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Studies in isothiazole chemistry: I. isothiazolo [5,4-b] pyridines. II. approaches to isothiazolynes
The first part of this thesis describes investigations on the synthesis and chemistry of isothiazolo[5,4-b] pyridines.
The syntheses of a number of alkyl isothiazolo[5,4-b] pyridines from 5-amino-3-methylisothiazole under conditions of the Skraup reaction are described and their nuclear magnetic resonance spectra are discussed.
The reactions of 3-methyl- and 3,6-dimethylisothiazolo[5,4-b] pyridine have been studied. In particular they did not undergo nitration under the conditions employed and whereas the 3-methyl compound did not condense with benzaldehyde, 3,6-dimethylisothiazolo[5,4-b] pyridine gave mono-styryl products with benzaldehyde, and p-nitrobenzaldehyde. Potassium permanganate oxidation gave isothiazolo[5,4-b] pyrid-3(2H)-one 1,1-dioxides rather than the expected isothiazolo[5,4-b] pyridine carboxylic acids and chromic acid oxidation resulted in cleavage of the isothiazole ring to give 2,3-disubstituted pyridines.
Ethyl 4-hydroxy-3-methylisothiazolo[5,4-b] pyridine-5-earboxylabe was readily obtained by thermal cyclisation of the malonate from 5-amino-3-methylisothiazole and ethoxymethylenemalonic ester. The hydroxyl ester was converted to a number of substituted isothiazolo[5,4-b] pyridines. In addition it has been established that methylation at nitrogen rather than oxygen occurs with both the 4-hydroxy ester and the 4-hydroxy compound which has been shown to exist preferentially in the carbonyl form, namely 3-methylisothiazolo[5,4-b] _7pyrid-4-one.
The reaction of 5-amino-3-methylisothiazole with ethylacetoacetate and with acetylacetone under the conditions of the Conrad Limpach reaction and the Combes reaction respectively, did not give isothiazolo[5,4-b] pyridines. The latter gave a product which has been tentatively formulated as 5-acetyl-3,4-dimethylisothiazole.
The second part of this work describes the synthesis of 5-amino-3-chloroisothiazole-4-carboxylic acid, 4-amino-3-methylisothiazole- 5-carboxylic acid and 4-aminoisothiazole-3-carboxylic acid and experiments aimed at investigating the possible intermediacy of isothiazolynes. Attempts to generate and trap isothiazolynes by their aprotic diazotisation with isoainyl nitrite in the presence of 2,3,4,5-tetraphenylcyclopentadienone gave only small quantities of isothiazoles in addition to a variety of oxidation products derived from the arynophile.
4-Amino3-methylisothiazole-5-carboxylic acid gave isothiazolyl-substituted products when furan and anthracene were used as trapping agents, and it was found that the reaction of isothiazole-4-diazonium carboxylate hydrochloride and propylene oxide in the presence of furan gave 4,-cyano-1,2,3-thiadiazole.
In none of the reactions investigated was there any evidence for the formation of inothiazolyne intermediates
A study protocol for a randomised open-label clinical trial of artesunate-mefloquine versus chloroquine in patients with non-severe Plasmodium knowlesi malaria in Sabah, Malaysia (ACT KNOW trial)
Introduction Malaria due to Plasmodium knowlesi is reported throughout South-East Asia, and is the commonest cause of it in Malaysia. P. knowlesi replicates every 24 h and can cause severe disease and death. Current 2010 WHO Malaria Treatment Guidelines have no recommendations for the optimal treatment of non-severe knowlesi malaria. Artemisinin-combination therapies (ACT) and chloroquine have each been successfully used to treat knowlesi malaria; however, the rapidity of parasite clearance has not been prospectively compared. Malaysia\u27s national policy for malaria pre-elimination involves mandatory hospital admission for confirmed malaria cases with discharge only after two negative blood films; use of a more rapidly acting antimalarial agent would have health cost benefits. P. knowlesi is commonly microscopically misreported as P. malariae, P. falciparum or P. vivax, with a high proportion of the latter two species being chloroquine-resistant in Malaysia. A unified ACT-treatment protocol would provide effective blood stage malaria treatment for all Plasmodium species.Methods and analysis ACT KNOW, the first randomised controlled trial ever performed in knowlesi malaria, is a two-arm open-label trial with enrolments over a 2-year period at three district sites in Sabah, powered to show a difference in proportion of patients negative for malaria by microscopy at 24 h between treatment arms (clinicaltrials.gov #NCT01708876). Enrolments started in December 2012, with completion expected by September 2014. A total sample size of 228 is required to give 90% power (α 0.05) to determine the primary end point using intention-to-treat analysis. Secondary end points include parasite clearance time, rates of recurrent infection/treatment failure to day 42, gametocyte carriage throughout follow-up and rates of anaemia at day 28, as determined by survival analysis.Ethics and dissemination This study has been approved by relevant institutional ethics committees in Malaysia and Australia. Results will be disseminated to inform knowlesi malaria treatment policy in this region through peer-reviewed publications and academic presentations.Trial registration number NCT01708876
An Evaluation of Commonly Used Surrogate Baseline Creatinine Values to Classify AKI During Acute Infection.
INTRODUCTION: Classification of acute kidney injury (AKI) requires a premorbid baseline creatinine, often unavailable in studies in acute infection. METHODS: We evaluated commonly used surrogate and imputed baseline creatinine values against a "reference" creatinine measured during follow-up in an adult clinical trial cohort. Known AKI incidence (Kidney Disease: Improving Global Outcomes [KDIGO] criteria) was compared with AKI incidence classified by (1) back-calculation using the Modification of Diet in Renal Disease (MDRD) equation with and without a Chinese ethnicity correction coefficient; (2) back-calculation using the Chronic Kidney Disease-Epidemiology Collaboration (CKD-EPI) equation; (3) assigning glomerular filtration rate (GFR) from age and sex-standardized reference tables; and (4) lowest measured creatinine during admission. Back-calculated distributions were performed using GFRs of 75 and 100 ml/min. RESULTS: All equations using an assumed GFR of 75 ml/min underestimated AKI incidence by more than 50%. Back-calculation with CKD-EPI and GFR of 100 ml/min most accurately predicted AKI but misclassified all AKI stages and had low levels of agreement with true AKI diagnoses. Back-calculation using MDRD and assumed GFR of 100 ml/min, age and sex-reference GFR values adjusted for good health, and lowest creatinine during admission performed similarly, best predicting AKI incidence (area under the receiver operating characteristic curves [AUC ROCs] of 0.85, 0.87, and 0.85, respectively). MDRD back-calculation using a cohort mean GFR showed low total error (22%) and an AUC ROC of 0.85. CONCLUSION: Current methods for estimating baseline creatinine are large sources of potential error in acute infection studies. Preferred alternatives include MDRD equation back-calculation with a population mean GFR, age- and sex-specific GFR values corrected for "good health," or lowest measured creatinine. Studies using surrogate baseline creatinine values should report specific methodology
At-admission prediction of mortality and pulmonary embolism in an international cohort of hospitalised patients with COVID-19 using statistical and machine learning methods
By September 2022, more than 600 million cases of SARS-CoV-2 infection have been reported globally, resulting in over 6.5 million deaths. COVID-19 mortality risk estimators are often, however, developed with small unrepresentative samples and with methodological limitations. It is highly important to develop predictive tools for pulmonary embolism (PE) in COVID-19 patients as one of the most severe preventable complications of COVID-19. Early recognition can help provide life-saving targeted anti-coagulation therapy right at admission. Using a dataset of more than 800,000 COVID-19 patients from an international cohort, we propose a cost-sensitive gradient-boosted machine learning model that predicts occurrence of PE and death at admission. Logistic regression, Cox proportional hazards models, and Shapley values were used to identify key predictors for PE and death. Our prediction model had a test AUROC of 75.9% and 74.2%, and sensitivities of 67.5% and 72.7% for PE and all-cause mortality respectively on a highly diverse and held-out test set. The PE prediction model was also evaluated on patients in UK and Spain separately with test results of 74.5% AUROC, 63.5% sensitivity and 78.9% AUROC, 95.7% sensitivity. Age, sex, region of admission, comorbidities (chronic cardiac and pulmonary disease, dementia, diabetes, hypertension, cancer, obesity, smoking), and symptoms (any, confusion, chest pain, fatigue, headache, fever, muscle or joint pain, shortness of breath) were the most important clinical predictors at admission. Age, overall presence of symptoms, shortness of breath, and hypertension were found to be key predictors for PE using our extreme gradient boosted model. This analysis based on the, until now, largest global dataset for this set of problems can inform hospital prioritisation policy and guide long term clinical research and decision-making for COVID-19 patients globally. Our machine learning model developed from an international cohort can serve to better regulate hospital risk prioritisation of at-risk patients
Age-Related Clinical Spectrum of Plasmodium knowlesi Malaria and Predictors of Severity.
Background: Plasmodium knowlesi is increasingly reported in Southeast Asia, but prospective studies of its clinical spectrum in children and comparison with autochthonous human-only Plasmodium species are lacking. Methods: Over 3.5 years, we prospectively assessed patients of any age with molecularly-confirmed Plasmodium monoinfection presenting to 3 district hospitals in Sabah, Malaysia. Results: Of 481 knowlesi, 172 vivax, and 96 falciparum malaria cases enrolled, 44 (9%), 71 (41%), and 31 (32%) children aged ≤12 years. Median parasitemia was lower in knowlesi malaria (2480/μL [interquartile range, 538-8481/μL]) than in falciparum (9600/μL; P 15000/μL the best predictor (adjusted odds ratio, 16.1; negative predictive value, 98.5%; P 15000/μL
At-admission prediction of mortality and pulmonary embolism in an international cohort of hospitalised patients with COVID-19 using statistical and machine learning methods
By September 2022, more than 600 million cases of SARS-CoV-2 infection have been reported globally, resulting in over 6.5 million deaths. COVID-19 mortality risk estimators are often, however, developed with small unrepresentative samples and with methodological limitations. It is highly important to develop predictive tools for pulmonary embolism (PE) in COVID-19 patients as one of the most severe preventable complications of COVID-19. Early recognition can help provide life-saving targeted anti-coagulation therapy right at admission. Using a dataset of more than 800,000 COVID-19 patients from an international cohort, we propose a cost-sensitive gradient-boosted machine learning model that predicts occurrence of PE and death at admission. Logistic regression, Cox proportional hazards models, and Shapley values were used to identify key predictors for PE and death. Our prediction model had a test AUROC of 75.9% and 74.2%, and sensitivities of 67.5% and 72.7% for PE and all-cause mortality respectively on a highly diverse and held-out test set. The PE prediction model was also evaluated on patients in UK and Spain separately with test results of 74.5% AUROC, 63.5% sensitivity and 78.9% AUROC, 95.7% sensitivity. Age, sex, region of admission, comorbidities (chronic cardiac and pulmonary disease, dementia, diabetes, hypertension, cancer, obesity, smoking), and symptoms (any, confusion, chest pain, fatigue, headache, fever, muscle or joint pain, shortness of breath) were the most important clinical predictors at admission. Age, overall presence of symptoms, shortness of breath, and hypertension were found to be key predictors for PE using our extreme gradient boosted model. This analysis based on the, until now, largest global dataset for this set of problems can inform hospital prioritisation policy and guide long term clinical research and decision-making for COVID-19 patients globally. Our machine learning model developed from an international cohort can serve to better regulate hospital risk prioritisation of at-risk patients
The Rapid Emergence of Hypervirulent Klebsiella Species and Burkholderia pseudomallei as Major Health Threats in Southeast Asia: The Urgent Need for Recognition as Neglected Tropical Diseases
The World Health Organization (WHO)’s list of neglected tropical diseases (NTDs) highlights conditions that are responsible for devastating health, social and economic consequences, and yet, they are overlooked and poorly resourced. The NTD list does not include conditions caused by Gram-negative bacilli (GNB). Infections due to GNB cause significant morbidity and mortality and are prevalent worldwide. Southeast Asia is a WHO region of low- and middle-income countries carrying the largest burden of NTDs. Two significant health threats in Southeast Asia are Burkholderia pseudomallei (causing melioidosis) and hypervirulent Klebsiella pneumoniae (HvKp). Both diseases have high mortality and increasing prevalence, yet both suffer from a lack of awareness, significant under-resourcing, incomplete epidemiological data, limited diagnostics, and a lack of evidence-based treatment. Emerging evidence shows that both melioidosis and HvKp are spreading globally, including in high-income countries, highlighting the potential future global threat they pose. In this article, we review both conditions, identifying current trends and challenges in Southeast Asia and areas for future research. We also argue that melioidosis and HvKp merit inclusion as NTDs, and that mandatory global surveillance and reporting systems should be established, and we make an urgent call for research to better understand, detect, and treat these neglected diseases
Quantifying human-animal contact rates in Malaysian Borneo: influence of agricultural landscapes on contact with potential zoonotic disease reservoirs
Changing landscapes across the globe, but particularly in Southeast Asia, are pushing humans and animals closer together and may increase the likelihood of zoonotic spillover events. Malaysian Borneo is hypothesized to be at high risk of spillover events due to proximity between reservoir species and humans caused by recent deforestation in the region. However, the relationship between landscape and human-animal contact rates has yet to be quantified. An environmentally stratified cross-sectional survey was conducted in Sabah, Malaysia in 2015, collecting geolocated questionnaire data on potential risk factors for contact with animals for 10,100 individuals. 51% of individuals reported contact with poultry, 46% with NHPs, 30% with bats, and 2% with swine. Generalised linear mixed models identified occupational and demographic factors associated with increased contact with these species, which varied when comparing wildlife to domesticated animals. Reported contact rates with each animal group were integrated with remote sensing-derived environmental data within a Bayesian framework to identify regions with high probabilities of contact with animal reservoirs. We have identified high spatial heterogeneity of contact with animals and clear associations between agricultural practices and high animal rates. This approach will help inform public health campaigns in at-risk populations and can improve pathogen surveillance efforts on Malaysian Borneo. This method can additionally serve as a framework for researchers looking to identify targets for future pathogen detection in a chosen region of study
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