Neutral and cationic half-sandwich arene ruthenium, Cp*Rh and Cp*Ir oximato and oxime complexes: Synthesis, structural, DFT and biological studies

The reaction of [(p-cymene)RuCl2]2 and [Cp*MCl2]2 (M = Rh/Ir) with chelating ligand 2-pyridylcyanoxime {pyC(CN)NOH} leads to the formation of neutral oximato complexes having the general formula [(arene)M{pyC(CN)NO}Cl] {arene = p-cymene, M = Ru, (1); Cp*, M = Rh (2);Cp*, M = Ir (3)}. Whereas the reaction of 2-pyridyl phenyloxime {pyC(Ph)NOH} and 2-thiazolyl methyloxime {tzC(Me)NOH} with precursor compounds afforded the cationic oxide complexes bearing formula [(arene)M{pyC(ph)NOH}Cl]+ and [(arene)M{tzC(Me)NOH}Cl]+{arene = p-cymene M = Ru, (4), (7); Cp*, M = Rh (5), (8); Cp*, M = Ir (6), (9)}. The cationic complexes were isolated as their hexafluorophosphate salts. All these complexes were fully characterized by analytical, spectroscopic and X-ray diffraction studies. The molecular structures of the complexes revealed typical piano stool geometry around the metal center within which the ligand acts as a NNʹ donor chelating ligand. The Chemo-sensitivity activities of the complexes evaluated against HT-29 (human colorectal cancer), and MIAPaCa-2 (human pancreatic cancer) cell line showed that the iridium-based complexes are much more potent than the ruthenium and rhodium analogues. Theoretical studies were carried out to have a deeper understanding about the charge distribution pattern and the various electronic transitions occurring in the complexes

Exploring Large Language Models for Code Explanation

Automating code documentation through explanatory text can prove highly beneficial in code understanding. Large Language Models (LLMs) have made remarkable strides in Natural Language Processing, especially within software engineering tasks such as code generation and code summarization. This study specifically delves into the task of generating natural-language summaries for code snippets, using various LLMs. The findings indicate that Code LLMs outperform their generic counterparts, and zero-shot methods yield superior results when dealing with datasets with dissimilar distributions between training and testing sets.Comment: Accepted at the Forum for Information Retrieval Evaluation 2023 (IRSE Track

Aboriginal Status is a Prognostic Factor for Mortality among Antiretroviral Naive HIV-Positive Individuals First Initiating HAART

Background: Although the impact of Aboriginal status on HIV incidence, HIV disease progression, and accessto treatment has been investigated previously, little is known about the relationship between Aboriginal ethnicityand outcomes associated with highly active antiretroviral therapy (HAART). We undertook the present analysisto determine if Aboriginal and non-Aboriginal persons respond differently to HAART by measuring HIV plasmaviral load response, CD4 cell response and time to all-cause mortality.Methods: A population-based analysis of a cohort of antiretroviral therapy naïve HIV-positive Aboriginal menand women 18 years or older in British Columbia, Canada. Participants were antiretroviral therapy naïve, initiatedtriple combination therapy between August 1, 1996 and September 30, 1999. Participants had to complete abaseline questionnaire as well as have at least two follow-up CD4 and HIV plasma viral load measures. Theprimary endpoints were CD4 and HIV plasma viral load response and all cause mortality. Cox proportionalhazards models were used to determine the association between Aboriginal status and CD4 cell response, HIVplasma viral load response and all-cause mortality while controlling for several confounder variables.Results: A total of 622 participants met the study criteria. Aboriginal status was significantly associated with noAIDS diagnosis at baseline (p = 0.0296), having protease inhibitor in the first therapy (p = 0.0209), lower baselineHIV plasma viral load (p < 0.001), less experienced HIV physicians (P = 0.0133), history of IDU (p < 0.001), notcompleting high school (p = 0.0046), and an income of less than $10,000 per year (p = 0.0115). Cox proportionalhazards models controlling for clinical characteristics found that Aboriginal status had an increased hazard ofmortality (HR = 3.12, 95% CI: 1.77–5.48) but did not with HIV plasma viral load response (HR = 1.15, 95% CI:0.89–1.48) or CD4 cell response (HR = 0.95, 95% CI: 0.73–1.23).Conclusion: Our study demonstrates that HIV-infected Aboriginal persons accessing HAART had similar HIVtreatment response as non-Aboriginal persons but have a shorter survival. This study highlights the need forcontinued research on medical interventions and behavioural changes among HIV-infected Aboriginal and othermarginalized populations

Prospective Study of the Diagnostic Accuracy of the In Vivo Laser Scanning Confocal Microscope for Severe Microbial Keratitis.

To determine the diagnostic accuracy of in vivo confocal microscopy (IVCM) for moderate to severe microbial keratitis (MK). Double-masked prospective cohort study. Consecutive patients presenting to Aravind Eye Hospital, Madurai, India, between February 2012 and February 2013 with MK (diameter ≥3 mm, excluding descemetocele, perforation, or herpetic keratitis). Following examination, the corneal ulcer was scanned by IVCM (HRT3/RCM, Heidelberg Engineering, Heidelberg, Germany). Images were graded for the presence or absence of fungal hyphae or Acanthamoeba cysts by the confocal microscopist who performed the scan (masked to microbial diagnosis) and 4 other experienced confocal graders (masked to clinical features and microbiology). The regrading of the shuffled image set was performed by 3 graders, 3 weeks later. Corneal-scrape samples were collected for microscopy and culture. The main outcome measures were sensitivity, specificity, and positive and negative predictive values of IVCM compared with those of a reference standard of positive culture or light microscopy. Sensitivities and specificities for multiple graders were pooled and 95% confidence intervals calculated using a bivariate random-effects regression model. The study enrolled 239 patients with MK. Fungal infection was detected in 176 (74%) and Acanthamoeba in 17 (7%) by microbiological methods. IVCM had an overall pooled (5 graders) sensitivity of 85.7% (95% confidence interval [CI]: 82.2%-88.6%) and pooled specificity of 81.4% (95% CI: 76.0%-85.9%) for fungal filament detection. For Acanthamoeba, the pooled sensitivity was 88.2% (95% CI: 76.2%-94.6%) and pooled specificity was 98.2% (95% CI: 94.9%-99.3%). Intergrader agreement was good: κ was 0.88 for definite fungus; κ was 0.72 for definite Acanthamoeba. Intragrader repeatability was high for both definite fungus (κ: 0.88-0.95) and definite Acanthamoeba classification (κ: 0.63-0.90). IVCM images from 11 patients were considered by all 5 graders to have a specific organism present (10 fungus, 1 Acanthamoeba) but had negative results via culture and light microscopy. Laser scanning IVCM performed with experienced confocal graders has high sensitivity, specificity, and test reproducibility for detecting fungal filaments and Acanthamoeba cysts in moderate to large corneal ulcers in India. This imaging modality was particularly useful for detecting organisms in deep ulcers in which culture and light microscopy results were negative

No budget, no worries: Free and open source publishing software in biomedical publishing

Open Medicine (http://www.openmedicine.ca) is an electronic open access, peer-reviewed general medical journal that started publication in April 2007. The editors of Open Medicine have been exploring the use of Free and Open Source Software (FOSS) in constructing an efficient and sustainable publishing model that can be adopted by other journals. The goal of using FOSS is to minimize scarce financial resources and maximize return to the community by way of software code and high quality articles. Using information collected through archived documents and interviews with key editorial and technical staff responsible for journal development, this paper reports on the incorporation of FOSS into the production workflow of Open Medicine. We discuss the different types of software used; how they interface; why they were chosen; and the successes and challenges associated with using FOSS rather than proprietary software. These include the flagship FOSS office and graphics packages (OpenOffice, The GIMP, Inkscape), the content management system Drupal to run our Open Medicine Blog, wiki software MediaWiki to communicate and archive our weekly editorial and operational meeting agenda, minutes and other documents that the team can collectively edit, Scribus for automated layout and VOIP software Skype and OpenWengo to communicate. All software can be run on any of the main operating systems, including the free and open source GNU/Linux Operating system. Journal management is provided by Open Journal Systems, developed by the Public Knowledge Project (http://pkp.sfu.ca/?q=ojs). OJS assists with every stage of the refereed publishing process, from submissions, assignment of peer reviewers, through to online publication and indexing. The Public Knowledge Project has also recently developed Lemon8-XML (http://pkp.sfu.ca/ lemon8), which automates the conversion of text document formats to XML, enabling structured markup of content for automated searching and indexing. As XML is required for inclusion in PubMed Central, this integrated, semi-automated processing of manuscripts is a key ingredient for biomedical publishing, and Lemon8-XML has significant resource implications for the many journals where XML conversion is currently done manually or with proprietary software. Conversion to XML and the use of Scribus has allowed semi-automated production of HTML and PDF documents for online publication, representing another significant resource saving. Extensive use of free and open source software by Open Medicine serves as a unique case study for the feasibility of FOSS use for all journals in scholarly publishing. It also demonstrates how innovative use of this software adds to a more sustainable publishing model that is replicable worldwide

Addiction Treatment and Stable Housing among a Cohort of Injection Drug Users

Background: Unstable housing and homelessness is prevalent among injection drug users (IDU). We sought to examine whether accessing addiction treatment was associated with attaining stable housing in a prospective cohort of IDU in Vancouver, Canada. Methods: We used data collected via the Vancouver Injection Drug User Study (VIDUS) between December 2005 and April 2010. Attaining stable housing was defined as two consecutive ‘‘stable housing’ ’ designations (i.e., living in an apartment or house) during the follow-up period. We assessed exposure to addiction treatment in the interview prior to the attainment of stable housing among participants who were homeless or living in single room occupancy (SRO) hotels at baseline. Bivariate and multivariate associations between the baseline and time-updated characteristics and attaining stable housing were examined using Cox proportional hazard regression models. Principal Findings: Of the 992 IDU eligible for this analysis, 495 (49.9%) reported being homeless, 497 (50.1%) resided in SRO hotels, and 380 (38.3%) were enrolled in addiction treatment at the baseline interview. Only 211 (21.3%) attained stable housing during the follow-up period and of this group, 69 (32.7%) had addiction treatment exposure prior to achieving stable housing. Addiction treatment was inversely associated with attaining stable housing in a multivariate model (adjusted hazard ratio [AHR] = 0.71; 95 % CI: 0.52–0.96). Being in a partnered relationship was positively associated with the primary outcom

HIV Prevention in Care and Treatment Settings: Baseline Risk Behaviors among HIV Patients in Kenya, Namibia, and Tanzania.

HIV care and treatment settings provide an opportunity to reach people living with HIV/AIDS (PLHIV) with prevention messages and services. Population-based surveys in sub-Saharan Africa have identified HIV risk behaviors among PLHIV, yet data are limited regarding HIV risk behaviors of PLHIV in clinical care. This paper describes the baseline sociodemographic, HIV transmission risk behaviors, and clinical data of a study evaluating an HIV prevention intervention package for HIV care and treatment clinics in Africa. The study was a longitudinal group-randomized trial in 9 intervention clinics and 9 comparison clinics in Kenya, Namibia, and Tanzania (N = 3538). Baseline participants were mostly female, married, had less than a primary education, and were relatively recently diagnosed with HIV. Fifty-two percent of participants had a partner of negative or unknown status, 24% were not using condoms consistently, and 11% reported STI symptoms in the last 6 months. There were differences in demographic and HIV transmission risk variables by country, indicating the need to consider local context in designing studies and using caution when generalizing findings across African countries. Baseline data from this study indicate that participants were often engaging in HIV transmission risk behaviors, which supports the need for prevention with PLHIV (PwP). TRIAL REGISTRATION: ClinicalTrials.gov NCT01256463

Towards Conversational Diagnostic AI

At the heart of medicine lies the physician-patient dialogue, where skillful history-taking paves the way for accurate diagnosis, effective management, and enduring trust. Artificial Intelligence (AI) systems capable of diagnostic dialogue could increase accessibility, consistency, and quality of care. However, approximating clinicians' expertise is an outstanding grand challenge. Here, we introduce AMIE (Articulate Medical Intelligence Explorer), a Large Language Model (LLM) based AI system optimized for diagnostic dialogue. AMIE uses a novel self-play based simulated environment with automated feedback mechanisms for scaling learning across diverse disease conditions, specialties, and contexts. We designed a framework for evaluating clinically-meaningful axes of performance including history-taking, diagnostic accuracy, management reasoning, communication skills, and empathy. We compared AMIE's performance to that of primary care physicians (PCPs) in a randomized, double-blind crossover study of text-based consultations with validated patient actors in the style of an Objective Structured Clinical Examination (OSCE). The study included 149 case scenarios from clinical providers in Canada, the UK, and India, 20 PCPs for comparison with AMIE, and evaluations by specialist physicians and patient actors. AMIE demonstrated greater diagnostic accuracy and superior performance on 28 of 32 axes according to specialist physicians and 24 of 26 axes according to patient actors. Our research has several limitations and should be interpreted with appropriate caution. Clinicians were limited to unfamiliar synchronous text-chat which permits large-scale LLM-patient interactions but is not representative of usual clinical practice. While further research is required before AMIE could be translated to real-world settings, the results represent a milestone towards conversational diagnostic AI.Comment: 46 pages, 5 figures in main text, 19 figures in appendi

Towards Accurate Differential Diagnosis with Large Language Models

An accurate differential diagnosis (DDx) is a cornerstone of medical care, often reached through an iterative process of interpretation that combines clinical history, physical examination, investigations and procedures. Interactive interfaces powered by Large Language Models (LLMs) present new opportunities to both assist and automate aspects of this process. In this study, we introduce an LLM optimized for diagnostic reasoning, and evaluate its ability to generate a DDx alone or as an aid to clinicians. 20 clinicians evaluated 302 challenging, real-world medical cases sourced from the New England Journal of Medicine (NEJM) case reports. Each case report was read by two clinicians, who were randomized to one of two assistive conditions: either assistance from search engines and standard medical resources, or LLM assistance in addition to these tools. All clinicians provided a baseline, unassisted DDx prior to using the respective assistive tools. Our LLM for DDx exhibited standalone performance that exceeded that of unassisted clinicians (top-10 accuracy 59.1% vs 33.6%, [p = 0.04]). Comparing the two assisted study arms, the DDx quality score was higher for clinicians assisted by our LLM (top-10 accuracy 51.7%) compared to clinicians without its assistance (36.1%) (McNemar's Test: 45.7, p < 0.01) and clinicians with search (44.4%) (4.75, p = 0.03). Further, clinicians assisted by our LLM arrived at more comprehensive differential lists than those without its assistance. Our study suggests that our LLM for DDx has potential to improve clinicians' diagnostic reasoning and accuracy in challenging cases, meriting further real-world evaluation for its ability to empower physicians and widen patients' access to specialist-level expertise