cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/hhv937v7pv.1
title: Artificial Intelligence-Powered Precision Medicine for Cardi-ovascular Disease Prevention and Management

description: Abstract
Artificial intelligence (AI) is transforming precision medicine, particularly in cardiovascular disease prevention and management. This bibliometric analysis examines the research land-scape from 2020 to 2024, focusing on AI's role in improving diagnostics, personalizing treatment, and advancing predictive healthcare. Using the PRISMA framework, VOSviewer, Harzing's Publish or Perish, and Excel, 137 articles from Scopus were systematically ana-lyzed. The study reveals a significant surge in research activity, with 2024 marking a peak. Machine learning and deep learning are central to key advancements, enabling early detec-tion and risk prediction. Contributions from leading institutions highlight the global and in-terdisciplinary nature of this field, with studies demonstrating AI's potential to integrate complex datasets and deliver tailored therapies. While AI-driven innovations show promise, challenges such as ethical concerns and healthcare disparities remain. This analysis under-scores AI's transformative potential in precision medicine and identifies opportunities for equitable, collaborative advancements.

Keywords: Artificial Intelligence, Machine Learning, Cardiovascular Disease, Heart Dis-ease, Coronary Artery Disease, Precision Medicine, Treatment
DOI: 10.5281/zenodo.12773304
title: Timing Performance Evaluation of a Dual-Axis Rotational PET System According to NEMA NU 4-2008 Standards: A Simulation Study

description: This repository contains the dataset used to identify easyPET.3D’s optimal performance based on NEMA standards. In addition, explores the impact of different CTWs on PET image quality by comparing simulated electronics capable of a 300 ps CTW with a 40 ns CTW 

The dataset is provided in the easyPET format, which includes:



Header: Contains metadata about the acquisition, structured as a binary file.







 Version_binary,

header,

dates,

otherinfo,

acquisitionInfo,

stringdata,

systemConfigurations_info,

energyfactor_info,

peakMatrix_info



The header is  processed into a JSON format. e.g.





systemConfigurations_info = np.fromfile(binary_file, dtype='|S1', count=size_systemConfigurations[0]* 2).astype('|U1')systemConfigurations_info = systemConfigurations_info.tolist()systemConfigurations_info = ''.join(systemConfigurations_info)systemConfigurations_info = json.loads(systemConfigurations_info)





7 or 8 arrays: stores the primary data. Motor angles, Detector IDs, Photon Energies and time of arrival 





number_of_arrays = 7 # or 8 for simulationsreading_data = np.fromfile(binary_file,                           dtype=[('stepbot', np.int16), ('steptop', np.int16),                                  ('idA', np.int16),                                  ('idB', np.int16),                                  ('EA', np.int16), ('EB', np.int16), ('time', np.float64),                                  ('time_b', np.float64)])







step_bot = round(header[1], 4) / header[2]step_top = round(header[3], 4) / header[4]topRange = header[5]listMode = np.zeros((len(reading_data["stepbot"]), number_of_arrays))listMode[:, 0] = reading_data["EA"]  # energias AlistMode[:, 1] = reading_data["EB"]  # energias BlistMode[:, 2] = reading_data["idA"]  # id AlistMode[:, 3] = reading_data["idB"]  # id bif open_files_to_join:    # This dont change the data in the way to merge the acquisitions files in just one    listMode[:, 4] = (reading_data["stepbot"]) # *2  # bot angle remove factor of 2 because it is already saved that way    listMode[:, 5] = (reading_data["steptop"] )  # top_angleelse:    listMode[:, 4] = (reading_data["stepbot"]) * step_bot #*2  # bot angle remove factor of 2 because it is already saved that way    listMode[:, 5] = (reading_data["steptop"] * step_top - topRange / 2)  # top_anglelistMode[:, 6] = reading_data["time"]  # timeif Version_binary ==  "Version 3":    listMode[:, 7] = reading_data["time_b"]  # time

15 December ---> Resolution test6 December ----> Sensitivity
DOI: 10.5281/zenodo.12773305
title: Timing Performance Evaluation of a Dual-Axis Rotational PET System According to NEMA NU 4-2008 Standards: A Simulation Study

description: This repository contains the dataset used to identify easyPET.3D’s optimal performance based on NEMA standards. In addition, explores the impact of different CTWs on PET image quality by comparing simulated electronics capable of a 300 ps CTW with a 40 ns CTW 

The dataset is provided in the easyPET format, which includes:



Header: Contains metadata about the acquisition, structured as a binary file.







 Version_binary,

header,

dates,

otherinfo,

acquisitionInfo,

stringdata,

systemConfigurations_info,

energyfactor_info,

peakMatrix_info



The header is  processed into a JSON format. e.g.





systemConfigurations_info = np.fromfile(binary_file, dtype='|S1', count=size_systemConfigurations[0]* 2).astype('|U1')systemConfigurations_info = systemConfigurations_info.tolist()systemConfigurations_info = ''.join(systemConfigurations_info)systemConfigurations_info = json.loads(systemConfigurations_info)





7 or 8 arrays: stores the primary data. Motor angles, Detector IDs, Photon Energies and time of arrival 





number_of_arrays = 7 # or 8 for simulationsreading_data = np.fromfile(binary_file,                           dtype=[('stepbot', np.int16), ('steptop', np.int16),                                  ('idA', np.int16),                                  ('idB', np.int16),                                  ('EA', np.int16), ('EB', np.int16), ('time', np.float64),                                  ('time_b', np.float64)])







step_bot = round(header[1], 4) / header[2]step_top = round(header[3], 4) / header[4]topRange = header[5]listMode = np.zeros((len(reading_data["stepbot"]), number_of_arrays))listMode[:, 0] = reading_data["EA"]  # energias AlistMode[:, 1] = reading_data["EB"]  # energias BlistMode[:, 2] = reading_data["idA"]  # id AlistMode[:, 3] = reading_data["idB"]  # id bif open_files_to_join:    # This dont change the data in the way to merge the acquisitions files in just one    listMode[:, 4] = (reading_data["stepbot"]) # *2  # bot angle remove factor of 2 because it is already saved that way    listMode[:, 5] = (reading_data["steptop"] )  # top_angleelse:    listMode[:, 4] = (reading_data["stepbot"]) * step_bot #*2  # bot angle remove factor of 2 because it is already saved that way    listMode[:, 5] = (reading_data["steptop"] * step_top - topRange / 2)  # top_anglelistMode[:, 6] = reading_data["time"]  # timeif Version_binary ==  "Version 3":    listMode[:, 7] = reading_data["time_b"]  # time

15 December ---> Resolution test6 December ----> Sensitivity
DOI: 10.17632/hhv937v7pv.2
title: Artificial Intelligence-Powered Precision Medicine for Cardi-ovascular Disease Prevention and Management

description: Abstract
Artificial intelligence (AI) is transforming precision medicine, particularly in cardiovascular disease prevention and management. This bibliometric analysis examines the research land-scape from 2020 to 2024, focusing on AI's role in improving diagnostics, personalizing treatment, and advancing predictive healthcare. Using the PRISMA framework, VOSviewer, Harzing's Publish or Perish, and Excel, 137 articles from Scopus were systematically ana-lyzed. The study reveals a significant surge in research activity, with 2024 marking a peak. Machine learning and deep learning are central to key advancements, enabling early detec-tion and risk prediction. Contributions from leading institutions highlight the global and in-terdisciplinary nature of this field, with studies demonstrating AI's potential to integrate complex datasets and deliver tailored therapies. While AI-driven innovations show promise, challenges such as ethical concerns and healthcare disparities remain. This analysis under-scores AI's transformative potential in precision medicine and identifies opportunities for equitable, collaborative advancements.

Keywords: Artificial Intelligence, Machine Learning, Cardiovascular Disease, Heart Dis-ease, Coronary Artery Disease, Precision Medicine, Treatment
DOI: 10.17632/wfdp6t7v2f.1
title: Artificial Intelligence in Medicine: Revolutionizing Healthcare Practices and Patient Outcomes

description: Abstract
Artificial intelligence (AI) has transformed medicine, advancing diagnostics, treatment, and patient outcomes. This study employs bibliometric analysis and the PRISMA framework to explore AI research trends in medicine from 2019 to 2023. Key findings reveal significant growth in publications, with radiomics, genomics, and predictive analytics as major focus areas. Federated learning frameworks and wearable technologies emerge as critical innova-tions, addressing challenges such as data privacy and real-time monitoring. Despite these advancements, significant barriers persist, including algorithmic bias, data confidentiality, and the need for infrastructure and training to integrate AI into clinical workflows. Future directions emphasize the importance of interdisciplinary collaboration, ethical AI develop-ment, and explainable models to ensure trust and equitable access. By addressing these chal-lenges, AI has the potential to revolutionize healthcare systems, optimize resource alloca-tion, and enhance personalized medicine. This study highlights the transformative role of AI in shaping the future of medicine and improving global health outcomes.

Keywords: Artificial Intelligence, Personalized Medicine, Healthcare Innovation, AI-Driven Diagnostics, Machine Learning, Deep Learning, Predictive Analytics
DOI: 10.17632/wfdp6t7v2f.2
title: Artificial Intelligence in Medicine: Revolutionizing Healthcare Practices and Patient Outcomes

description: Abstract
Artificial intelligence (AI) has transformed medicine, advancing diagnostics, treatment, and patient outcomes. This study employs bibliometric analysis and the PRISMA framework to explore AI research trends in medicine from 2019 to 2023. Key findings reveal significant growth in publications, with radiomics, genomics, and predictive analytics as major focus areas. Federated learning frameworks and wearable technologies emerge as critical innova-tions, addressing challenges such as data privacy and real-time monitoring. Despite these advancements, significant barriers persist, including algorithmic bias, data confidentiality, and the need for infrastructure and training to integrate AI into clinical workflows. Future directions emphasize the importance of interdisciplinary collaboration, ethical AI develop-ment, and explainable models to ensure trust and equitable access. By addressing these chal-lenges, AI has the potential to revolutionize healthcare systems, optimize resource alloca-tion, and enhance personalized medicine. This study highlights the transformative role of AI in shaping the future of medicine and improving global health outcomes.

Keywords: Artificial Intelligence, Personalized Medicine, Healthcare Innovation, AI-Driven Diagnostics, Machine Learning, Deep Learning, Predictive Analytics
DOI: 10.17632/g82zmwvh8n.1
title: Predictive Analytics for Mitigating Supply Chain Disruptions

description: These are the initial dataset used to predict disruptions in the supply chain. There is yet a lot of other factors that need to be considered but as a MVP of such an ML model i have taken these and normalized and cleaned them
DOI: 10.17632/g82zmwvh8n.2
title: Predictive Analytics for Mitigating Supply Chain Disruptions

description: These are the initial dataset used to predict disruptions in the supply chain. There is yet a lot of other factors that need to be considered but as a MVP of such an ML model i have taken these and normalized and cleaned them
DOI: 10.5281/zenodo.15095451
title: Ben Johnston: String Quartet No. 7, 3rd movement: Superposition of the different scale types

description: In his theoretical work “Rational Structure in Music” (1976), ben Johnston explains how scales of varying degrees of density are structurally connected. Johnston names four levels for the size of the intervals:1. triadic2. diatonic3. chromatic4. ultrachromatic 

The final movement of the seventh string quartet is designed as a superposition of different scales, analogous to this description. On the one hand, these are used to generate the tonal material; on the other, the formal proportions correspond to those of the ratios used. This translation process gives the scales a structure-forming effect and they become the blueprint of the movement.
DOI: 10.5281/zenodo.15095452
title: Ben Johnston: String Quartet No. 7, 3rd movement: Superposition of the different scale types

description: In his theoretical work “Rational Structure in Music” (1976), ben Johnston explains how scales of varying degrees of density are structurally connected. Johnston names four levels for the size of the intervals:1. triadic2. diatonic3. chromatic4. ultrachromatic 

The final movement of the seventh string quartet is designed as a superposition of different scales, analogous to this description. On the one hand, these are used to generate the tonal material; on the other, the formal proportions correspond to those of the ratios used. This translation process gives the scales a structure-forming effect and they become the blueprint of the movement.
DOI: 10.6084/m9.figshare.28677731.v1
title: Supplementary Material for: Relation of Serum Creatinine Twitches and Outcomes among STEMI Patients

description: Background: Acute kidney injury (AKI) is notoriously associated with adverse outcomes and mortality in patients with acute coronary syndrome. However, using the general cutoff of 0.3 mg/dL increase from baseline for AKI definition and neglecting smaller changes could result in late diagnosis and impaired prognostication. We aimed to assess the prognostic utility of minor creatinine changes ("twitches") in a large cohort of ST-segment-elevation myocardial infraction (STEMI) patients and determine an optimal cutoff value for future use. 

Methods: This retrospective analysis of a prospective database included 2933 consecutive patients admitted with STEMI between 2008-2022 to the cardiac intensive care unit of a large tertiary medical center. Renal function was assessed upon admission and at-least once daily thereafter. Creatinine twitches were defined as a change from baseline to peak creatinine level of between 0.1 to 0.3 mg/dl. 30-day and 1-year mortality were the main outcomes.

Results: From the study cohort (mean age 62 ±13, 19% female, 16% with prior MI), 551 (19%) subjects presented creatinine twitches and 254 (9%) developed AKI. Compared to subjects with stable creatinine, those with creatinine twitches had higher rates of 30-day (1% vs. 2.5%, p&lt;0.001) and 1-year (1.6% vs. 4.4%, p&lt;0.001) mortality. In cox multivariate analysis, creatinine twitches had a higher hazard for 1-year mortality (HR 1.87, 95% CI 1.1-3.2) and only a trend for 30-day mortality (HR 1.52, 95% CI 0.96-2.96). Creatinine rise had an area under the curve of 0.780 (95% CI 0.73-0.83) for 1-year mortality prediction, and 0.12 mg/dl was the optimal cutoff for prediction, with a sensitivity of 71%, specificity of 79%. In sub-group multivariate analysis, only twitches that did not resolve during hospitalization had higher hazard for mortality (HR 3.42, 95% CI 1.65-7.05). 

Conclusion: Serum creatinine twitches are common among STEMI patients and correlate with elevated 30 days and 1-year mortality. These seemingly minor changes should prompt renal protective strategies for early detection and treatment.
DOI: 10.6084/m9.figshare.28677734
title: Supplementary Material for: Advancing Precision Medicine for hypertensive nephropathy: a novel prognostic model incorporating pathological indicators

description: Introduction: This study aims to assess the long-term renal prognosis of patients with hypertensive nephropathy (HN) diagnosed through renal biopsy, utilizing the random survival forest (RSF) algorithm.
Methods: From December 2010 to December 2022, HN patients diagnosed by renal biopsy in Xijing Hospital were enrolled and randomly divided into training set and testing set at a ratio of 7∶3. The study's composite endpoint was defined as a ≥ 50% decline in estimated glomerular filtration rate (eGFR), ESRD, or death. RSF and Cox regression were used to establish a renal prognosis prediction model based on the factors screened by the RSF algorithm. The Concordance index (C-index), integrated brier score (IBS), net reclassification index (NRI), and integrated discrimination improvement (IDI) were used to evaluate discrimination, calibration, and risk classification, respectively.
Results: A total of 225 patients were included in this study, with 72 (32.0%) patients experiencing combined events after a median follow-up of 29.9 (16.6, 52.1) months. Six eligible variables (overall chronicity grade of renal pathology, eGFR, high-density lipoprotein cholesterol, hematocrit, monocyte, and stroke volume) were selected from clinical data and introduced into the RSF model. The RSF model had a higher C-index in both the training set [0.904 (95%CI 0.842 - 0.938) vs 0.831 (95%CI 0.768 - 0.894), P &lt; 0.001] and the testing set [0.893 (95%CI 0.770 - 0.944) vs 0.841 (95%CI 0.751 - 0.931), P = 0.021] compared to the Cox model. NRI and IDI indicated that the RSF model outperformed the Cox model regarding risk classification and discrimination.
Conclusion: In this study, the RSF algorithm was employed to identify the risk factors affecting the prognosis of HN patients, and a clinical prognostic RSF model was constructed to predict the adverse outcomes of HN patients based on renal pathology. Compared to the traditional Cox regression model, the RSF model offers superior performance and can provide valuable new insights for clinical diagnosis and treatment strategies.
DOI: 10.6084/m9.figshare.28677737.v1
title: Supplementary Material for: Investigating the Mechanisms of Reduced Blood Transfusions after Delayed Umbilical Cord Clamping: The TITANS causal mediation analysis

description: Introduction
Delaying clamping of the umbilical cord (DCC) in preterm infants reduces mortality and need for blood transfusions. The mechanisms leading to these benefits are not well understood. The TITANS study investigates potential mediators of the reduction in blood transfusions in infants who received DCC.

Materials and Methods
Additional patient data was sourced from Australian and New Zealand sites from the Australian Placental Transfusion Study (APTS). APTS randomized preterm infants &lt;30 weeks’ gestation to receive DCC (60 seconds) or immediate-cord-clamping (ICC). We examined whether placental transfusion or initial severity of illness mediated the reduced requirement for blood transfusions for infants randomized to DCC. Peak haematocrit in the first 7 days (Hct) was used as an indicator of placental transfusion quantity. Cumulative blood sampled, mechanical ventilation, and arterial sampling lines were used as indicators of severity if illness. We quantified the natural indirect effect of peak Hct and then for all mediators in a joint model with sequential mediation.

Results
Data from 1260 (of 1401) Australian and New Zealand APTS infants were obtained. The effect of DCC on subsequent blood transfusion was mediated through peak Hct (indirect effect OR = 0.85, 95%CI:0.79-0.93; p&lt;0.001), which accounted for 37% of the total effect. Indicators of severity of illness did not mediate the effect independently of peak Hct. 

Discussion/Conclusion
Peak Hct mediated some, but not all, of the effect of DCC on blood transfusion, whereas markers of severity of illness were not independent mediators.
DOI: 10.17632/x4r2y8kwjv.1
title: Nicotinamide Mononucleotide Ameliorates Alzheimer’s Disease Symptoms through Modulation of Gut Microbiota in APP/PS1 Mice

description: Research Hypothesis:
NMN supplementation can alleviate AD symptoms in APP/PS1 mice by modulating gut microbiota and its metabolites, influencing brain health through the gut-brain axis.
Data Collection and Methodology:
APP/PS1 (AD) and C57BL/6 (NC) mice were divided into four groups: ADNMN, ADPBS, NCNMN, and NCPBS. Experiments included microbiota analysis (DNA extraction, sequencing, MENs, PCA, LEfSe), metabolite analysis (HPLC-MS/MS), pathological analysis (Alcian Blue staining, immunohistochemistry), ROS detection, RNA sequencing for GO and KEGG enrichment, Morris water maze test for spatial memory, ATP and mitochondrial complex I activity measurement, and Western blotting for protein expression.
Notable Findings:
1. NMN restored intestinal flora structure and composition in AD mice, increasing MEN complexity and resilience. PCA showed similarity to normal controls, and LEfSe identified reduced bacteria (Catellicoccus, Lactobacillus_aviarius, and uncultured_bacterium) levels in ADNMN.
2. After 16 weeks of NMN treatment, most metabolite changes in AD mice reversed, with Acylcarnitine 14:1, Corticosterone, and Taurine showing significant improvement. Spearman correlation analysis revealed that Bacteroides, uncultured-bacterium-c-Gammaproteobacteria, and Campylobacter were key genera affecting metabolite differences between ADNMN and ADPBS groups. In ADNMN, Bacteroides and Enterorhabdus correlated positively with LysoPI 18:0, LysoPE 12:0, and DHA methyl ester, while in ADPBS, Gemella correlated positively with LysoPG 16:0, Cer (AP) 33:2, LysoPG 18:1, and LysoPI 20:3. 
3.NMN altered metabolic pathways in AD mice, promoting nervous system development and reducing abnormal immune activation. Key microbial taxa were identified, with metabolites impacting AD development.
4. NMN increased goblet cell counts in both AD and healthy mice, enhancing the mucus barrier to protect against microbial invasion. RNA-Seq analysis showed elevated expression of mucus-related genes (Clca1 and Fcgbp) in the ADNMN group. NMN also partially reversed downregulated genes in AD mice, upregulating immune and cell adhesion pathways while downregulating oxidative stress and lipid metabolism genes. In vivo imaging showed NMN reduces colon ROS, lessening oxidative stress damage in AD mice. 
5. In the MWM test, ADPBS mice had worse performance than NCPBS mice during training (days 1-5). NMN improved metrics for both groups. On day 6, ADPBS mice showed cognitive impairments, but NMN helped restore their spatial memory.
6.NMN mitigated brain inflammation and oxidative stress in AD mice through pathway regulation, reducing lipids, azelaic acid metabolism, and key acids.
7. NMN restored ATP levels and mitochondrial complex I activity in AD mice's hippocampus. It also enhanced synaptic density and reversed synaptic dysfunction by increasing presynaptic and postsynaptic protein expression.
DOI: 10.17632/yyckd5nkmw
title: Dataset for East Java Analysis

description: Dataset for East Java Analysis
DOI: 10.17632/yyckd5nkmw.1
title: Dataset for East Java Analysis

description: Dataset for East Java Analysis
DOI: 10.5281/zenodo.15095385
title: Ben Johnston: String Quartet No. 7, 1st movement, Forceful 3: Chordal structure

description: The harmonization of “Forceful 3” again consists mainly of triads with a tridecimal sixth ajoutée. Unlike in “Forceful 2”, the connecting intervals between the central notes are now much more unusual.
DOI: 10.5281/zenodo.15095386
title: Ben Johnston: String Quartet No. 7, 1st movement, Forceful 3: Chordal structure

description: The harmonization of “Forceful 3” again consists mainly of triads with a tridecimal sixth ajoutée. Unlike in “Forceful 2”, the connecting intervals between the central notes are now much more unusual.
DOI: 10.17632/682svc2j4x.1
title: File B1

description: FASTA sequences of the homologous gene family of SbPDI1-1, SbPD2-1a, SbPDI2-1b and SbPDI5-1 from 1,355 different eukaryotic species obtained from the Plaza 5.0 database, and used for the MSA alignment.
DOI: 10.17632/8vt7tjvg8k
title: "Dogs' Catching Mice" Conjecture

description: Minimal anonymized dataset of The “Dogs’ Catching Mice” conjecture in Chinese phonogram processing