cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.29606114
title: The predictive value of hyaluronic acid for the severity and prognosis of COVID-19: a retrospective multicenter cohort study

description: The aim of this study was to elucidate the relationship between HA levels and clinical outcomes, including disease severity and prognosis. A retrospective multicenter cohort analysis was conducted across three medical centers in China from September 2022 to February 2023. Patients with positive nucleic acid for the novel coronavirus were included in the study. The primary endpoint is 28-day mortality.The severity was assessed by the ‘Diagnosis and Treatment Plan for Novel Coronavirus Pneumonia (Trial Version 9)’. Analyses assessed HA correlation with severity (Kendall), predictive value for mortality (logistic regression, ROC, Delong test), and survival differences (KM analysis using a Youden index-derived HA cutoff). A total of 862 patients (median age 72; 58.47% male) were included in the study, of whom 108 died. Non-survivors were older, more men, lower BMI, and more complicatinos. The overall median HA level was 121.65 ng/ml, significantly higher in non-survivors(<i>p</i> &lt; 0.001). Kendall correlation analysis revealed a significant correlation between HA level and COVID-19 severity (correlation coefficient: 0.365, <i>p</i> &lt; 0.001). Our preliminary analysis revealed positive correlations between HA and D-dimer and chest CT. In logistic regression models, HA was an independent predictor of 28-day mortality(<i>p</i> = 0.001). The AUROC for HA was 0.840 (95% CI: 0.806-0.974). The cut-off value was 141.65 ng/ml (sensitivity 61.27%, specificity 91.67%). KM survival analysis showed that patients with HA levels higher than 141.65 ng/ml had a significantly lower survival probability(<i>p</i> &lt; 0.001). HA may potentially function as a biomarker for COVID-19 and could provide a vital reference for the clinical treatment.
DOI: 10.6084/m9.figshare.29606114.v1
title: The predictive value of hyaluronic acid for the severity and prognosis of COVID-19: a retrospective multicenter cohort study

description: The aim of this study was to elucidate the relationship between HA levels and clinical outcomes, including disease severity and prognosis. A retrospective multicenter cohort analysis was conducted across three medical centers in China from September 2022 to February 2023. Patients with positive nucleic acid for the novel coronavirus were included in the study. The primary endpoint is 28-day mortality.The severity was assessed by the ‘Diagnosis and Treatment Plan for Novel Coronavirus Pneumonia (Trial Version 9)’. Analyses assessed HA correlation with severity (Kendall), predictive value for mortality (logistic regression, ROC, Delong test), and survival differences (KM analysis using a Youden index-derived HA cutoff). A total of 862 patients (median age 72; 58.47% male) were included in the study, of whom 108 died. Non-survivors were older, more men, lower BMI, and more complicatinos. The overall median HA level was 121.65 ng/ml, significantly higher in non-survivors(<i>p</i> &lt; 0.001). Kendall correlation analysis revealed a significant correlation between HA level and COVID-19 severity (correlation coefficient: 0.365, <i>p</i> &lt; 0.001). Our preliminary analysis revealed positive correlations between HA and D-dimer and chest CT. In logistic regression models, HA was an independent predictor of 28-day mortality(<i>p</i> = 0.001). The AUROC for HA was 0.840 (95% CI: 0.806-0.974). The cut-off value was 141.65 ng/ml (sensitivity 61.27%, specificity 91.67%). KM survival analysis showed that patients with HA levels higher than 141.65 ng/ml had a significantly lower survival probability(<i>p</i> &lt; 0.001). HA may potentially function as a biomarker for COVID-19 and could provide a vital reference for the clinical treatment.
DOI: 10.6084/m9.figshare.29437250
title: Diagnostic pathway for cardiac amyloidosis from the healthcare professional’s perspective: results from the French DIAM-ATTR survey

description: Diagnosis of cardiac amyloidosis (CA) is complex and implicates several medical specialists. CA is usually suspected based on symptoms (‘red flags’) and non-invasive imagery. Early diagnosis and appropriate treatment are critical in patients with CA. The DIAM-ATTR survey assessed the diagnostic pathway, from the French healthcare professional’s (HCPs) perspective, for patients with transthyretin amyloidosis (ATTR)-cardiomyopathy (CM). Between February and March 2023, 13,830 HCPs were solicited to complete a 35-question survey. Among the 13,830 HCPs solicited, 1264 HCPs completed the survey: 471 cardiologists, 186 internists, 148 nuclear medicine physicians, 125 geriatricians, 120 orthopaedic surgeons, 112 neurologists, and 102 rheumatologists. In general, echocardiographic abnormalities, heart failure, and a family history of amyloid neuropathy evoked CA. The knowledge of the 22 ‘red flags’ assessed varied among specialists. Among HCPs, 70% had suspected an ATTR-CM: from 96% of cardiologist to 6% of orthopaedic surgeons. Complete diagnosis was performed by 48% of both cardiologists and internists. The other HCPs referred patients to colleagues for complete diagnosis. Overall, echocardiography was performed first, then gammopathy assessment and bone scintigraphy. Delays for examinations and difficulties varied among specialists. Overall, French HCPs prioritize diagnostic examinations for ATTR-CM as recommended. However, HCPs need an increased awareness of ‘red flags’ and the importance of excluding monoclonal gammopathies during diagnosis.
DOI: 10.6084/m9.figshare.29437250.v1
title: Diagnostic pathway for cardiac amyloidosis from the healthcare professional’s perspective: results from the French DIAM-ATTR survey

description: Diagnosis of cardiac amyloidosis (CA) is complex and implicates several medical specialists. CA is usually suspected based on symptoms (‘red flags’) and non-invasive imagery. Early diagnosis and appropriate treatment are critical in patients with CA. The DIAM-ATTR survey assessed the diagnostic pathway, from the French healthcare professional’s (HCPs) perspective, for patients with transthyretin amyloidosis (ATTR)-cardiomyopathy (CM). Between February and March 2023, 13,830 HCPs were solicited to complete a 35-question survey. Among the 13,830 HCPs solicited, 1264 HCPs completed the survey: 471 cardiologists, 186 internists, 148 nuclear medicine physicians, 125 geriatricians, 120 orthopaedic surgeons, 112 neurologists, and 102 rheumatologists. In general, echocardiographic abnormalities, heart failure, and a family history of amyloid neuropathy evoked CA. The knowledge of the 22 ‘red flags’ assessed varied among specialists. Among HCPs, 70% had suspected an ATTR-CM: from 96% of cardiologist to 6% of orthopaedic surgeons. Complete diagnosis was performed by 48% of both cardiologists and internists. The other HCPs referred patients to colleagues for complete diagnosis. Overall, echocardiography was performed first, then gammopathy assessment and bone scintigraphy. Delays for examinations and difficulties varied among specialists. Overall, French HCPs prioritize diagnostic examinations for ATTR-CM as recommended. However, HCPs need an increased awareness of ‘red flags’ and the importance of excluding monoclonal gammopathies during diagnosis.
DOI: 10.6084/m9.figshare.29126407
title: Construction of a predictive model for relapse of primary autoimmune hemolytic anemia: a retrospective cohort study

description: To develop a machine learning-based model to predict the relapse risk of Primary Autoimmune Haemolytic Anaemia (AIHA) after the last remission. A retrospective study was conducted on primary AIHA cases who visited the Affiliated Hospital of Southwest Medical University and Xuyong County People’s Hospital from May 2017 to May 2022. Cases were categorized as relapsed or non-relapsed based on the 1-year outcomes. Twenty-two features were analyzed to identify relapse risk factors. The least absolute shrinkage and selection operator (LASSO) regression model and multivariate logistic regression analysis were used to establish a predictive model. The <i>C</i>-index, Calibration curves, ROC, and Decision curve analysis (DCA) were used to evaluate the discriminatory, corrective, accurate, and clinical effectiveness of the predictive model. A total of 232 cases of primary AIHA were included, and five potential variables including ‘DAT results’, ‘Hb’, ‘Multiline therapy’, ‘Complicating ITP’, and ‘Complicating infection’, have been screened for constructing a 1-year relapse risk prediction nomogram for primary AIHA. The nomogram has a <i>C</i>-index of 0.852 (95% CI: 0.797–0.907), confirmed by bootstrapping validation as 0.829. The area under the ROC was 0.846. The DCA shows that when the threshold probability is in the range of 1 ∼ 91%. By following the current diagnostic and treatment criteria for AIHA in China, we retrospectively collect a multitude of medical records and analyze several relevant variables of AIHA, construct a predictive model by machine learning. Using this 1-year relapse risk nomogram can effectively predict the risk of relapse within 1 year after remission of primary AIHA.
DOI: 10.6084/m9.figshare.29126407.v1
title: Construction of a predictive model for relapse of primary autoimmune hemolytic anemia: a retrospective cohort study

description: To develop a machine learning-based model to predict the relapse risk of Primary Autoimmune Haemolytic Anaemia (AIHA) after the last remission. A retrospective study was conducted on primary AIHA cases who visited the Affiliated Hospital of Southwest Medical University and Xuyong County People’s Hospital from May 2017 to May 2022. Cases were categorized as relapsed or non-relapsed based on the 1-year outcomes. Twenty-two features were analyzed to identify relapse risk factors. The least absolute shrinkage and selection operator (LASSO) regression model and multivariate logistic regression analysis were used to establish a predictive model. The <i>C</i>-index, Calibration curves, ROC, and Decision curve analysis (DCA) were used to evaluate the discriminatory, corrective, accurate, and clinical effectiveness of the predictive model. A total of 232 cases of primary AIHA were included, and five potential variables including ‘DAT results’, ‘Hb’, ‘Multiline therapy’, ‘Complicating ITP’, and ‘Complicating infection’, have been screened for constructing a 1-year relapse risk prediction nomogram for primary AIHA. The nomogram has a <i>C</i>-index of 0.852 (95% CI: 0.797–0.907), confirmed by bootstrapping validation as 0.829. The area under the ROC was 0.846. The DCA shows that when the threshold probability is in the range of 1 ∼ 91%. By following the current diagnostic and treatment criteria for AIHA in China, we retrospectively collect a multitude of medical records and analyze several relevant variables of AIHA, construct a predictive model by machine learning. Using this 1-year relapse risk nomogram can effectively predict the risk of relapse within 1 year after remission of primary AIHA.
DOI: 10.6084/m9.figshare.31116313
title: Dataset and R codes

description: Dataset and R code for manuscript: Developing vegetation tools for assessing the ecological condition of wetlands in western Patagonia
DOI: 10.6084/m9.figshare.31116313.v1
title: Dataset and R codes

description: Dataset and R code for manuscript: Developing vegetation tools for assessing the ecological condition of wetlands in western Patagonia
DOI: 10.6084/m9.figshare.29291073
title: The performance of artificial intelligence in image-based prediction of hematoma enlargement: a systematic review and meta-analysis

description: Accurately predicting hematoma enlargement (HE) is crucial for improving the prognosis of patients with cerebral haemorrhage. Artificial intelligence (AI) is a potentially reliable assistant for medical image recognition. This study systematically reviews medical imaging articles on the predictive performance of AI in HE. Retrieved relevant studies published before October, 2024 from Embase, Institute of Electrical and Electronics Engineers (IEEE), PubMed, Web of Science, and Cochrane Library databases. The diagnostic test of predicting hematoma enlargement based on CT image training artificial intelligence model, and reported 2 × 2 contingency tables or provided sensitivity (SE) and specificity (SP) for calculation. Two reviewers independently screened the retrieved citations and extracted data. The methodological quality of studies was assessed using the QUADAS-AI, and Preferred Reporting Items for Systematic reviews and Meta-Analyses was used to ensure standardised reporting of studies. Subgroup analysis was performed based on sample size, risk of bias, year of publication, ratio of training set to test set, and number of centres involved. 36 articles were included in this Systematic review to qualitative analysis, of which 23 have sufficient information for further quantitative analysis. Among these articles, there are a total of 7 articles used deep learning (DL) and 16 articles used machine learning (ML). The comprehensive SE and SP of ML are 78% (95% CI: 69–85%) and 85% (78–90%), respectively, while the AUC is 0.89 (0.86–0.91). The SE and SP of DL was 87% (95% CI: 80–92%) and 75% (67–81%), respectively, with an AUC of 0.88 (0.85–0.91). The subgroup analysis found that when the ratio of the training set to the test set is 7:3, the sensitivity is 0.77(0.62–0.91), <i>p</i> = 0.03; In terms of specificity, the group with sample size more than 200 has higher specificity, which is 0.83 (0.75–0.92), <i>p</i> = 0.02; among the risk groups in the study design, the specificity of the risk group was higher, which was 0.83 (0.76–0.89), <i>p</i> = 0.02. The group specificity of articles published before 2021 was higher, 0.84 (0.77–0.90); and the specificity of data from a single research centre was higher, which was 0.85 (0.80–0.91), <i>p</i> &lt; 0.001. Artificial intelligence algorithms based on imaging have shown good performance in predicting HE.
DOI: 10.6084/m9.figshare.29291073.v1
title: The performance of artificial intelligence in image-based prediction of hematoma enlargement: a systematic review and meta-analysis

description: Accurately predicting hematoma enlargement (HE) is crucial for improving the prognosis of patients with cerebral haemorrhage. Artificial intelligence (AI) is a potentially reliable assistant for medical image recognition. This study systematically reviews medical imaging articles on the predictive performance of AI in HE. Retrieved relevant studies published before October, 2024 from Embase, Institute of Electrical and Electronics Engineers (IEEE), PubMed, Web of Science, and Cochrane Library databases. The diagnostic test of predicting hematoma enlargement based on CT image training artificial intelligence model, and reported 2 × 2 contingency tables or provided sensitivity (SE) and specificity (SP) for calculation. Two reviewers independently screened the retrieved citations and extracted data. The methodological quality of studies was assessed using the QUADAS-AI, and Preferred Reporting Items for Systematic reviews and Meta-Analyses was used to ensure standardised reporting of studies. Subgroup analysis was performed based on sample size, risk of bias, year of publication, ratio of training set to test set, and number of centres involved. 36 articles were included in this Systematic review to qualitative analysis, of which 23 have sufficient information for further quantitative analysis. Among these articles, there are a total of 7 articles used deep learning (DL) and 16 articles used machine learning (ML). The comprehensive SE and SP of ML are 78% (95% CI: 69–85%) and 85% (78–90%), respectively, while the AUC is 0.89 (0.86–0.91). The SE and SP of DL was 87% (95% CI: 80–92%) and 75% (67–81%), respectively, with an AUC of 0.88 (0.85–0.91). The subgroup analysis found that when the ratio of the training set to the test set is 7:3, the sensitivity is 0.77(0.62–0.91), <i>p</i> = 0.03; In terms of specificity, the group with sample size more than 200 has higher specificity, which is 0.83 (0.75–0.92), <i>p</i> = 0.02; among the risk groups in the study design, the specificity of the risk group was higher, which was 0.83 (0.76–0.89), <i>p</i> = 0.02. The group specificity of articles published before 2021 was higher, 0.84 (0.77–0.90); and the specificity of data from a single research centre was higher, which was 0.85 (0.80–0.91), <i>p</i> &lt; 0.001. Artificial intelligence algorithms based on imaging have shown good performance in predicting HE.
DOI: 10.6084/m9.figshare.30029735
title: Diagnostic PANoptosis-related genes in acute kidney injury: bioinformatics, machine learning, and validation

description: Acute kidney injury (AKI) is a prevalent and life-threatening condition characterized by abrupt renal function decline and subsequent inflammatory cascades. PANoptosis has emerged as a significant contributor to the pathophysiology of AKI. This research aimed to explore the diagnostic and therapeutic implications of PANoptosis-related genes in AKI. Kidney biopsy transcriptomic expression data were obtained from the GEO database. Differentially expressed genes (DEGs) associated with PANoptosis were identified between AKI and controls. WGCNA identified hub PANoptosis-related genes. PANoptosis scores and immune cell infiltration were calculated by ssGSEA. Machine learning algorithms was used to select feature genes. ROC analysis evaluated their diagnostic performance. Drug–gene interactions were explored. We identified 3460 DEGs between AKI and controls (61 upregulated and 11 downregulated) related to PANoptosis, mainly enriched in cytokine signaling and apoptosis. Eight hub PANoptosis genes were identified. PANoptosis scores were significantly higher in AKI patients (<i>p</i> &lt; 0.001). CASP8, CASP4, SFN, FAS, and CASP1 were selected as feature genes, with CASP8 having the highest AUC at 0.850 in the training set. A nomogram combining these genes demonstrated strong predictive power. Furthermore, these genes were related to immune cell infiltration positively and had potential drug associations. Validation in a renal ischemia–reperfusion injury rat model confirmed the upregulation of CASP8 (<i>p</i> &lt; 0.01), CASP4 (<i>p</i> &lt; 0.001), SFN (<i>p</i> &lt; 0.0001), FAS (<i>p</i> &lt; 0.01), and CASP1 (<i>p</i> &lt; 0.01). Our study identifies PANoptosis-related genes as potential diagnostic markers and therapeutic targets in AKI, highlighting their role in immune dysregulation in AKI.
DOI: 10.6084/m9.figshare.30029735.v1
title: Diagnostic PANoptosis-related genes in acute kidney injury: bioinformatics, machine learning, and validation

description: Acute kidney injury (AKI) is a prevalent and life-threatening condition characterized by abrupt renal function decline and subsequent inflammatory cascades. PANoptosis has emerged as a significant contributor to the pathophysiology of AKI. This research aimed to explore the diagnostic and therapeutic implications of PANoptosis-related genes in AKI. Kidney biopsy transcriptomic expression data were obtained from the GEO database. Differentially expressed genes (DEGs) associated with PANoptosis were identified between AKI and controls. WGCNA identified hub PANoptosis-related genes. PANoptosis scores and immune cell infiltration were calculated by ssGSEA. Machine learning algorithms was used to select feature genes. ROC analysis evaluated their diagnostic performance. Drug–gene interactions were explored. We identified 3460 DEGs between AKI and controls (61 upregulated and 11 downregulated) related to PANoptosis, mainly enriched in cytokine signaling and apoptosis. Eight hub PANoptosis genes were identified. PANoptosis scores were significantly higher in AKI patients (<i>p</i> &lt; 0.001). CASP8, CASP4, SFN, FAS, and CASP1 were selected as feature genes, with CASP8 having the highest AUC at 0.850 in the training set. A nomogram combining these genes demonstrated strong predictive power. Furthermore, these genes were related to immune cell infiltration positively and had potential drug associations. Validation in a renal ischemia–reperfusion injury rat model confirmed the upregulation of CASP8 (<i>p</i> &lt; 0.01), CASP4 (<i>p</i> &lt; 0.001), SFN (<i>p</i> &lt; 0.0001), FAS (<i>p</i> &lt; 0.01), and CASP1 (<i>p</i> &lt; 0.01). Our study identifies PANoptosis-related genes as potential diagnostic markers and therapeutic targets in AKI, highlighting their role in immune dysregulation in AKI.
DOI: 10.6084/m9.figshare.29476453
title: The spectrum of overlapping anti-NMDAR encephalitis and demyelinating syndromes: a systematic review of presentation, diagnosis, management, and outcomes

description: Anti-NMDAR encephalitis frequently overlaps with demyelinating diseases (MOGAD, NMOSD, MS), creating complex syndromes with diverse presentations and challenging management. Systematic search of databases including MEDLINE, Google Scholar, Embase, Scopus, Cochrane Library, and Web of Science up to March 2024 for studies on co-existing anti-NMDAR encephalitis and demyelinating syndromes. Data extracted on clinical characteristics, diagnostics, treatments, and outcomes. Twenty-five studies identified 256 patients (16.2%) with co-existing Anti-NMDAR encephalitis and demyelinating syndromes, primarily MOGAD (94.5%), with fewer cases involving NMOSD or MS. The Anti-NMDAR + MOGAD subgroup exhibited seizures (51–72.7%), psychiatric symptoms (45.5–71.4%), cognitive dysfunction (30.6%), and movement disorders (30.6%). All patients had CSF anti-NMDAR antibodies, with MOG (60%) or AQP4 (25%) antibodies. Use of standardized, cell-based assays and adherence to established criteria are essential to avoid false positives, particularly for MOG. MRI abnormalities were seen in 75% of patients. First-line immunotherapies were effective in 70% of cases; 80% of refractory cases responded to second-line therapies. Anti-NMDAR encephalitis overlapping with demyelinating diseases is challenging. Tailored treatments based on detailed immune profiles are key to better outcomes.
DOI: 10.6084/m9.figshare.29476453.v1
title: The spectrum of overlapping anti-NMDAR encephalitis and demyelinating syndromes: a systematic review of presentation, diagnosis, management, and outcomes

description: Anti-NMDAR encephalitis frequently overlaps with demyelinating diseases (MOGAD, NMOSD, MS), creating complex syndromes with diverse presentations and challenging management. Systematic search of databases including MEDLINE, Google Scholar, Embase, Scopus, Cochrane Library, and Web of Science up to March 2024 for studies on co-existing anti-NMDAR encephalitis and demyelinating syndromes. Data extracted on clinical characteristics, diagnostics, treatments, and outcomes. Twenty-five studies identified 256 patients (16.2%) with co-existing Anti-NMDAR encephalitis and demyelinating syndromes, primarily MOGAD (94.5%), with fewer cases involving NMOSD or MS. The Anti-NMDAR + MOGAD subgroup exhibited seizures (51–72.7%), psychiatric symptoms (45.5–71.4%), cognitive dysfunction (30.6%), and movement disorders (30.6%). All patients had CSF anti-NMDAR antibodies, with MOG (60%) or AQP4 (25%) antibodies. Use of standardized, cell-based assays and adherence to established criteria are essential to avoid false positives, particularly for MOG. MRI abnormalities were seen in 75% of patients. First-line immunotherapies were effective in 70% of cases; 80% of refractory cases responded to second-line therapies. Anti-NMDAR encephalitis overlapping with demyelinating diseases is challenging. Tailored treatments based on detailed immune profiles are key to better outcomes.