cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.26990280.v1
title: The effectiveness of targeted therapy for recurrence or metastasis adenoid cystic carcinoma: a systematic review and meta-analysis

description: Several clinical studies have demonstrated the potential of molecular-targeted agents for the treatment of recurrent or metastatic adenoid cystic carcinoma (R/M ACC). However, there is currently no consensus regarding the efficacy of molecular-targeted agents for patients with R/M ACC. This study aimed to evaluate the therapeutic efficacy and safety of molecular-targeted agents in patients with R/M ACC and provide insights to guide clinical decision-making. Five databases (PubMed, Embase, Cochrane, ProQuest, and Scopus) were searched based on the search strategy and selection criteria. Primary endpoints were objective response rate (ORR) and progression-free survival (PFS). The secondary endpoints were disease control rate (DCR), overall survival (OS), metastatic sites, and adverse events (AE). Pooled estimates were calculated using a random-effects meta-analysis. Finally, 28 studies, involving 849 patients, were included. The most common metastatic sites were the lungs, bones, liver, lymph nodes, and kidneys. The pooled ORR was 4.0% (95% CI, 0.7–8.8%), the pooled DCR was 80.5% (95% CI, 72.2%–87.7%). Compared with other-target drugs, multiple kinase inhibitors (MKIs) improved the ORR (pooled ORR for single-target drugs vs. MKIs: 5.9% vs. 0%). The combination of MKIs and immune checkpoint inhibitors (ICIs) had a significantly higher ORR (17.9% in the axitinib + avelumab group). The pooled median PFS and OS were 8.35 and 25.62 months, respectively. MKIs improved the median PFS compared to other-target drugs (9.43 months vs 5.06 months). In addition, the most common adverse events (AEs) were fatigue (51.6%), hypertension (44.2%), and nausea (40.0%), followed by hand-foot skin syndrome (36.8%), diarrhoea (34.4%), weight loss (34.2%), anorexia (31.8%), rash (31.7%), and headache (29.0%). The findings of this study suggest that MKIs have a better therapeutic efficacy than single-target drugs in patients with R/M ACC. Future studies are warranted to verify the synergistic role of the combination strategy of MKIs plus ICIs, given the limited number of studies on this topic conducted and published to date.
DOI: 10.6084/m9.figshare.26796945
title: Efficacy of corticosteroids in patients with acute respiratory distress syndrome: a meta-analysis

description: Acute respiratory distress syndrome (ARDS), are respiratory diseases with high morbidity and mortality. Clinical trials investigating the efficacy of corticosteroids in the treatment of ARDS often yield contradictory results. We hereby conducted a systematic review and meta-analysis to investigate the efficacy of corticosteroids in ARDS management. We conducted a search for randomized clinical trials (RCT) and observational studies that utilized corticosteroids for patients with ARDS in Web of Science, PubMed, and Embase. The primary outcome was mortality. Risk of bias was assessed using Cochrane or NOS scales. Statistical effect size was analyzed using the Mantel-Haenszel method. A total of 20 studies, comprising 11 observational studies and 9 RCTs, were eligible for analysis. In RCTs, corticosteroids were associated with a reduction of mortality in ARDS patients (relative risk [RR] = 0.80, 95%CI: 0.71-0.91, <i>p</i> = 0.001). Further subgroup analysis indicated that specific variables, such as low-dose (RR = 0.81; 95%CI: 0.67-0.98; <i>p</i> = 0.034), methylprednisolone (RR = 0.70; 95%CI: 0.49-0.98; <i>p</i> = 0.035), and dexamethasone (RR = 0.82; 95%CI: 0.69-0.98; <i>p</i> = 0.029) were associated with mortality among patients receiving corticosteroids. However, in observational studies, corticosteroids increased the risk of death (RR = 1.16, 95%CI: 1.04-1.29; <i>p</i> = 0.001). Subgroup analysis showed that the use of high-dose corticosteroids was associated with higher patient mortality (RR = 1.20; 95%CI: 1.04-1.38; <i>p</i> = 0.001). The efficacy of corticosteroids on the mortality of ARDS differed by the type and dosage of corticosteroids used, as well as the etiologies. Current data do not support routine use of corticosteroids in ARDS since protective effects were observed in RCTs but increased mortality was found in observational studies. More well designed and large clinical trials are needed to specify the favorable subgroups for corticosteroid therapy. Corticosteroid use may reduce the risk of death in patients with acute respiratory distress syndrome (ARDS) according to randomized controlled trials. Observational studies indicate that corticosteroid use may increase the risk of death in non-COVID-19 ARDS patients but not in COVID-19 ARDS patients. Both regular and low-dose corticosteroids show benefits in reducing mortality in RCTs, but observational studies associate these doses with increased mortality.
DOI: 10.6084/m9.figshare.26796945.v1
title: Efficacy of corticosteroids in patients with acute respiratory distress syndrome: a meta-analysis

description: Acute respiratory distress syndrome (ARDS), are respiratory diseases with high morbidity and mortality. Clinical trials investigating the efficacy of corticosteroids in the treatment of ARDS often yield contradictory results. We hereby conducted a systematic review and meta-analysis to investigate the efficacy of corticosteroids in ARDS management. We conducted a search for randomized clinical trials (RCT) and observational studies that utilized corticosteroids for patients with ARDS in Web of Science, PubMed, and Embase. The primary outcome was mortality. Risk of bias was assessed using Cochrane or NOS scales. Statistical effect size was analyzed using the Mantel-Haenszel method. A total of 20 studies, comprising 11 observational studies and 9 RCTs, were eligible for analysis. In RCTs, corticosteroids were associated with a reduction of mortality in ARDS patients (relative risk [RR] = 0.80, 95%CI: 0.71-0.91, <i>p</i> = 0.001). Further subgroup analysis indicated that specific variables, such as low-dose (RR = 0.81; 95%CI: 0.67-0.98; <i>p</i> = 0.034), methylprednisolone (RR = 0.70; 95%CI: 0.49-0.98; <i>p</i> = 0.035), and dexamethasone (RR = 0.82; 95%CI: 0.69-0.98; <i>p</i> = 0.029) were associated with mortality among patients receiving corticosteroids. However, in observational studies, corticosteroids increased the risk of death (RR = 1.16, 95%CI: 1.04-1.29; <i>p</i> = 0.001). Subgroup analysis showed that the use of high-dose corticosteroids was associated with higher patient mortality (RR = 1.20; 95%CI: 1.04-1.38; <i>p</i> = 0.001). The efficacy of corticosteroids on the mortality of ARDS differed by the type and dosage of corticosteroids used, as well as the etiologies. Current data do not support routine use of corticosteroids in ARDS since protective effects were observed in RCTs but increased mortality was found in observational studies. More well designed and large clinical trials are needed to specify the favorable subgroups for corticosteroid therapy. Corticosteroid use may reduce the risk of death in patients with acute respiratory distress syndrome (ARDS) according to randomized controlled trials. Observational studies indicate that corticosteroid use may increase the risk of death in non-COVID-19 ARDS patients but not in COVID-19 ARDS patients. Both regular and low-dose corticosteroids show benefits in reducing mortality in RCTs, but observational studies associate these doses with increased mortality.
DOI: 10.6084/m9.figshare.27037189
title: Serum metabolomics profile identifies patients with community-acquired pneumonia infected by bacteria, fungi, and viruses

description: Patients with bacterial, fungal, and viral community-acquired pneumonia (CAP) were studied to determine their metabolic profiles. Loop-mediated isothermal amplification technology and nucleic acid sequence-dependent amplification combined with microfluidic chip technology were applied to screen multiple pathogens from respiratory tract samples. Eighteen patients with single bacterial infection (B-CAP), fifteen with single virus infection (V-CAP), twenty with single fungal infection (F-CAP), and twenty controls were enrolled. UHPLC-MS/MS analysis of untargeted serum samples for metabolic profiles. Multiple linear regression and Spearman’s rank correlation analysis were used to determine associations between metabolites and clinical parameters. The sensitivity and specificity of the screened metabolites were also examined, along with their area under the curve. The metabolic signatures of patients with CAP infected by bacteria, viruses, and fungi were markedly different from those of controls. The abundances of 45, 56, and 79 metabolites were significantly unbalanced. Among these differential metabolites, 11, 13, and 29 were unique to the B-CAP, V-CAP, and F-CAP groups, respectively. Bacterial infections were the only known causes of disturbances in the pentose and glucuronate and aldarate and ascorbate metabolism interconversions metabolic pathway. Serum metabolomic techniques based on UHPLC-MS/MS may identify differences between individuals with CAP who have been infected by various pathogens, and they can also build a metabolite signature for early detection of the origin of infection and prompt care.
DOI: 10.6084/m9.figshare.27037189.v1
title: Serum metabolomics profile identifies patients with community-acquired pneumonia infected by bacteria, fungi, and viruses

description: Patients with bacterial, fungal, and viral community-acquired pneumonia (CAP) were studied to determine their metabolic profiles. Loop-mediated isothermal amplification technology and nucleic acid sequence-dependent amplification combined with microfluidic chip technology were applied to screen multiple pathogens from respiratory tract samples. Eighteen patients with single bacterial infection (B-CAP), fifteen with single virus infection (V-CAP), twenty with single fungal infection (F-CAP), and twenty controls were enrolled. UHPLC-MS/MS analysis of untargeted serum samples for metabolic profiles. Multiple linear regression and Spearman’s rank correlation analysis were used to determine associations between metabolites and clinical parameters. The sensitivity and specificity of the screened metabolites were also examined, along with their area under the curve. The metabolic signatures of patients with CAP infected by bacteria, viruses, and fungi were markedly different from those of controls. The abundances of 45, 56, and 79 metabolites were significantly unbalanced. Among these differential metabolites, 11, 13, and 29 were unique to the B-CAP, V-CAP, and F-CAP groups, respectively. Bacterial infections were the only known causes of disturbances in the pentose and glucuronate and aldarate and ascorbate metabolism interconversions metabolic pathway. Serum metabolomic techniques based on UHPLC-MS/MS may identify differences between individuals with CAP who have been infected by various pathogens, and they can also build a metabolite signature for early detection of the origin of infection and prompt care.
DOI: 10.6084/m9.figshare.27926751
title: Triglyceride-glucose index and its additive interaction with <i>ABCG2/SLC2A9</i> polygenic risk score on hyperuricemia in middle age and older adults: findings from the DLCC and BHMC study

description: We aim to investigate the joint effect of triglyceride-glucose (TyG) index and polygenic risk scores (PRS) of urate transporter genes <i>ABCG2</i> and <i>SLC2A9</i> on hyperuricemia. Baseline data from two prospective population-based cohort studies, including 30,453 individuals aged 50 years or older, were used to analyze the association between TyG index and hyperuricemia. A case-control study was then designed from the cohorts to investigate the interaction between genetic predisposition and TyG index on hyperuricemia among 595 matched pairs. PRS was constructed using 14 single nucleotide polymorphisms located in the <i>ABCG2</i> and <i>SLCA29</i> genes. In both sexes, higher TyG index levels were correlated with elevated serum urate (SUA) levels (<i>p</i> values in both sexes &lt; 0.001). In men, per unit increase of TyG was associated with a 1.44-fold (95% confidence interval [CI]: 1.35–1.55) higher risk of hyperuricemia after adjusted for covariates. In women, this estimate was 1.69 (1.51–1.89). Demonstrated by the restrict cubic spline model, TyG index was both linearly and non-linearly associated with elevated SUA (both <i>p</i> values &lt; 0.001). Association between TyG index and hyperuricemia was stronger among people with higher genetic risk, and vice versa. Compared to people with TyG &lt; 9 and PRS &lt; 2, the odds ratios (ORs) (95% CIs) for hyperuricemia in the TyG &lt;9 but PRS ≥2, TyG ≥9 but PRS &lt; 2, TyG ≥9 and PRS ≥2 groups were 3.30 (1.53–7.14), 3.16 (1.23–8.11) and 7.55 (2.76–20.65), respectively. Additive interaction was also significant, with 57.5% (30.5%–84.4%) of the excess risk attributable to the additive gene-TyG index interaction. The impact of genetic predisposition on hyperuricemia was significantly greater among individuals with a higher TyG index. Over 50% of the increased risk can be attributed to the interaction, indicating a crucial synergy between genetic factors and TyG index when estimating hyperuricemia risk. There are both linear and non-linear relationships between TyG index and serum urates.Genetic predisposition on hyperuricemia was significantly greater among individuals with a higher TyG index.Over 50% of the increased risk of hyperuricemia can be attributed to the genetic-phenotype interaction. There are both linear and non-linear relationships between TyG index and serum urates. Genetic predisposition on hyperuricemia was significantly greater among individuals with a higher TyG index. Over 50% of the increased risk of hyperuricemia can be attributed to the genetic-phenotype interaction.
DOI: 10.6084/m9.figshare.27926751.v1
title: Triglyceride-glucose index and its additive interaction with <i>ABCG2/SLC2A9</i> polygenic risk score on hyperuricemia in middle age and older adults: findings from the DLCC and BHMC study

description: We aim to investigate the joint effect of triglyceride-glucose (TyG) index and polygenic risk scores (PRS) of urate transporter genes <i>ABCG2</i> and <i>SLC2A9</i> on hyperuricemia. Baseline data from two prospective population-based cohort studies, including 30,453 individuals aged 50 years or older, were used to analyze the association between TyG index and hyperuricemia. A case-control study was then designed from the cohorts to investigate the interaction between genetic predisposition and TyG index on hyperuricemia among 595 matched pairs. PRS was constructed using 14 single nucleotide polymorphisms located in the <i>ABCG2</i> and <i>SLCA29</i> genes. In both sexes, higher TyG index levels were correlated with elevated serum urate (SUA) levels (<i>p</i> values in both sexes &lt; 0.001). In men, per unit increase of TyG was associated with a 1.44-fold (95% confidence interval [CI]: 1.35–1.55) higher risk of hyperuricemia after adjusted for covariates. In women, this estimate was 1.69 (1.51–1.89). Demonstrated by the restrict cubic spline model, TyG index was both linearly and non-linearly associated with elevated SUA (both <i>p</i> values &lt; 0.001). Association between TyG index and hyperuricemia was stronger among people with higher genetic risk, and vice versa. Compared to people with TyG &lt; 9 and PRS &lt; 2, the odds ratios (ORs) (95% CIs) for hyperuricemia in the TyG &lt;9 but PRS ≥2, TyG ≥9 but PRS &lt; 2, TyG ≥9 and PRS ≥2 groups were 3.30 (1.53–7.14), 3.16 (1.23–8.11) and 7.55 (2.76–20.65), respectively. Additive interaction was also significant, with 57.5% (30.5%–84.4%) of the excess risk attributable to the additive gene-TyG index interaction. The impact of genetic predisposition on hyperuricemia was significantly greater among individuals with a higher TyG index. Over 50% of the increased risk can be attributed to the interaction, indicating a crucial synergy between genetic factors and TyG index when estimating hyperuricemia risk. There are both linear and non-linear relationships between TyG index and serum urates.Genetic predisposition on hyperuricemia was significantly greater among individuals with a higher TyG index.Over 50% of the increased risk of hyperuricemia can be attributed to the genetic-phenotype interaction. There are both linear and non-linear relationships between TyG index and serum urates. Genetic predisposition on hyperuricemia was significantly greater among individuals with a higher TyG index. Over 50% of the increased risk of hyperuricemia can be attributed to the genetic-phenotype interaction.
DOI: 10.15468/dl.7cr4fu
title: Occurrence Download

description: A dataset containing 234 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-5.4823 36.11497,-5.49008 36.11531,-5.49555 36.11274,-5.49243 36.11217,-5.48895 36.11472,-5.48694 36.11404,-5.48173 36.11451,-5.4823 36.11497))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Plantae"
 ]
}

 The dataset includes 234 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014541-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.x4w45p
title: Occurrence Download

description: A dataset containing 260 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-5.47703 36.1175,-5.4823 36.11497,-5.49008 36.11531,-5.49555 36.11274,-5.49243 36.11217,-5.48895 36.11472,-5.48694 36.11404,-5.48173 36.11451,-5.47703 36.1175))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Plantae"
 ]
}

 The dataset includes 260 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014542-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.26779780
title: Development and validation of risk prediction model for adverse outcomes in trauma patients

description: The prognosis of trauma patients is highly dependent on early medical diagnosis. By constructing a nomogram model, the risk of adverse outcomes can be displayed intuitively and individually, which has important clinical implications for medical diagnosis. To develop and evaluate models for predicting patients with adverse outcomes of trauma that can be used in different data availability settings in China. This was a retrospective prognostic study using data from 8 public tertiary hospitals in China from 2018. The data were randomly divided into a development set and a validation set. Simple, improved and extended models predicting adverse outcomes were developed, with adverse outcomes defined as in-hospital death or ICU transfer, and patient clinical characteristics, vital signs, diagnoses, and laboratory test values as predictors. The results of the models were presented in the form of nomograms, and performance was evaluated using area under the receiver operating characteristic curve (ROC-AUC), precision-recall (PR) curves (PR-AUC), Hosmer-Lemeshow goodness-of-fit test, calibration curve, and decision curve analysis (DCA). Our final dataset consisted of 18,629 patients (40.2% female, mean age of 52.3), 1,089 (5.85%) of whom resulted in adverse outcomes. In the external validation set, three models achieved ROC-AUC of 0.872, 0.881, and 0.903, and a PR-AUC of 0.339, 0.337, and 0.403, respectively. In terms of the calibration curves and DCA, the models also performed well. This prognostic study found that three prediction models and nomograms including the patient clinical characteristics, vital signs, diagnoses, and laboratory test values can support clinicians in more accurately identifying patients who are at risk of adverse outcomes in different settings based on data availability.
DOI: 10.15468/dl.bg62cv
title: Occurrence Download

description: A dataset containing 260 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-5.47703 36.1175,-5.4823 36.11497,-5.49008 36.11531,-5.49555 36.11274,-5.49243 36.11217,-5.48895 36.11472,-5.48694 36.11404,-5.48173 36.11451,-5.47703 36.1175))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Plantae"
 ]
}

 The dataset includes 260 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014543-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.26779780.v1
title: Development and validation of risk prediction model for adverse outcomes in trauma patients

description: The prognosis of trauma patients is highly dependent on early medical diagnosis. By constructing a nomogram model, the risk of adverse outcomes can be displayed intuitively and individually, which has important clinical implications for medical diagnosis. To develop and evaluate models for predicting patients with adverse outcomes of trauma that can be used in different data availability settings in China. This was a retrospective prognostic study using data from 8 public tertiary hospitals in China from 2018. The data were randomly divided into a development set and a validation set. Simple, improved and extended models predicting adverse outcomes were developed, with adverse outcomes defined as in-hospital death or ICU transfer, and patient clinical characteristics, vital signs, diagnoses, and laboratory test values as predictors. The results of the models were presented in the form of nomograms, and performance was evaluated using area under the receiver operating characteristic curve (ROC-AUC), precision-recall (PR) curves (PR-AUC), Hosmer-Lemeshow goodness-of-fit test, calibration curve, and decision curve analysis (DCA). Our final dataset consisted of 18,629 patients (40.2% female, mean age of 52.3), 1,089 (5.85%) of whom resulted in adverse outcomes. In the external validation set, three models achieved ROC-AUC of 0.872, 0.881, and 0.903, and a PR-AUC of 0.339, 0.337, and 0.403, respectively. In terms of the calibration curves and DCA, the models also performed well. This prognostic study found that three prediction models and nomograms including the patient clinical characteristics, vital signs, diagnoses, and laboratory test values can support clinicians in more accurately identifying patients who are at risk of adverse outcomes in different settings based on data availability.
DOI: 10.15468/dl.j8yn77
title: Occurrence Download

description: A dataset containing 741 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-5.4823 36.11497,-5.49008 36.11531,-5.49555 36.11274,-5.49243 36.11217,-5.48895 36.11472,-5.48694 36.11404,-5.48173 36.11451,-5.4823 36.11497))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 741 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014544-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.