Effect of leaf phenology and morphology on the coordination between stomatal and minor vein densities

Leaf phenology (evergreen vs. deciduous) and morphology (simple vs. compound) are known to be related to water use strategies in tree species and critical adaptation to certain climatic conditions. However, the effect of these two traits and their interactions on the coordination between minor vein density (MVD) and stomatal density (SD) remains unclear. In this study, we examined the leaves of 108 tree species from plots in a primary subtropical forest in southern China, including tree species with different leaf morphologies and phenologies. We assessed nine leaf water-related functional traits for all species, including MVD, SD, leaf area (LA), minor vein thickness (MVT), and stomatal length (SL). The results showed no significant differences in mean LA and SD between either functional group (simple vs. compound and evergreen vs. deciduous). However, deciduous trees displayed a significantly higher mean MVD compared to evergreen trees. Similarly, compound-leaved trees have a higher (marginally significant) MVD than simple-leaved trees. Furthermore, we found that leaf morphology and phenology have significantly interactive effects on SL, and the compound-leafed deciduous trees exhibited the largest average SL among the four groups. There were significant correlations between the MVD and SD in all different tree groups; however, the slopes and interceptions differed within both morphology and phenology. Our results indicate that MVD, rather than SD, may be the more flexible structure for supporting the coordination between leaf water supply and demand in different leaf morphologies and phenologies. The results of the present study provide mechanistic understandings of the functional advantages of different leaf types, which may involve species fitness in community assembly and divergent responses to climate changes

Impact of systemic lupus erythematosus disease activity, hydroxychloroquine and NSAID on the risk of subsequent organ system damage and death: analysis in a single US medical centre

Objective To assess the impact of mild-moderate systemic lupus erythematosus (SLE) disease activity during a 12-month period on the risk of death or subsequent organ system damage.Methods 1168 patients with ≥24 months of follow-up from the Hopkins Lupus Cohort were included. Disease activity in a 12-month observation period was calculated using adjusted mean Safety of Estrogens in Lupus Erythematosus National Assessment (SELENA) version of the SLE Disease Activity Index (SLEDAI), defined as the area under the curve divided by the time interval. Damage accrual in the follow-up period was defined as change in Systemic Lupus International Collaborating Clinics/American College of Rheumatology Damage Index (SDI) score ≥1 among patients without prior damage. Patients visited the clinic quarterly and had SELENA-SLEDAI and SDI assessed at every visit.Results During follow-up (median 7 years), 39% of patients accrued new damage in any organ system (7% cardiovascular and 3% renal) and 8% died. In adjusted models, an increased SELENA-SLEDAI score increased the risk of death (HR=1.22, 95% CI 1.13 to 1.32, p<0.001), renal damage (HR=1.24, 95% CI 1.08 to 1.42, p=0.003) and cardiovascular damage (HR=1.17, 95% CI 1.07 to 1.29, p<0.001). Hydroxychloroquine use reduced the risk of death (HR=0.46, 95% CI 0.29 to 0.72, p<0.05) and renal damage (HR=0.30, 95% CI 0.13 to 0.68, p<0.05). Non-steroidal anti-inflammatory drug use increased the risk of cardiovascular damage (HR=1.66, 95% CI 1.04 to 2.63, p<0.05). Without prior damage, an increased adjusted mean SELENA-SLEDAI score increased the risk of overall damage accrual (HR=1.09, 95% CI 1.04 to 1.15, p<0.001).Conclusions Each one-unit increase in adjusted mean SELENA-SLEDAI during a 12-month observation period was associated with an increased risk of death and developing cardiovascular and renal damage

Utilisation and Expenditures in the Treatment of Patients with Gastro-Oesophageal Reflux Disease: Prevalence and High Cost Factors

Objective: Disease management can be described as a data-driven care process across the continuum from symptom perception through to diagnosis and treatment. In order to effectively manage this medical-care process, the practitioner must understand the epidemiology of the disease, and nature of the interaction between the disease, the patients and the healthcare system. With this framework in mind, this research provides a picture of gastro-oesophageal reflux disease (GORD) prevalence, demographics, treatment utilisation patterns and costs in a managed-care environment. Design and Setting: Because GORD is prevalent, affecting up to 10% of the US population, and has high levels of comorbidity and serious complications, even the direct costs of GORD can be significant. Medical and pharmacy claim databases from 4 health maintenance organisations (HMOs) in 1993 and 1994 were used to identify GORD patients. Total health services and drug utilisation, GORD-related medical procedures, concomitant diseases, and associated costs were tallied. Logistic regression models were used to estimate the high cost factors in GORD patients. A health service profile of patients with GORD treated in a managed care setting was also developed in this analysis. Results: While GORD represents a relatively small percentage of managed care spending (9.9%) there was wide variation in utilisation patterns for drugs, laboratory tests and provider types. Drugs accounted for nearly 50% of the total costs of GORD treatment. In addition, comorbid conditions seemed to have a significant impact on the overall spending of GORD patients in this environment. Asthma and chronic obstructive pulmonary disease were predictive of high cost. Conclusions: Variance in costs across managed care plans studied implies that the cost structures and clinical management approaches of the plans may have an impact on the treatment of GORD.Pharmacoeconomics, Gastro-oesophageal-reflux, Cost-analysis, Resource-use, Drug-utilisation, Antiulcers, Managed-care

Extending the data collection from a clinical trial: The Extended Salford Lung Study research cohort

Abstract The Extended Salford Lung Study (Ext-SLS) is an extension of the Salford Lung Studies (SLS) in asthma and chronic obstructive pulmonary disease (COPD) through retrospective and prospective collection of patient-level electronic health record (EHR) data. We compared the Ext-SLS cohort with the SLS intention-to-treat populations using descriptive analyses to determine if the strengths (e.g. randomization) of the clinical trial were maintained in the new cohort. Historical and patient-reported outcome data were captured from asthma-/COPD-specific questionnaires (e.g., Asthma Control Test [ACT]/COPD Assessment Test [CAT]). The Ext-SLS included 1147 participants (n = 798, SLS asthma; n = 349, SLS COPD). Of participants answering the ACT, 39% scored <20, suggesting poorly controlled asthma. For COPD, 61% of participants answering the CAT scored ≥21, demonstrating a high disease burden. Demographic/clinical characteristics of the cohorts were similar at SLS baseline. EHR data provided a long-term view of participants’ disease, and questionnaires provided information not typically captured. The Ext-SLS cohort is a valuable resource for respiratory research, and ongoing prospective data collection will add further value and ensure the Ext-SLS is an important source of patient-level information on obstructive airways disease

Fine-tuning Large Language Models for Chemical Text Mining

Extracting knowledge from complex and diverse chemical texts is a pivotal task for both experimental and computational chemists. The task is still considered to be extremely challenging due to the complexity of the chemical language and scientific literature. This study explored the power of fine-tuned large language models (LLMs) on five intricate chemical text mining tasks: compound entity recognition, reaction role labelling, metal-organic framework (MOF) synthesis information extraction, nuclear magnetic resonance spectroscopy (NMR) data extraction, and the conversion of reaction paragraph to action sequence. The fine-tuned LLMs models demonstrated impressive performance, significantly reducing the need for repetitive and extensive prompt engineering experiments. For comparison, we guided GPT-3.5 and GPT-4 with prompt engineering and fine-tuned GPT-3.5 as well as other open-source LLMs such as Llama2, T5, and BART. The results showed that the fine-tuned GPT models excelled in all tasks. It achieved exact accuracy levels ranging from 69% to 95% on these tasks with minimal annotated data. It even outperformed those task-adaptive pre-training and fine-tuning models that were based on a significantly larger amount of in-domain data. Given its versatility, robustness, and low-code capability, leveraging fine-tuned LLMs as flexible and effective toolkits for automated data acquisition could revolutionize chemical knowledge extraction