification Pipeline that Fuses Deep Learning Image-Level Results with Conventional Hand-Crafted Features from Irregular Pigment Networks. We Identified and Annotated 487 Unique Dermoscopic Melanoma Lesions from Images in the ISIC 2019 Dermoscopic Dataset to Create a Ground-Truth Irregular Pigment Network Dataset. We Trained Multiple Transfer Learned Segmentation Models to Detect Irregular Networks in This Training Set. a Separate, Mutually Exclusive Subset of the International Skin Imaging Collaboration (ISIC) 2019 Dataset with 500 Melanomas and 500 Benign Lesions Was Used for Training and Testing Deep Learning Models for the Binary Classification of Melanoma Versus Benign. the Best Segmentation Model, U-Net++, Generated Irregular Network Masks on the 1000-Image Dataset. Other Classical Color, Texture, and Shape Features Were Calculated for the Irregular Network Areas. We Achieved an Increase in the Recall of Melanoma Versus Benign of 11% and in Accuracy of 2% over DL-Only Models using Conventional Classifiers in a Sequential Pipeline based on the Cascade Generalization Framework, with the Highest Increase in Recall Accompanying the Use of the Random Forest Algorithm. the Proposed Approach Facilitates Leveraging the Strengths of Both Deep Learning and Conventional Image Processing Techniques to Improve the Accuracy of Melanoma Diagnosis. Further Research Combining Deep Learning with Conventional Image Processing on Automatically Detected Dermoscopic Features is Warranted

Antihyperglycemic activity of compounds isolated from Indian medicinal plants

294-298Eleven antidiabetic Indian medicinal plants were investigated in streptozotocin induced diabetic rat model and provided scientific validation to prove their antihyperglycemic activity. Antidiabetic principles from five plants were isolated. All the compounds isolated were evaluated for antihyperglycemic activity in streptozotocin induced diabetic rat model and activities were compared with standard drug metformin. Some compounds were also screened in db/db mice. Two compounds (PP-1 and PP-2) inhibited significantly the activity of PTPase-1B in an in vitro system. This might be the underlying mechanism of antihyperglycemic activity of these compound

“Correlation of C-reactive protein and Blood culture in Neonatal sepsis’’ at Integral Institute of Medical Sciences and Research, Hospital Lucknow (UP)

INTRODUCTION- Neonatal sepsis is a clinical condition of bacteraemia characterized by systemic signs and symptoms of infection under 4 weeks. It involves systemic infection of newborn including septicemia, meningitis, pneumonia, arthritis, osteomyelitis and urinary tract infection (UTI). CRP is an abnormal β-globulin produced by liver during any inflammatory. The gold standard method to diagnose neonatal sepsis is blood culture however it is time consuming is, requires well equipped laboratory and trained personnel.   AIM- To study the correlation of C - reactive protein and Blood Culture in Neonatal sepsis. MATERIAL & METHODS-. The present study was a Retrospective study conducted at Integral Institute of Medical Sciences and Research, Hospital Lucknow. (UP) All the indoor neonates attending at Integral Institute of Medical Science and Research, Hospital who’s both parameters CRP as well as blood culture were noted in microbiology department register during my study period. In Blood culture sample was collected aseptically and it was processed by either conventional or automated blood culture method. CRP estimation was done by latex agglutination card test. CRP was reported as positive if agglutination particles were seen. RESULT- CRP positivity rate, Out of 235 samples, 72(30.64%) samples were positive and 163 (69.36%) were negative. Blood culture positivity rate, Out of 118 samples 71 (60.17%) cases were culture positive and 47(39.83%) were negative. After comparison of CRP samples with blood culture samples, 90 samples were tested for blood culture and CRP both and the age of babies was under 4 weeks.   CONCLUSION - So from our study we are concluding that blood culture is the gold standard method for the diagnosis of neonatal sepsis although we can use CRP as the screening method, this test is not specific enough to be relied upon as the only indicato

Cytotoxic and genotoxic assessment of wastewater on HEK293 cell line

The increasing industrialisation and urbanisation have deteriorated the quality and quantity of water bodies, harming the surrounding flora and fauna. Therefore, in our studies, we have chosen the HEK293 cell line to examine further the level of wastewater toxicity to which living beings are exposed. The water samples were collected from various sites around the Agra Canal in the Faridabad region of Haryana. Furthermore, cytotoxicity and genotoxicity confirmation of wastewater samples were done by MTT and comet assay, respectively. The water quality of the Agra canal is heavily influenced by agricultural, domestic, and industrial waste, which may affect the genetic material of species exposed to contaminated water and the sustainability of the local environment. As a result, continuous environmental monitoring and proper policy formulation are required to minimise the adverse effects of pollutants in waste, which would further enrich India’s preparation to take India a step ahead, and that could be the best possible way to commemorate India’s 75th year of Independence with the Azadi Ka Amrit Mahotsav

Development and validation of an HPLC method for Karanjin in Pongamia pinnata linn. leaves

A rapid, simple and specific reversed-phase HPLC method has been developed for analysis of karanjin in Pongamia pinnata Linn. leaves. HPLC analysis was performed on a C 18 column using an 85:13.5:1.5 (v/v) mixtures of methanol, water and acetic acid as isocratic mobile phase at a flow rate of 1 ml/min. UV detection was at 300 nm. The method was validated for accuracy, precision, linearity, specificity. Validation revealed the method is specific, accurate, precise, reliable and reproducible. Good linear correlation coefficients (r 2 >0.997) were obtained for calibration plots in the ranges tested. Limit of detection was 4.35 μg and limit of quantification was 16.56 μg. Intra and inter-day RSD of retention times and peak areas was less than 1.24% and recovery was between 95.05 and 101.05%. The established HPLC method is appropriate enabling efficient quantitative analysis of karanjin in Pongamia pinnata leaves

Increasing Melanoma Diagnostic Confidence: Forcing the Convolutional Network to Learn from the Lesion

Deep learning implemented with convolutional network architectures can exceed specialists' diagnostic accuracy. However, whole-image deep learning trained on a given dataset may not generalize to other datasets. The problem arises because extra-lesional features - ruler marks, ink marks, and other melanoma correlates - may serve as information leaks. These extra-lesional features, discoverable by heat maps, degrade melanoma diagnostic performance and cause techniques learned on one data set to fail to generalize. We propose a novel technique to improve melanoma recognition by an EfficientNet model. The model trains the network to detect the lesion and learn features from the detected lesion. A generalizable elliptical segmentation model for lesions was developed, with an ellipse enclosing a lesion and the ellipse enclosed by an extended rectangle (bounding box). The minimal bounding box was extended by 20% to allow some background around the lesion. The publicly available International Skin Imaging Collaboration (ISIC) 2020 skin lesion image dataset was used to evaluate the effectiveness of the proposed method. Our test results show that the proposed method improved diagnostic accuracy by increasing the mean area under receiver operating characteristic curve (mean AUC) score from 0.9 to 0.922. Additionally, correctly diagnosed scores are also improved, providing better separation of scores, thereby increasing melanoma diagnostic confidence. The proposed lesion-focused convolutional technique warrants further study.Comment: 6 pages, 5 figure

Primary pancreatic Ewing sarcoma with metastases on FDG PET/CT

Abstract Background Ewing sarcoma (ES) is a highly aggressive malignant tumor most commonly affecting long bones. Extraskeletal Ewing sarcoma (EES) is a malignant tumor with aggressive behavior carrying bad prognosis with pancreas being an extremely rare primary site. We present a case of histopathologically proven EES of the pancreas in a young female who presented with abdominal pain. 18F-fluorodeoxyglucose positron emission tomography (18F FDG PET/CT) is a useful modality for detecting distant metastases in EES. It helps in diagnosis, localizing the primary, its extension, optimal treatment planning and evaluation of response to standard treatments available. Case presentation An 18-year-old female presented with complaints of progressive abdominal pain and distention since 6 weeks. Physical examination was suggestive of a solid large mass in the upper left abdomen and decreased breath sounds with dullness in the left lower lung fields. On Contrast enhanced computed tomography (CECT) imaging, a large heterogeneously enhancing mass was seen arising from pancreas along with retroperitoneal lymphadenopathy. A moderate sized left sided pleural effusion with atelectasis of lower lobe of left lung was also noted. Histopathological analysis was suggestive of pancreatic ES following which the patient underwent five cycles of chemotherapy. Following this, she underwent 18F FDG PET/CT which showed hypermetabolic large mass arising from body and tail of pancreas with areas of internal necrosis along with left adrenal metastasis, retroperitoneal lymphadenopathy, a massive left pleural effusion and compressive atelectasis of left lower lobe. The patient expired within a week following PET/CT. Conclusions EES most often presents in the late stage of the disease with vague symptoms. Timely diagnosis and initiation of treatment is of utmost importance considering the aggressiveness of the tumor. Establishing a diagnosis of Ewing sarcoma is especially difficult when the mass is arising from the pancreas. Imaging, histopathology and immunohistochemistry (IHC) play a key role in accurate diagnosis of such masses. 18F FDG PET/CT can be useful for detecting local and distant spread, operability, treatment planning and evaluation of response to chemotherapy

Basal cell carcinoma diagnosis with fusion of deep learning and telangiectasia features

Telangiectasia masks dataset created on a subset of the ISIC18, ISIC19 training datasets and the NIH study dataset R43 CA153927-01 and CA101639-02A2. All annotations are for Basal Cell Carcinoma lesions. This dataset was used in the publication: " Basal cell carcinoma diagnosis with fusion of deep learning and telangiectasia features " (to be submitted). This is an expanded dataset that was initially used in “A Deep Learning Approach to Detect Blood Vessels in Basal Cell Carcinoma”. A sample lesion image and the corresponding mask is provided for preview. Lesion images and masks have been uploaded as separate zipped folders that can be downloaded