A Study of The Bacteriological Profile of Chronic Osteomyelitis in A Tertiary Care Referral Centre: A Contemporary Study

Chronic osteomyelitis is a huge burden on healthcare, especially in developing and poorer countries. With emerging antibiotic resistance patterns and increase in the incidence of Multi drug resistant (MDR) organisms, this challenge has gotten bigger. The notion that previous patterns would remain is being challenged now. We wanted to examine the trends in chronic osteomyelitis causing organisms in the contemporary era.    Materials and Methods: The study included 76 chronic osteomyelitis patients who were admitted to the hospital during the course of the study period. During surgery, a deep culture was obtained intra-operatively then submitted for bacteriological examination. Results: Males made up 81% of the instances examined in our study, while females made up 19%. With 84.21% of the cases, the lower limb was more often affected. In 44.73% of the cases, the femur was the most commonly impacted bone; the tibia (35.52%), humerus (11.29%), and radius (3.94%) were the next most common affected bones. In 71.05% of the cases, the culture was affirmative with mono-microbial proliferation; in 5.26% of the cases, polymicrobial growth was seen; while in 23.68% of the cases, the culture was sterile. In haematogenous osteomyelitis (HOM), which accounted for 65.21% of cases, Staphylococcus aureus was more prevalent than in implant- or trauma-related infections (26.41%). Younger age groups have higher rates of HOM. 40.32% of isolates were Gram-negative bacteria, while 59.67% were Gram-positive bacteria. Escherichia coli dominated the group of isolates that were Gram-negative (9.67%). Gram-negative organisms were uncommon in HOM cases and abundant in those with a history of trauma or implant in situ. The most frequent predisposing factor was a history of prior orthopaedic surgery with implant in situ (74.19%), and in 37.09% of the cases, hemorrhagic infection was discovered. Conclusion: The most frequent predisposing factor (74.19%) overall in our analysis of chronic osteomyelitis was a history of previous orthopaedic surgery with implant in situ, while 37.09% of the cases had a haematogenous infection. Gram-negative organisms were frequently linked to cases with a history of trauma. But Staphylococcus aureus (46.77%) continues to be the most prevalent agent, followed by Psuedomonas aueroginosa (8.06%), Escherichia coli (9.67%), and Enterobacter cloacae complex (8.06%). 58.62% of the isolates of Staphylococcus auerus were resistant to methicillin. Staphylococcus aureus was the most frequent cause of HOM, accounting for 65% of cases. Gram-positive bacteria remain more prevalent than Gram-negative bacteria, despite the rise in the prevalence of the latter. Of the cultures, 59.67% showed Gram positivity, whereas 40.32% contained Gram-negative organisms

Active Domain Adaptation via Clustering Uncertainty-weighted Embeddings

Generalizing deep neural networks to new target domains is critical to their real-world utility. In practice, it may be feasible to get some target data labeled, but to be cost-effective it is desirable to select a maximally-informative subset via active learning (AL). We study the problem of AL under a domain shift, called Active Domain Adaptation (Active DA). We empirically demonstrate how existing AL approaches based solely on model uncertainty or diversity sampling are suboptimal for Active DA. Our algorithm, Active Domain Adaptation via Clustering Uncertainty-weighted Embeddings (ADA-CLUE), i) identifies target instances for labeling that are both uncertain under the model and diverse in feature space, and ii) leverages the available source and target data for adaptation by optimizing a semi-supervised adversarial entropy loss that is complementary to our active sampling objective. On standard image classification-based domain adaptation benchmarks, ADA-CLUE consistently outperforms competing active adaptation, active learning, and domain adaptation methods across domain shifts of varying severity

Evaluating Visual Conversational Agents via Cooperative Human-AI Games

As AI continues to advance, human-AI teams are inevitable. However, progress in AI is routinely measured in isolation, without a human in the loop. It is crucial to benchmark progress in AI, not just in isolation, but also in terms of how it translates to helping humans perform certain tasks, i.e., the performance of human-AI teams. In this work, we design a cooperative game - GuessWhich - to measure human-AI team performance in the specific context of the AI being a visual conversational agent. GuessWhich involves live interaction between the human and the AI. The AI, which we call ALICE, is provided an image which is unseen by the human. Following a brief description of the image, the human questions ALICE about this secret image to identify it from a fixed pool of images. We measure performance of the human-ALICE team by the number of guesses it takes the human to correctly identify the secret image after a fixed number of dialog rounds with ALICE. We compare performance of the human-ALICE teams for two versions of ALICE. Our human studies suggest a counterintuitive trend - that while AI literature shows that one version outperforms the other when paired with an AI questioner bot, we find that this improvement in AI-AI performance does not translate to improved human-AI performance. This suggests a mismatch between benchmarking of AI in isolation and in the context of human-AI teams.Comment: HCOMP 201

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License

PANC Study (Pancreatitis: A National Cohort Study): national cohort study examining the first 30 days from presentation of acute pancreatitis in the UK

Abstract Background Acute pancreatitis is a common, yet complex, emergency surgical presentation. Multiple guidelines exist and management can vary significantly. The aim of this first UK, multicentre, prospective cohort study was to assess the variation in management of acute pancreatitis to guide resource planning and optimize treatment. Methods All patients aged greater than or equal to 18 years presenting with acute pancreatitis, as per the Atlanta criteria, from March to April 2021 were eligible for inclusion and followed up for 30 days. Anonymized data were uploaded to a secure electronic database in line with local governance approvals. Results A total of 113 hospitals contributed data on 2580 patients, with an equal sex distribution and a mean age of 57 years. The aetiology was gallstones in 50.6 per cent, with idiopathic the next most common (22.4 per cent). In addition to the 7.6 per cent with a diagnosis of chronic pancreatitis, 20.1 per cent of patients had a previous episode of acute pancreatitis. One in 20 patients were classed as having severe pancreatitis, as per the Atlanta criteria. The overall mortality rate was 2.3 per cent at 30 days, but rose to one in three in the severe group. Predictors of death included male sex, increased age, and frailty; previous acute pancreatitis and gallstones as aetiologies were protective. Smoking status and body mass index did not affect death. Conclusion Most patients presenting with acute pancreatitis have a mild, self-limiting disease. Rates of patients with idiopathic pancreatitis are high. Recurrent attacks of pancreatitis are common, but are likely to have reduced risk of death on subsequent admissions. </jats:sec

Clinical profile and management pattern of melasma patients in Western Nepal: A hospital based study

Introduction: Melasma is an acquired hypermelanosis of sun-exposed areas. Melasma is much more common in women than in men. Women are affected in 90% of cases. The patient usually presents with tan to brown patches. Multiple factors have been postulated to involve in the etiology and pathogenesis of melasma including pregnancy, oral contraceptives, genetics, sun exposure, cosmetics and race. Objectives: The objectives of the present studies are to study the demographic details of the melasma patients visiting the Manipal Teaching Hospital (MTH), to study the drugs used in management of melasma and to estimate the treatment cost to the patients. Materials and methods: We went through the records of the patient at MTH and found out the number of patients suffering from melasma and other related data from 25th November 2005 to 30th November 2007. The data obtained were analyzed as per the study objectives. Results: Altogether 107 patient files were included in the study. There were 75 (70.09%) females and 32 (29.90%) males (the total is 107). More than 50% of the patients were above the age 20 years. Most common site of occurrence of pigmentation was forehead accounting for 56.45% (n= 70) of the total case followed by cheeks 12.09% (n=15). Altogether 290 drugs were used in the patients with a Mean □ SD of 2.71 □ 0.89 drugs per patient. The Mean □ SD cost of medications was NRs 458 □ 251.36. Conclusion: The present study analyzed the clinical profile and treatment pattern of the melasma patients visiting MTH. Majority of the patients were of child bearing age. Sun screens were the most commonly employed medications followed by antioxidants 20 (6.89%) and antibiotics 18 (6.2%).</p