Sleep patterns, physical activity, genetic susceptibility, and incident rheumatoid arthritis: a prospective cohort study.

BACKGROUND: Sleep and physical activity (PA) are thought to be interconnected with the development of rheumatoid arthritis (RA). However, the precise nature and extent of these relationships have yet to be fully quantified. This study aimed to quantify the longitudinal effects of sleep behaviors, PA, and genetic susceptibility on the incidence of RA and to estimate the combined effects and interactions among these exposures. METHODS: A total of 363,211 adults were derived from a large European cohort. We incorporated five sleep behaviors (sleep duration, insomnia, snoring, chronotype, and daytime sleepiness) to generate sleep patterns, which were defined based on healthy sleep scores. Multivariate-adjusted Cox proportional hazard models were conducted to assess the individual and combined associations of sleep patterns, PA, and genetic susceptibility with the risk of RA occurrence. Multiplicative and additive interactions were estimated by Pinteraction and relative excess risk due to interaction (RERI) between each of the two exposures. RESULTS: During a follow-up of 12.5 years, 4262 RA cases were ascertained. A healthy sleep pattern was associated with a decreased risk of RA in a dose-response manner, with an adjusted hazard ratio (HR) of 0.79 (95% confidence interval [CI] = 0.75-0.84), independent of traditional risk factors and genetic predisposition. Under the restricted cubic splines model, a non-linear association was detected for PA and RA risk. Participants in the intermediate quintile 3 showed the lowest risk for developing RA, with a HR 95% CI of 0.84 (0.76-0.92). Moreover, there was an additive interaction effect of intermediate sleep pattern and PA, with a 0.45 (95% CI = 0.02-0.87) RERI of developing RA. Additionally, individuals at high genetic risk had the greatest 10-year absolute risk reduction (10.58 per 1000 person-years) when adopting both favorable behaviors. CONCLUSIONS: A healthy sleep pattern and moderate PA were associated with a reduced risk of developing RA, which can offset the deleterious effects of predisposing genetic components. Implementing these modifiable lifestyle factors into public health practices is beneficial for RA prevention.

Global, regional and national temporal trends in prevalence for musculoskeletal disorders in women of childbearing age, 1990-2019: an age-period-cohort analysis based on the Global Burden of Disease Study 2019.

OBJECTIVES: To provide an overview and in-depth analysis of temporal trends in prevalence of musculoskeletal (MSK) disorders in women of childbearing age (WCBA) at global, regional and national levels over the last 30 years, with a special focus on their associations with age, period and birth cohort. METHODS: Estimates and 95% uncertainty intervals (UIs) for MSK disorders prevalence in WCBA were extracted from the Global Burden of Diseases, Injuries and Risk Factors Study 2019. An age-period-cohort model was adopted to estimate the overall annual percentage change of prevalence (net drift, % per year), annual percentage change of prevalence within each age group (local drift, % per year), fitted longitudinal age-specific rates adjusted for period deviations (age effects) and period/cohort relative risks (period/cohort effects) from 1990 to 2019. RESULTS: In 2019, the global number of MSK disorders prevalence in WCBA was 354.57 million (95% UI: 322.64 to 387.68). Fifty countries had at least one million prevalence, with India, China, the USA, Indonesia and Brazil being the highest accounting for 51.03% of global prevalence. From 1990 to 2019, a global net drift of MSK disorders prevalence in WCBA was -0.06% (95% CI: -0.07% to -0.05%) per year, ranging from -0.09% (95% CI: -0.10% to -0.07%) in low-middle sociodemographic index (SDI) region to 0.10% (95% CI: 0.08% to 0.12%) in high-middle SDI region, with 138 countries presenting increasing trends, 24 presenting decreasing trends and 42 presenting relatively flat trends. As reflected by local drift, higher SDI regions had more age groups showing rising prevalence whereas lower SDI regions had more declining prevalence. Globally, an increasing occurrence of MSK disorders prevalence in WCBA beyond adolescent and towards the adult stage has been prominent. Age effects illustrated similar patterns across different SDI regions, with risk increasing with age. High SDI region showed generally lower period risks over time, whereas others showed more unfavourable period risks. High, high-middle and middle SDI regions presented unfavourable prevalence deteriorations, whereas others presented favourable prevalence improvements in successively birth cohorts. CONCLUSIONS: Although a favourable overall temporal trend (net drift) of MSK disorders prevalence in WCBA was observed over the last 30 years globally, there were 138 countries showing unfavourable rising trends, coupled with deteriorations in period/cohort risks in many countries, collectively raising concerns about timely realisation of the Targets of Sustainable Development Goal. Improvements in the MSK disorders-related prevention, management and treatment programmes in WCBA could decline the relative risk for successively younger birth cohorts and for all age groups over period progressing.

Global, regional and national burden of CKD in children and adolescents from 1990 to 2019.

BACKGROUND: Chronic kidney disease(CKD) is one of the most prevalent non-communicable health concerns in children and adolescents worldwide; however, data on its incidence, prevalence, disability-adjusted life years (DALYs) and trends in the population are limited. We aimed to assess the global, regional and national trends in CKD burden in children and adolescents. METHODS: In this trend analysis based on the 2019 Global Diseases, Injuries, and Risk Factors Study, CKD incidence, prevalence and DALYs rates per 100 000 population for children and adolescents were reported at the global, regional and national levels, as well as the average annual percentage change (AAPC). These global trends were analyzed by age, sex, region and socio-demographic index (SDI). RESULTS: Globally, the overall incidence of CKD (all stages including kidney replacement therapy) in children and adolescents showed an increasing trend [AAPC 0.44 (95% confidence interval 0.36-0.52)] between 1990 and 2019. Similarly, the overall prevalence of CKD also showed an upward trend [AAPC 0.46 (0.42-0.51)]. However, the DALYs of CKD showed a continuous decreasing trend [AAPC -1.18 (-1.37 to -0.99)]. The population aged 15-19 years had the largest CKD incidence increase during this period. The largest increase in age-standardized incidence rate (ASIR) was in middle SDI countries [AAPC 0.56 (0.45-0.67)]. The relationship between the ASIR and SDI showed an inverse U-shaped correlation while the relationship between the age-standardized DALYs rate (ASDR) and SDI showed an inverse trend with SDI. Among adolescents (15-19 years), the ASIR continued to increase for five causes of CKD, owing to type 2 diabetes mellitus and hypertension. Most of the disease burden was concentrated in countries with a lower SDI. Andean Latin America and Central Latin America showed the largest increases in CKD ASIR between 1990 and 2019. CONCLUSION: The burden of CKD in children and adolescents has increased worldwide, especially in regions and countries with a lower SDI.

Joint effects of sleep disturbance and renal function impairment on incident new-onset severe metabolic dysfunction-associated steatotic liver disease.

AIM: To elucidate the effects of sleep parameters and renal function on the risk of developing new-onset severe metabolic dysfunction-associated steatotic liver disease (MASLD). MATERIALS AND METHODS: The primary analysis involved a cohort of 305 257 participants. Multivariable Cox models were employed to calculate hazard ratios and 95% confidence intervals. Traditional mediation and two-step Mendelian randomization (MR) analyses were conducted to assess the associations and mediating roles of renal function indicators between sleep and new-onset severe MASLD. RESULTS: Poor sleep score and renal function biomarker score (RFS) were associated with an increased risk of new-onset severe MASLD (all ptrend <0.001). Participants with poor sleep patterns and the highest RFS had a 5.45-fold higher risk of new-onset severe MASLD, compared to those with healthy sleep patterns and the lowest RFS (p < 0.001). The RFS could explain 10.08% of the correlations between poor sleep score and risk of new-onset severe MASLD. Additionally, MR analyses supported a causal link between insomnia and new-onset severe MASLD and revealed a mediating role of chronic kidney disease in the connection between insomnia and new-onset severe MASLD risk. CONCLUSIONS: This study highlights the independent and combined associations of sleep parameters and renal function indicators with new-onset severe MASLD, underscoring the bidirectional communication of the liver-kidney axis and providing modifiable strategies for preventing MASLD.

Associations between air pollutants and acute exacerbation of drug-resistant tuberculosis: evidence from a prospective cohort study.

BACKGROUND: Short-term exposure to air pollution may trigger symptoms of drug-resistant tuberculosis (DR-TB) through stimulating lung tissue, damaging tracheobronchial mucosa, the key anti-mycobacterium T cell immune function, and production and release of inflammatory cytokines. OBJECTIVE: To investigate the association between acute exacerbations of DR-TB and short-term residential exposure to air pollutants (PM10, PM2.5, SO2, NO2, CO and O3) based on a large prospective cohort in Anhui Province, China. METHOD: Patients were derived from a prospective cohort study of DR-TB in Anhui Province. All DR-TB patients underwent drug-susceptibility testing and prefecture-level reference laboratories confirmed their microbiologies. The case-crossover design was performed to evaluate the association between the risk of acute exacerbations of DR-TB and short-term residential exposure to air pollution. RESULTS: Short-term NO2 exposure was significantly related to an elevated risk of first-time outpatient visit due to acute exacerbations of DR-TB(relative risk:1.159, 95% confidence interval:1.011 ~ 1.329). Stratification analyses revealed that the relationship between the risk of acute exacerbations and NO2 exposure was stronger in the elderly (age ≥ 65) DR-TB patients, and in individuals with a history of TB treatment. CONCLUSIONS: NO2 Exposure was significantly associated with an elevated risk of acute exacerbation of DR-TB in Anhui Province, China.

Ultrafast spectroscopy study of DNA photophysics after proflavine intercalation.

Proflavine (PF), an acridine DNA intercalating agent, has been widespread applied as an anti-microbial and topical antiseptic agent due to its ability to suppress DNA replication. On the other hand, various studies show that PF intercalation to DNA can increase photogenotoxicity and has potential chances to induce carcinomas of skin appendages. However, the effects of PF intercalation on the photophysical and photochemical properties of DNA have not been sufficiently explored. In this study, the excited state dynamics of the PF intercalated d(GC)9 • d(GC)9 and d(AT)9 • d(AT)9 DNA duplex are investigated in an aqueous buffer solution. Under 267 nm excitation, we observed ultrafast charge transfer (CT) between PF and d(GC)9 • d(GC)9 duplex, generating a CT state with an order of magnitude longer lifetime compared to that of the intrinsic excited state reported for the d(GC)9 • d(GC)9 duplex. In contrast, no excited state interaction was detected between PF and d(AT)9 • d(AT)9. Nevertheless, a localized triplet state with a lifetime over 5 µs was identified in the PF-d(AT)9 • d(AT)9 duplex.

Identification of lipid-modifying drug targets for autoimmune diseases: insights from drug target mendelian randomization.

BACKGROUNDS: A growing body of evidence has highlighted the interactions of lipids metabolism and immune regulation. Nevertheless, there is still a lack of evidence regarding the causality between lipids and autoimmune diseases (ADs), as well as their possibility as drug targets for ADs. OBJECTIVES: This study was conducted to comprehensively understand the casual associations between lipid traits and ADs, and evaluate the therapeutic possibility of lipid-lowering drug targets on ADs. METHODS: Genetic variants for lipid traits and variants encoding targets of various lipid-lowering drugs were derived from Global Lipid Genetics Consortium (GLGC) and verified in Drug Bank. Summary data of ADs were obtained from MRC Integrative Epidemiology Unit (MER-IEU) database and FinnGen consortium, respectively. The causal inferences between lipid traits/genetic agents of lipid-lowering targets and ADs were evaluated by Mendelian randomization (MR), summary data-based MR (SMR), and multivariable MR (MVMR) analyses. Enrichment analysis and protein interaction network were employed to reveal the functional characteristics and biological relevance of potential therapeutic lipid-lowering targets. RESULTS: There was no evidence of causal effects regarding 5 lipid traits and 9 lipid-lowering drug targets on ADs. Genetically proxied 3-hydroxy-3-methylglutaryl-CoA reductase (HMGCR) inhibition was associated with a reduced risk of rheumatoid arthritis (RA) in both discovery (OR [odds ratio] = 0.45, 95%CI: 0.32, 0.63, P = 6.79 × 10- 06) and replicate datasets (OR = 0.37, 95%CI: 0.23, 0.61, P = 7.81 × 10- 05). SMR analyses supported that genetically proxied HMGCR inhibition had causal effects on RA in whole blood (OR = 0.48, 95%CI: 0.29, 0.82, P = 6.86 × 10- 03) and skeletal muscle sites (OR = 0.75, 95%CI: 0.56, 0.99, P = 4.48 × 10- 02). After controlling for blood pressure, body mass index (BMI), smoking and drinking alchohol, HMGCR suppression showed a direct causal effect on a lower risk of RA (OR = 0.33, 95%CI: 0.40, 0.96, P = 0.042). CONCLUSIONS: Our study reveals causal links of genetically proxied HMGCR inhibition (lipid-lowering drug targets) and HMGCR expression inhibition with a decreased risk of RA, suggesting that HMGCR may serve as candidate drug targets for the treatment and prevention of RA.

Global burden of musculoskeletal disorders in children and adolescents from 1990 to 2021: a joint point regression and decomposition analysis.

The study aimed to investigate the pattern and trend of Musculoskeletal (MSK) disorders in people aged 5-19 years from 1990 to 2021. The data was sourced from the Global Burden of Disease study 2021. The Age-standardized DALYs rates (ASDR), age-standardized mortality rate (ASMR), age-standardized prevalence rate (ASPR), and age-standardized incidence rate (ASIR) and their corresponding average annual percent change (AAPC) for MSK disorders were evaluated by sex, region, and sociodemographic index (SDI) quintiles. Globally, the ASPR of MSK disorders among children and adolescents increased per 100,000 population from 3048.66 (95% confidence interval [CI]: 2336.68-3887.02) in 1990 to 3105.46 (95% CI: 2421.09-3904.95) in 2021 (AAPC 0.06 [95% CI: 0.05-0.07]). In 2021, individuals aged 15-19 experienced the highest burden compared to those aged 5-9 and 10-14. In 2021, high SDI countries had the highest ASIR, ASPR, ASDR of MSK disorders. The AAPC of ASPR in high SDI countries showed a stark contrast to that in low SDI countries for the same period (AAPC 0.48 vs. AAPC -0.03). From 1990 to 2021, in low SDI and low-middle SDI countries, the increase in DALYs was primarily due to population growth. However, in middle SDI, high-middle, and high SDI countries, the increases were mainly due to epidemiological changes. Globally, patients aged 10-14 experienced better care compared to those in the 5-9 and 15-19 age groups. Specific preventive health measures are needed for females and adolescents aged 15-19 in high SDI countries.

Changes in frailty and depressive symptoms among middle-aged and older Chinese people: a nationwide cohort study.

BACKGROUND AND AIMS: The older people bears a severe burden of disease due to frailty and depressive symptoms, however, the results of association between the two in the older Chinese people have been conflicting. Therefore, this study aimed to investigate the developmental trajectories and interactions of frailty and depressive symptoms in the Chinese middle-aged and older adults. METHODS: The study used four waves of data from 2011, 2013, 2015 and 2018 in the China Health and Retirement Longitudinal Study (CHARLS) database, focused on middle-aged and older people ≥ 45 years of age, and analyzed using latent growth models and cross-lagged models. RESULTS: The parallel latent growth model showed that the initial level of depressive symptoms had a significant positive predictive effect on the initial level of frailty. The rate of change in depressive symptoms significantly positively predicted the rate of change in frailty. The initial level of frailty had a significant positive predictive effect on the initial level of depressive symptoms, but a significant negative predictive effect on the rate of change in depressive symptoms. The rate of change in frailty had a significant positive predictive effect on the rate of change in depressive symptoms. The results of the cross-lagged analysis indicated a bidirectional causal association between frailty and depressive symptoms in the total sample population. Results for the total sample population grouped by age and gender were consistent with the total sample. CONCLUSIONS: This study recommends advancing the age of concern for frailty and depressive symptoms to middle-aged adults. Both men and women need early screening and intervention for frailty and depressive symptoms to promote healthy aging.

Essential amino acid residues and catalytic mechanism of trans-epoxysuccinate hydrolase for production of meso-tartaric acid.

OBJECTIVES: This study aimed to discuss the essential amino acid residues and catalytic mechanism of trans-epoxysuccinate hydrolase from Pseudomonas koreensis for the production of meso-tartaric acid. RESULTS: The optimum conditions of the enzyme were 45 °C and pH 9.0, respectively. It was strongly inhibited by Zn2+, Mn2+ and SDS. Michaelis-Menten enzyme kinetics analysis gave a Km value of 3.50 mM and a kcat of 99.75 s-1, with an exceptional EE value exceeding 99.9%. Multiple sequence alignment and homology modeling revealed that the enzyme belonged to MhpC superfamily and possessed a typical α/ß hydrolase folding structure. Site-directed mutagenesis indicated H34, D104, R105, R108, D128, Y147, H149, W150, Y211, and H272 were important catalytic residues. The 18O-labeling study suggested the enzyme acted via two-step catalytic mechanism. CONCLUSIONS: The structure and catalytic mechanism of trans-epoxysuccinate hydrolase were first reported. Ten residues were critical for its catalysis and a two-step mechanism by an Asp-His-Asp catalytic triad was proposed.

Serum albumin mediates the associations between heavy metals and two novel systemic inflammation indexes among U.S. adults.

BACKGROUND: The effects of heavy metal exposure on immunological function have sparked widespread concern, but unequivocal evidence on the association between mixed metal exposure and novel systemic inflammatory indexes remains scarce. OBJECTIVES: This study aimed to analyze the associations of heavy metals with two novel systemic inflammation indexes and the mediated effects of serum albumin. METHODS: Nineteen metals were detected among 4082 U.S. adults based on the NHANES. A linear regression, restricted cubic splines (RCS) regression, weighted quantile sum (WQS), Quantile-based Gcomputation (qgcomp), and Bayesian kernel machine regression (BKMR) were conducted to evaluate the associations of single metal and mixed metals with systemic immune-inflammation index (SII) and systemic inflammation response index (SIRI) levels, respectively. A series of subgroup analyses were used to identify potentially vulnerable populations. Furthermore, we conducted mediation analyses to investigate the mediated effects of serum albumin on the associations of metals with SII and SIRI. RESULTS: In the single-exposure model, exposure to various metals such as urinary Co, As, and serum Zn, Cu was associated with SII and SIRI (PFDR<0.05). Simultaneously, the above metals were linear positively correlated with SII and SIRI. Mixed-exposure analyses consistently showed that overall mixed urinary metal levels were positively pertinent for SII and SIRI levels, and the metal Co played a significant role in the urinary metal mixtures. Subgroup analyses showed that exposure to urinary Cd in men and elderly people increased SII and SIRI levels. The results of mediation analyses suggested the association of urinary metal mixture with SII and SIRI was mediated by albumin, and the proportion of mediation was 14.45% and 9.49%, respectively. CONCLUSIONS: Our findings suggested that metal exposure is strongly associated with the levels of system inflammation indexes and that serum albumin is, in part, a mediator of this association.

Interpretable machine learning model for predicting acute kidney injury in critically ill patients.

BACKGROUND: This study aimed to create a method for promptly predicting acute kidney injury (AKI) in intensive care patients by applying interpretable, explainable artificial intelligence techniques. METHODS: Population data regarding intensive care patients were derived from the Medical Information Mart for Intensive Care IV database from 2008 to 2019. Machine learning (ML) techniques with six methods were created to construct the predicted models for AKI. The performance of each ML model was evaluated by comparing the areas under the curve (AUC). Local Interpretable Model-Agnostic Explanations (LIME) method and Shapley Additive exPlanation values were used to decipher the best model. RESULTS: According to inclusion and exclusion criteria, 53,150 severely sick individuals were included in the present study, of which 42,520 (80%) were assigned to the training group, and 10,630 (20%) were allocated to the validation group. Compared to the other five ML models, the eXtreme Gradient Boosting (XGBoost) model greatly predicted AKI following ICU admission, with an AUC of 0.816. The top four contributing variables of the XGBoost model were SOFA score, weight, mechanical ventilation, and the Simplified Acute Physiology Score II. An AKI and Non-AKI cases were predicted separately using the LIME algorithm. CONCLUSION: Overall, the constructed clinical feature-based ML models are excellent in predicting AKI in intensive care patients. It would be constructive for physicians to provide early support and timely intervention measures to intensive care patients at risk of AKI.

Machine learning algorithm for predict the in-hospital mortality in critically ill patients with congestive heart failure combined with chronic kidney disease.

BACKGROUND: The objective of this study was to develop and validate a machine learning (ML) model for predict in-hospital mortality among critically ill patients with congestive heart failure (CHF) combined with chronic kidney disease (CKD). METHODS: After employing least absolute shrinkage and selection operator regression for feature selection, six distinct methodologies were employed in the construction of the model. The selection of the optimal model was based on the area under the curve (AUC). Furthermore, the interpretation of the chosen model was facilitated through the utilization of SHapley Additive exPlanation (SHAP) values and the Local Interpretable Model-Agnostic Explanations (LIME) algorithm. RESULTS: This study collected data and enrolled 5041 patients on CHF combined with CKD from 2008 to 2019, utilizing the Medical Information Mart for Intensive Care Unit. After selection, 22 of the 47 variables collected post-intensive care unit admission were identified as mortality-associated and subsequently utilized in the development of ML models. Among the six models generated, the eXtreme Gradient Boosting (XGBoost) model demonstrated the highest AUC at 0.837. Notably, the SHAP values highlighted the sequential organ failure assessment score, age, simplified acute physiology score II, and urine output as the four most influential variables in the XGBoost model. In addition, the LIME algorithm explains the individualized predictions. CONCLUSIONS: In conclusion, our study accomplished the successful development and validation of ML models for predicting in-hospital mortality in critically ill patients with CHF combined with CKD. Notably, the XGBoost model emerged as the most efficacious among all the ML models employed.

Unraveling the Origin of Multichannel Circularly Polarized Luminescence in a Pyrene-Functionalized Topologically Chiral [2]Catenane.

Mechanically interlocked molecules (MIMs) are promising platforms for developing functionalized artificial molecular machines. The construction of chiral MIMs with appealing circularly polarized luminescence (CPL) properties has boosted their potential application in biomedicine and the optical industry. However, there is currently little knowledge about the CPL emission mechanism or the emission dynamics of these related MIMs. Herein, we demonstrate that time-resolved circularly polarized luminescence (TRCPL) spectroscopy combined with transient absorption (TA) spectroscopy offers a feasible approach to elucidate the origins of CPL emission in pyrene-functionalized topologically chiral [2]catenane as well as in a series of pyrene-functionalized chiral molecules. For the first time, direct evidence differentiating the chiroptical signals originating from either topological (local state emission) or Euclidean chirality (excimer state emission) in these pyrene-functionalized chiral molecules has been discovered. Our work not only establishes a novel and ideal approach to study CPL mechanism, but also provides a theoretical foundation for the rational design of novel chiral materials in the future.

Avian influenza A virus H7N9 in China, a role reversal from reassortment receptor to the donator.

Reassortment can introduce one or more gene segments of influenza A viruses (IAVs) into another, resulting in novel subtypes. Since 2013, a new outbreak of human highly pathogenic avian influenza has emerged in the Yangtze River Delta (YRD) and South-Central regions of China. In this study, using Anhui province as an example, we discuss the possible impact of H7N9 IAVs on future influenza epidemics through a series of gene reassortment events. Sixty-one human H7N9 isolates were obtained from five outbreaks in Anhui province from 2013 to 2019. Bioinformatics analyses revealed that all of them were characterized by low pathogenicity and high human or mammalian tropism and had introduced novel avian influenza A virus (AIV) subtypes such as H7N2, H7N6, H9N9, H5N6, H6N6, and H10N6 through gene reassortment. In reassortment events, Anhui isolates may donate one or more segments of HA, NA, and the six internal protein-coding genes for the novel subtype AIVs. Our study revealed that H7N9, H9N2, and H5N1 can serve as stable and persistent gene pools for AIVs in the YRD and South-Central regions of China. Novel AIV subtypes might be generated continuously by reassortment. These AIVs may have obtained human-type receptor-binding abilities from their donors and prefer binding to them, which can cause human epidemics through accidental spillover infections. Facing the continual threat of emerging avian influenza, constant monitoring of AIVs should be conducted closely for agricultural and public health.

Distinct doorway states lead to triplet excited state generation in epigenetic RNA nucleosides.

N 6-Hydroxymethyladenosine (hm6A) and N6-formyladenosine (f6A) are two important intermediates during the demethylation process of N6-methyladenosine (m6A), which has been proven to show epigenetic function in mRNA. However, there is no knowledge about how the chemical integrity and stability could be altered when these two nucleosides are exposed to ultraviolet (UV) radiation. Herein, we report the first study on excited state dynamics of hm6A and f6A in solutions by using femtosecond time-resolved spectroscopy and quantum chemistry calculations. Surprisingly, triplet excited species are clearly identified in both hm6A and f6A after UV excitation, which is in sharp contrast to the 10-3 level triplet yield of adenosine scaffolds. Moreover, the doorway states leading to triplet states are found to be an intramolecular charge transfer state and a lower-lying dark nπ* state in hm6A and f6A, respectively. These discoveries pave the way to further study their effects on RNA strands and provide insight for understanding RNA photochemistry.

Emerging roles of air pollution and meteorological factors in autoimmune eye diseases.

Autoimmune eye diseases (AEDs), a collection of autoimmune inflammatory ocular conditions resulting from the dysregulation of immune system at the ocular level, can target both intraocular and periorbital structures leading to severe visual deficit and blindness globally. The roles of air pollution and meteorological factors in the initiation and progression of AEDs have been increasingly attractive, among which the systemic and local mechanisms are both involved in. Exposure to excessive air pollution and extreme meteorological conditions including PM2.5/PM0.1, environmental tobacco smoke, insufficient sunshine, and high temperature, etc., can disturb Th17/Treg balance, regulate macrophage polarization, activate neutrophils, induce systemic inflammation and oxidative stress, decrease retinal blood flow, promote tissue fibrosis, activate sympathetic nervous system, adversely affect nutrients synthetization, as well as induce heat stress, therefore may together deteriorate AEDs. The crosstalk among inflammation, oxidative stress and dysregulated immune system appeared to be prominent. In the present review, we will concern and summarize the potential mechanisms underlying linkages of air pollution and meteorological factors to ocular autoimmune and inflammatory responses. Moreover, we concentrate on the specific roles of air pollutants and meteorological factors in several major AEDs including uveitis, Graves' ophthalmopathy (GO), ocular allergic disease (OAD), glaucoma, diabetic retinopathy (DR), etc.

Impact of climate factors and climate-gene interaction on systemic lupus erythematosus patients' response to glucocorticoids therapy.

BACKGROUND: Glucocorticoids (GCs) were the essential drugs for systemic lupus erythematosus (SLE). However, different patients differ substantially in their response to GCs treatment. Our current study aims at investigating whether climate variability and climate-gene interaction influence SLE patients' response to the therapy of GCs. METHODS: In total, 778 SLE patients received therapy of GCs for a study of 12-week follow-up. The efficacy of GCs treatment was evaluated using the Systemic Lupus Erythematosus Disease Activity Index. The climatic data were provided by China Meteorological Data Service Center. Additive and multiplicative interactions were examined. RESULTS: Compared with patients with autumn onset, the efficacy of GCs in patients with winter onset is relatively poor (ORadj = 1.805, 95%CIadj : 1.181-3.014, padj = 0.020). High mean relative humidity during treatment decreased the efficacy of GCs (ORadj = 1.033, 95%CIadj : 1.008-1.058, padj = 0.011), especially in female (ORadj = 1.039, 95%CIadj : 1.012-1.067, padj = 0.004). There was a significant interaction between sunshine during treatment and TRAP1 gene rs12597773 on GCs efficacy (Recessive model: AP = 0.770). No evidence of significant interaction was found between climate factors and the GR gene polymorphism on the improved GCs efficacy in the additive model. Multiplicative interaction was found between humidity in the month prior to treatment and GR gene rs4912905 on GCs efficacy (Dominant model: OR = 0.470, 95%CI: 0.244-0.905, p = 0.024). CONCLUSIONS: Our findings suggest that climate variability influences SLE patients' response to the therapy of GCs. Interactions between climate and TRAP1/GR gene polymorphisms were related to GCs efficacy. The results guide the individualized treatment of SLE patients.

Global Chronic obstructive pulmonary disease burden attributable to air pollution from 1990 to 2019.

BACKGROUND: The disease burden attributable to chronic obstructive pulmonary disease (COPD) is significant worldwide. Some studies have linked exposure to air pollution to COPD, but there has been little research on this. METHODS: We aimed to assess the COPD-related disease burden attributable to air pollution from multiple epidemiological perspectives. This study conducted a three-stage analysis. Firstly, we reported on the burden of disease worldwide in 2019 by different subgroups including sex, age, region, and country. Secondly, we studied the trends in disease burden from 1990 to 2019. Finally, we explored the association of some national indicators with disease burden to look for risk factors. RESULTS: In 2019, the death number of COPD associated with air pollution accounted for 2.32% of the total global death, and the number of DALY accounted for 1.12% of the global DALY. From 1990 to 2019, the death number of COPD associated with air pollution increased peaked at 1.41 million in 1993, fluctuated, and then declined. We found the same temporal pattern of DALY. The corresponding age-standardized rates had been falling. At the same time, the burden of COPD associated with air pollution was also affected by some national indicators. CONCLUSIONS: This study indicated that air pollution-related COPD contributed to a significant global disease burden. We called for health policymakers to take action and interventions targeting vulnerable countries and susceptible populations.

Machine learning algorithm to predict the in-hospital mortality in critically ill patients with chronic kidney disease.

BACKGROUND: This study aimed to establish and validate a machine learning (ML) model for predicting in-hospital mortality in critically ill patients with chronic kidney disease (CKD). METHODS: This study collected data on CKD patients from 2008 to 2019 using the Medical Information Mart for Intensive Care IV. Six ML approaches were used to build the model. Accuracy and area under the curve (AUC) were used to choose the best model. In addition, the best model was interpreted using SHapley Additive exPlanations (SHAP) values. RESULTS: There were 8527 CKD patients eligible for participation; the median age was 75.1 (interquartile range: 65.0-83.5) years, and 61.7% (5259/8527) were male. We developed six ML models with clinical variables as input factors. Among the six models developed, the eXtreme Gradient Boosting (XGBoost) model had the highest AUC, at 0.860. According to the SHAP values, the sequential organ failure assessment score, urine output, respiratory rate, and simplified acute physiology score II were the four most influential variables in the XGBoost model. CONCLUSIONS: In conclusion, we successfully developed and validated ML models for predicting mortality in critically ill patients with CKD. Among all ML models, the XGBoost model is the most effective ML model that can help clinicians accurately manage and implement early interventions, which may reduce mortality in critically ill CKD patients with a high risk of death.