A 6 months retrospective observational study to assess the rationality and effectiveness of snake bite management in a tertiary care teaching hospital of rural Bengal, India

Background: Snake envenomation is a common life-threatening problem encountered all-over West Bengal particularly in the rural areas. There are a large number of patients attending the Emergency unit and being admitted to the Medicine ward, some in the intensive care unit (ICU) and intensive therapy unit (ITU) of the tertiary health care facilities. The objective of this study was to assess rationality and effectiveness of management of venomous snake bite following standard protocol – Standard treatment guidelines of Government of West Bengal and National snakebite management protocol of Government of India.Methods: This was a retrospective observational study of six months (May - October 2017) duration. Data were collected from the treatment records of patients admitted with history of snake bite in the Medicine ward, ICU and ITU of tertiary care teaching hospital of rural Bengal.Results: Of the 63 venomous bite patients, most (82.5 %) were diagnosed to have features of neurotoxic envenomation. All of them (100%) received anti-snake venom (ASV). There was no incidence of anaphylactic reaction as well as any serious adverse drug reaction following ASV administration. Two patients developed acute renal failure, needed haemodialysis. Overall percentage of mortality was 3.2%.Conclusions: The survival rate in venomous snake bite is found to be high in this institution. The practice of snake bite management is found to be adherent with standard protocol. A multicentric study of longer duration is suggested to draw a firm conclusion

MRI-based classification of IDH mutation and 1p/19q codeletion status of gliomas using a 2.5D hybrid multi-task convolutional neural network

Isocitrate dehydrogenase (IDH) mutation and 1p/19q codeletion status are important prognostic markers for glioma. Currently, they are determined using invasive procedures. Our goal was to develop artificial intelligence-based methods to non-invasively determine these molecular alterations from MRI. For this purpose, pre-operative MRI scans of 2648 patients with gliomas (grade II-IV) were collected from Washington University School of Medicine (WUSM; n = 835) and publicly available datasets viz. Brain Tumor Segmentation (BraTS; n = 378), LGG 1p/19q (n = 159), Ivy Glioblastoma Atlas Project (Ivy GAP; n = 41), The Cancer Genome Atlas (TCGA; n = 461), and the Erasmus Glioma Database (EGD; n = 774). A 2.5D hybrid convolutional neural network was proposed to simultaneously localize the tumor and classify its molecular status by leveraging imaging features from MR scans and prior knowledge features from clinical records and tumor location. The models were tested on one internal (TCGA) and two external (WUSM and EGD) test sets. For IDH, the best-performing model achieved areas under the receiver operating characteristic (AUROC) of 0.925, 0.874, 0.933 and areas under the precision-recall curves (AUPRC) of 0.899, 0.702, 0.853 on the internal, WUSM, and EGD test sets, respectively. For 1p/19q, the best model achieved AUROCs of 0.782, 0.754, 0.842, and AUPRCs of 0.588, 0.713, 0.782, on those three data-splits, respectively. The high accuracy of the model on unseen data showcases its generalization capabilities and suggests its potential to perform a 'virtual biopsy' for tailoring treatment planning and overall clinical management of gliomas

Framing image registration as a landmark detection problem for better representation of clinical relevance

Nowadays, registration methods are typically evaluated based on sub-resolution tracking error differences. In an effort to reinfuse this evaluation process with clinical relevance, we propose to reframe image registration as a landmark detection problem. Ideally, landmark-specific detection thresholds are derived from an inter-rater analysis. To approximate this costly process, we propose to compute hit rate curves based on the distribution of errors of a sub-sample inter-rater analysis. Therefore, we suggest deriving thresholds from the error distribution using the formula: median + delta * median absolute deviation. The method promises differentiation of previously indistinguishable registration algorithms and further enables assessing the clinical significance in algorithm development

Integrative Imaging Informatics for Cancer Research: Workflow Automation for Neuro-oncology (I3CR-WANO)

Efforts to utilize growing volumes of clinical imaging data to generate tumor evaluations continue to require significant manual data wrangling owing to the data heterogeneity. Here, we propose an artificial intelligence-based solution for the aggregation and processing of multisequence neuro-oncology MRI data to extract quantitative tumor measurements. Our end-to-end framework i) classifies MRI sequences using an ensemble classifier, ii) preprocesses the data in a reproducible manner, iii) delineates tumor tissue subtypes using convolutional neural networks, and iv) extracts diverse radiomic features. Moreover, it is robust to missing sequences and adopts an expert-in-the-loop approach, where the segmentation results may be manually refined by radiologists. Following the implementation of the framework in Docker containers, it was applied to two retrospective glioma datasets collected from the Washington University School of Medicine (WUSM; n = 384) and the M.D. Anderson Cancer Center (MDA; n = 30) comprising preoperative MRI scans from patients with pathologically confirmed gliomas. The scan-type classifier yielded an accuracy of over 99%, correctly identifying sequences from 380/384 and 30/30 sessions from the WUSM and MDA datasets, respectively. Segmentation performance was quantified using the Dice Similarity Coefficient between the predicted and expert-refined tumor masks. Mean Dice scores were 0.882 ($\pm$0.244) and 0.977 ($\pm$0.04) for whole tumor segmentation for WUSM and MDA, respectively. This streamlined framework automatically curated, processed, and segmented raw MRI data of patients with varying grades of gliomas, enabling the curation of large-scale neuro-oncology datasets and demonstrating a high potential for integration as an assistive tool in clinical practice

The Brain Tumor Sequence Registration Challenge: Establishing Correspondence between Pre-Operative and Follow-up MRI scans of diffuse glioma patients

Registration of longitudinal brain Magnetic Resonance Imaging (MRI) scans containing pathologies is challenging due to tissue appearance changes, and still an unsolved problem. This paper describes the first Brain Tumor Sequence Registration (BraTS-Reg) challenge, focusing on estimating correspondences between pre-operative and follow-up scans of the same patient diagnosed with a brain diffuse glioma. The BraTS-Reg challenge intends to establish a public benchmark environment for deformable registration algorithms. The associated dataset comprises de-identified multi-institutional multi-parametric MRI (mpMRI) data, curated for each scan's size and resolution, according to a common anatomical template. Clinical experts have generated extensive annotations of landmarks points within the scans, descriptive of distinct anatomical locations across the temporal domain. The training data along with these ground truth annotations will be released to participants to design and develop their registration algorithms, whereas the annotations for the validation and the testing data will be withheld by the organizers and used to evaluate the containerized algorithms of the participants. Each submitted algorithm will be quantitatively evaluated using several metrics, such as the Median Absolute Error (MAE), Robustness, and the Jacobian determinant

Electronic spectroscopy of cold cations in a 22-pole trap by indirect methods

The confining capabilities of a 22-pole ion trap have been employed to mea- sure electronic spectra of cations of astrophysical relevance. The ion trap sits on the head of a closed cycle cryostat, enabling collisional cooling of the confined ions with helium buffer gas. The relaxation of the ions enables measurement of electronic spectra under conditions similar to the interstellar medium. The ions are mass selected before injection into the trap, ensuring that only a single species is probed at a time and eliminating the possibility of overlapping contributions from other molecules. Most of the results presented in this thesis have been obtained by a resonant two colour two photon dissociation approach (R2C2PD). However, this method was found to be suitable only for certain molecules because of several inherent disadvantages. In order to broaden the classes of molecules whose electronic spectra can be measured, two other methods have been employed. The first one was Laser Induced Charge Transfer (LICT) and the second one was Laser Induced Inhibition of Complex Growth (LIICG). The latter is a novel method that has been developed and tested for the first time

AI-driven Neuro-oncology Imaging Analysis of Intracranial Tumors

According to 2016 cancer statistics, brain tumors are the leading cause of cancer-related morbidity and mortality around the world. More than 100 types of brain tumors, distinguishable by unique histopathological features, have been identified that differ significantly in prognosis and treatment strategies. Currently, histopathology is the diagnostic standard for characterizing brain tumors, which carries risks and potential complications. So, MRI is frequently used as an alternative to or in conjunction with histopathology due to its non-invasive nature and high soft-tissue contrast. With the emergence of artificial intelligence (AI) based approaches, different machine learning (ML) and deep learning (DL) models have been proposed for classification of tumors from MRI. Nevertheless, these methods are limited by reliance on manually engineered features, small patient cohorts used for training, and especially the requirement of manually segmented tumor volumes. Moreover, gliomas, the most common and aggressive malignant adult brain tumor, requires information of molecular parameters in addition to histopathology for a comprehensive classification. Currently, the clinical gold-standard of subtyping glioma involves invasive brain biopsy procedures that can be risky, may fail to capture intra-tumoral spatial heterogeneity due to localized samples, or can be inaccessible in low-resource settings. Multiple ML and DL models that have been proposed to classify molecular parameters by leveraging the variation in phenotypical characteristics of tumor manifested in MRI scans, are limited by the requirement of costly manual annotations, lack of rigorous validation, or applicability on only specific grades of glioma which hinders their translation to a pre-operative clinical setting. Additionally, the success of most existing AI-based methods proposed for tumor diagnosis are contingent on careful manual selection and pre-processing of MRI scans with appropriate tissue contrast properties, which is extremely time-intensive due to the high degree of non-uniformity in clinical neuro-oncology studies.Hence, the over-arching goal of my research is to build AI-driven solutions for curation, pre-processing, classification, and segmentation of intracranial tumors without the requirement of expert supervision. To this end, first, I have developed a 3d convolutional neural network (CNN) for classifying MRI scans into a healthy class and six most commonly occurring intracranial tumor classes viz., high grade glioma, low grade glioma, brain metastases, meningioma, pituitary adenoma, and acoustic neuroma, using only a single 3d post-contrast T1-weighted MRI volume per subject and without the requirement of any additional manual interaction. Second, I have built a hybrid CNN to simultaneously detect and segment glioma from MRI scans as well as classify two important molecular markers viz. the mutation of isocitrate dehydrogenase (IDH) enzyme and co-deletion status of chromosome arms 1p and 19q (1p/19q). The classification is performed by leveraging both MR imaging features and prior knowledge features acquired from clinical records and anatomical location of tumors. Third, I have designed and developed an end-to-end AI-driven framework for the aggregation and processing of multisequence neuro-oncology MRI data to extract quantitative tumor measurements. This framework i) classifies MRI sequence types using an ensemble classifier, ii) preprocesses the data in a reproducible manner, iii) delineates tumor tissue subtypes using CNNs, and iv) extracts diverse radiomic features. In this emerging era of precision diagnostics, these developments demonstrate a high potential for integration as assistive tools into clinical workflows to support clinical management and drive personalized treatment planning for intracranial tumors

Water nucleation at extreme supersaturation

ISSN:0021-9606ISSN:1089-769

Observation of propane cluster size distributions during nucleation and growth in a Laval expansion

We report on molecular-level studies of the condensation of propane gas and propane/ethane gas mixtures in the uniform (constant pressure and temperature) postnozzle flow of Laval expansions using soft single-photon ionization by vacuum ultraviolet light and mass spectrometric detection. The whole process, from the nucleation to the growth to molecular aggregates of sizes of several nanometers (∼5 nm), can be monitored at the molecular level with high time-resolution (∼3 μs) for a broad range of pressures and temperatures. For each time, pressure, and temperature, a whole mass spectrum is recorded, which allows one to determine the critical cluster size range for nucleation as well as the kinetics and mechanisms of cluster-size specific growth. The detailed information about the size, composition, and population of individual molecular clusters upon condensation provides unique experimental data for comparison with future molecular-level simulations.ISSN:0021-9606ISSN:1089-769