A rare case of tubeculous mesenteric cyst

We report a case of 17 year old female weighing 85 kg with chronic abdominal pain. Radiological imaging techniques revealed it as an enteric duplication cyst or mesenteric cyst. Diagnostic laparoscopy confirmed the cyst originating from mesentery. After laparoscopic excision of this cyst histopathology report was unusual, as a tuberculous mesenteric cyst

Intussusception due to caecal carcinoma in a young man: unusual cause of presentation a case report

A young 26 year male patient admitted with colicky pain in right iliac fossa with well palpable tender lump. After radiological investigation lump was diagnosed as ileocaecal intussusception. Patient underwent laparoscopy which diagnosed as intussusception due to caecal carcinoma. Laparoscopy again proved to be useful diagnostic tool over imaging techniques in this case. Laparoscopic assisted surgery of right radical hemicolectomy done successfully

Everest: Towards a Verified, Drop-in Replacement of HTTPS

The HTTPS ecosystem is the foundation on which Internet security is built. At the heart of this ecosystem is the Transport Layer Security (TLS) protocol, which in turn uses the X.509 public-key infrastructure and numerous cryptographic constructions and algorithms. Unfortunately, this ecosystem is extremely brittle, with headline-grabbing attacks and emergency patches many times a year. We describe our ongoing efforts in Everest (The Everest VERified End-to-end Secure Transport) a project that aims to build and deploy a verified version of TLS and other components of HTTPS, replacing the current infrastructure with proven, secure software. Aiming both at full verification and usability, we conduct high-level code-based, game-playing proofs of security on cryptographic implementations that yield efficient, deployable code, at the level of C and assembly. Concretely, we use F*, a dependently typed language for programming, meta-programming, and proving at a high level, while relying on low-level DSLs embedded within F* for programming low-level components when necessary for performance and, sometimes, side-channel resistance. To compose the pieces, we compile all our code to source-like C and assembly, suitable for deployment and integration with existing code bases, as well as audit by independent security experts. Our main results so far include (1) the design of Low*, a subset of F* designed for C-like imperative programming but with high-level verification support, and KreMLin, a compiler that extracts Low* programs to C; (2) an implementation of the TLS-1.3 record layer in Low*, together with a proof of its concrete cryptographic security; (3) Vale, a new DSL for verified assembly language, and several optimized cryptographic primitives proven functionally correct and side-channel resistant. In an early deployment, all our verified software is integrated and deployed within libcurl, a widely used library of networking protocols

MoNuSAC2020:A Multi-Organ Nuclei Segmentation and Classification Challenge

Detecting various types of cells in and around the tumor matrix holds a special significance in characterizing the tumor micro-environment for cancer prognostication and research. Automating the tasks of detecting, segmenting, and classifying nuclei can free up the pathologists' time for higher value tasks and reduce errors due to fatigue and subjectivity. To encourage the computer vision research community to develop and test algorithms for these tasks, we prepared a large and diverse dataset of nucleus boundary annotations and class labels. The dataset has over 46,000 nuclei from 37 hospitals, 71 patients, four organs, and four nucleus types. We also organized a challenge around this dataset as a satellite event at the International Symposium on Biomedical Imaging (ISBI) in April 2020. The challenge saw a wide participation from across the world, and the top methods were able to match inter-human concordance for the challenge metric. In this paper, we summarize the dataset and the key findings of the challenge, including the commonalities and differences between the methods developed by various participants. We have released the MoNuSAC2020 dataset to the public

Antibodies against endogenous retroviruses promote lung cancer immunotherapy

B cells are frequently found in the margins of solid tumours as organized follicles in ectopic lymphoid organs called tertiary lymphoid structures (TLS). Although TLS have been found to correlate with improved patient survival and response to immune checkpoint blockade (ICB), the underlying mechanisms of this association remain elusive. Here we investigate lung-resident B cell responses in patients from the TRACERx 421 (Tracking Non-Small-Cell Lung Cancer Evolution Through Therapy) and other lung cancer cohorts, and in a recently established immunogenic mouse model for lung adenocarcinoma. We find that both human and mouse lung adenocarcinomas elicit local germinal centre responses and tumour-binding antibodies, and further identify endogenous retrovirus (ERV) envelope glycoproteins as a dominant anti-tumour antibody target. ERV-targeting B cell responses are amplified by ICB in both humans and mice, and by targeted inhibition of KRAS(G12C) in the mouse model. ERV-reactive antibodies exert anti-tumour activity that extends survival in the mouse model, and ERV expression predicts the outcome of ICB in human lung adenocarcinoma. Finally, we find that effective immunotherapy in the mouse model requires CXCL13-dependent TLS formation. Conversely, therapeutic CXCL13 treatment potentiates anti-tumour immunity and synergizes with ICB. Our findings provide a possible mechanistic basis for the association of TLS with immunotherapy response

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BACKGROUND Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING Bill & Melinda Gates Foundation

ISOMAP TRACKING WITH PARTICLE FILTER

The problem of tracking an object in an image sequence involves challenges like translation, rotation, scaling, varying ambient light and occlusions. A model of an object is built off-line by making a training set with images of the object with different poses. A dimensionality reduction technique is used to capture the variations in the training images. This gives a low-dimensional representation of the data. Isometric feature mapping is the dimensionality reduction technique used to capture the true degrees of freedom in the data. Once the data is reduced to low-dimensions it forms a part of the state-vector of the object. Tracking is done in the Bayesian framework. Particle filters track the object in presence of nonlinearity and non-Gaussianity. The focus of this thesis is the problem of tracking a person\u27s head and also estimating its pose using Isometric feature mapping for dimensionality reduction and particle filter for tracking

Diagnosis of Crohn’s disease in India where tuberculosis is widely prevalent

AIM: To define the parameters that positively predict diagnosis of Crohn’s disease (CD) and differentiate it from gastrointestinal tuberculosis (GITB)

Hypoalbuminemia as an early predictor of severe COVID-19 infection: A retrospective observational study

Objectives: Several unique characteristics have been found in severe COVID19, such as lymphopenia, old age, high CRP level, elevated D dimer levels and underlying comorbid diseases. Serum albumin, being a negative acute phase reactant has been found to be associated with inflammatory response and poor outcomes in infectious diseases. The aim of the study was to analyse whether the serum albumin levels on admission might reflect the severity of systemic inflammation in COVID 19 infection and thus serve as an early predictive factor for COVID 19 outcomes. Materials and Method: This retrospective observational study included 185 COVID-19 positive patients. Laboratory data was recorded from blood samples collected at admission and analyzed by standard methods in the laboratory. Hypoalbuminemia was defined as serum albumin levels <3.5g/dl. p < 0.05 was considered statistically significant. Results: In the 185 COVID 19 patients studied, average age was 51.29 (±15.68) years. The study population had a male predominance (68.11%). 85 (45.95%) individuals were found to have hypoalbuminemia on admission. 18 (9.73%) deaths were reported amongst the study population and  a significant association was found between low serum albumin levels on admission and mortality.(p < 0.001)

Current Practices and the Future of Robotic Surgical Training

© 2023 Royal College of Surgeons of Edinburgh and Royal College of Surgeons in Ireland. Published by Elsevier Ltd. All rights reserved. This is the accepted manuscript version of an article which has been published in final form at https://doi.org/10.1016/j.surge.2023.02.006Introduction: This study reviews the current state of robotic surgery training for surgeons, including the various curricula, training methods, and tools available, as well as the challenges and limitations of these. Methods: The authors carried out a literature search across PubMed, MEDLINE, and Google Scholar using keywords related to ‘robotic surgery’, ‘computer-assisted surgery’, ‘simulation’, ‘virtual reality’, ‘surgical training’, and ‘surgical education’. Full text analysis was performed on 112 articles. Training Programmes: The training program for robotic surgery should focus on proficiency, deliberation, and distribution principles. The curricula can be broadly split up into pre-console and console-side training. Pre-Console and Console-Side Training: Simulation training is an important aspect of robotic surgery training to improve technical skill acquisition and reduce mental workload, which helps prepare trainees for live procedures. Operative Performance Assessment: The study also discusses the various validated assessment tools used for operative performance assessments. Future Advances: Finally, the authors propose potential future directions for robotic surgery training, including the use of emerging technologies such as AI and machine learning for real-time feedback, remote mentoring, and augmented reality platforms like Proximie to reduce costs and overcome geographic limitations. Conclusion: Standardisation in trainee performance assessment is needed. Each of the robotic curricula and platforms has strengths and weaknesses. The ERUS Robotic Curriculum represents an evidence-based example of how to implement training from novice to expert. Remote mentoring and augmented reality platforms can overcome the challenges of high equipment costs and limited access to experts. Emerging technologies offer promising advancements for real-time feedback and immersive training environments, improving patient outcomes.Peer reviewe