bluuebunny/crossref_metadata_2025_split huggingface dataset:
Predicting mortality in critically ill patients with hypertension using machine learning and deep learning models
Authors: Zhang Ziyang , Ye Jiancheng 
Published: August 2024

ABSTRACTBackgroundAccurate prediction of mortality in critically ill patients with hypertension admitted to the Intensive Care Unit (ICU) is essential for guiding clinical decision-making and improving patient outcomes. Traditional prognostic tools often fall short in capturing the complex interactions between clinical variables in this high-risk population. Recent advances in machine learning (ML) and deep learning (DL) offer the potential for developing more sophisticated and accurate predictive models.ObjectiveThis study aims to evaluate the performance of various ML and DL models in predicting mortality among critically ill patients with hypertension, with a particular focus on identifying key clinical predictors and assessing the comparative effectiveness of these models.MethodsWe conducted a retrospective analysis of 30,096 critically ill patients with hypertension admitted to the ICU. Various ML models, including logistic regression, decision trees, and support vector machines, were compared with advanced DL models, including 1D convolutional neural networks (CNNs) and long short-term memory (LSTM) networks. Model performance was evaluated using area under the receiver operating characteristic curve (AUC) and other performance metrics. SHapley Additive exPlanations (SHAP) values were used to interpret model outputs and identify key predictors of mortality.ResultsThe 1D CNN model with an initial selection of predictors achieved the highest AUC (0.7744), outperforming both traditional ML models and other DL models. Key clinical predictors of mortality identified across models included the APS-III score, age, and length of ICU stay. The SHAP analysis revealed that these predictors had a substantial influence on model predictions, underscoring their importance in assessing mortality risk in this patient population.ConclusionDeep learning models, particularly the 1D CNN, demonstrated superior predictive accuracy compared to traditional ML models in predicting mortality among critically ill patients with hypertension. The integration of these models into clinical workflows could enhance the early identification of high-risk patients, enabling more targeted interventions and improving patient outcomes. Future research should focus on the prospective validation of these models and the ethical considerations associated with their implementation in clinical practice.
Development of an optimized machine learning approach for assessing brain metastatic burden in preclinical models
Authors: Rappaport Jessica , Chen Quanyi , McGuire Tomi , Daugherty-Lopès Amélie , Goldszmid Romina 
Published: August 2024

AbstractBrain metastases (BrM) occur when malignant cells spread from a primary tumor located in other parts of the body to the brain. BrM is a deadly complication for cancer patients and currently lacks effective therapies. Due to the limited access to patient samples, preclinical models remain a valuable tool for studying metastasis development, progression, and response to therapy. Thus, reliable methods for quantifying metastatic burden in these models are crucial. Here, we describe step by step a new semi-automatic machine-learning approach to quantify metastatic burden on mouse whole-brain stereomicroscope images while preserving tissue integrity. This protocol utilizes the open-source, user-friendly image analysis software QuPath. The method is fast, reproducible, unbiased, and provides access to data points not always obtainable with other existing strategies.
Speech Synthesis from Electrocorticogram During Imagined Speech Using a Transformer-Based Decoder and Pretrained Vocoder
Authors: Komeiji Shuji , Shigemi Kai , Mitsuhashi Takumi , Iimura Yasushi , Suzuki Hiroharu , Sugano Hidenori , Shinoda Koichi , Yatabe Kohei , Tanaka Toshihisa 
Published: August 2024

AbstractThis study describes speech synthesis from an Electrocorticogram (ECoG) during imagined speech. We aim to generate high-quality audio despite the limitations of available training data by employing a Transformer-based decoder and a pretrained vocoder. Specifically, we used a pre-trained neural vocoder, Parallel WaveGAN, to convert the log-mel spectrograms output by the Transformer decoder, which was trained on ECoG signals, into high-quality audio signals. In our experiments, using ECoG signals recorded from 13 participants, the synthesized speech from imagined speech achieved a dynamic time-warping (DTW) Pearson correlation ranging from 0.85 to 0.95. This high-quality speech synthesis can be attributed to the Transformer decoder’s ability to accurately reconstruct high-fidelity log-mel spectrograms, demonstrating its effectiveness in dealing with limited training data.
Tracking clonal evolution of drug resistance in ovarian cancer patients by exploiting structural variants in cfDNA
Authors: Williams Marc J. , Vázquez-García Ignacio , Tam Grittney , Wu Michelle , Varice Nancy , Havasov Eliyahu , Shi Hongyu , Satas Gryte , Lees Hannah J. , Lee Jake June-Koo , Myers Matthew A. , Zatzman Matthew , Rusk Nicole , Ali Emily , Shah Ronak H , Berger Michael F. , Mohibullah Neeman , Lakhman Yulia , Chi Dennis S. , Abu-Rustum Nadeem R. , Aghajanian Carol , McPherson Andrew , Zamarin Dmitriy , Loomis Brian , Weigelt Britta , Friedman Claire F. , Shah Sohrab P. 
Published: August 2024

ABSTRACTDrug resistance is the major cause of therapeutic failure in high-grade serous ovarian cancer (HGSOC). Yet, the mechanisms by which tumors evolve to drug resistant states remains largely unknown. To address this, we aimed to exploit clone-specific genomic structural variations by combining scaled single-cell whole genome sequencing with longitudinally collected cell-free DNA (cfDNA), enabling clonal tracking before, during and after treatment. We developed a cfDNA hybrid capture, deep sequencing approach based on leveraging clone-specific structural variants as endogenous barcodes, with orders of magnitude lower error rates than single nucleotide variants in ctDNA (circulating tumor DNA) detection, demonstrated on 19 patients at baseline. We then applied this to monitor and model clonal evolution over several years in ten HGSOC patients treated with systemic therapy from diagnosis through recurrence. We found drug resistance to be polyclonal in most cases, but frequently dominated by a single high-fitness and expanding clone, reducing clonal diversity in the relapsed disease state in most patients. Drug-resistant clones frequently displayed notable genomic features, including high-level amplifications of oncogenes such asCCNE1,RAB25,NOTCH3, andERBB2. Using a population genetics Wright-Fisher model, we found evolutionary trajectories of these features were consistent with drug-induced positive selection. In select cases, these alterations impacted selection of secondary lines of therapy with positive patient outcomes. For cases with matched single-cell RNA sequencing data, pre-existing and genomically encoded phenotypic states such as upregulation of EMT and VEGF were linked to drug resistance. Together, our findings indicate that drug resistant states in HGSOC pre-exist at diagnosis and lead to dramatic clonal expansions that alter clonal composition at the time of relapse. We suggest that combining tumor single cell sequencing with cfDNA enables clonal tracking in patients and harbors potential for evolution-informed adaptive treatment decisions.
Alterations in the Brain Lipidome of Alzheimer’s Disease Donors with Rare TREM2 Risk Variants
Authors: Proitsi Petroula , Ebshiana Amera , Wretlind Asger , Xu Jin , Hodges Angela , Legido-Quigley Cristina 
Published: August 2024

AbstractTREM2 is a microglial receptor, sensitive to Phospholipids and Sphingomyelins, associated with neurodegeneration. Hypomorphic variants in theTREM2gene significantly increase the risk of developing AD.The main aim of this study was to characterize networks of lipids in post-mortem brain from AD and control donors, and to identify key lipids that are associated with AD and impacted by dysfunctional TREM2.We studied human post-mortem brain tissue from the hippocampus and the BA9 pre-association cortex from a total of 102 post-mortem brains. Specifically, brain tissue from the BA9 pre-association cortex was available for n=55 donors and brain tissue from the hippocampus was available for n=47 brain donors from three groups: AD donors with a non-synonymous risk DNA variant inTREM2(AD-TREM2var), AD donors with noTREM2-associated variant (AD-TREM2wt), and control donors. Mass Spectrometry was performed to obtain lipidomic signatures spanning 99 lipid species that included the following lipid classes: Ceramides, Sphingomyelins, Phosphatidic acids, Phopshatidyl-cholines, Phosphatidyl-ethalonamines, Phosphatidyl-glycerols, Phosphatidyl-inositols, Phosphatidyl-serines and Triglycerides. Weighted gene co-expression network analysis (WGCNA) was used to identify highly correlated lipid modules and ‘hub’ lipids. Linear mixed models and linear regression analyses, adjusted for age, biological sex, number of APOEε4 alleles and post-mortem delay were used to assess the association of modules and individual hub lipids with AD andTREM2status.Four lipid modules were found to be relatively well-preserved between the two brain regions, and three of these modules were altered in AD donors and/or in AD donors withTREM2genetic variants. Levels of the BA9 “turquoise” module (“blue” hippocampus module), enriched in Sphingolipids and Phospholipids, were elevated in AD brain donors and in AD TREM2 carriers. The key lipid (hub) of the BA9 “turquoise”/ hippocampus “blue” module was a Phosphatidylserine (PS(32:1)), increased in both AD donors and TREM2 carriers (AD-TREM2wtversus controls: beta=0.468, 95% CI 0.05 – 0.89, p= 3.02E-02; and AD-TREM2varversus controls: beta=1.00, 95% CI 0.53 – 1.47, p= 5.57E-03), whereas the strongest association was observed with a Ceramide (Cer(d38:1)) (AD-TREM2wtversus controls: beta=0.663, 95% CI 0.17 – 1.16, p= 8.9E-03; and AD-TREM2varversus controls: beta=1.31, 95% CI 0.78 – 1.84, p= 4.35E-06).The consistent increase in TREM2 ligands such as Ceramides and Phosphatidyl-serines in the brains of AD donors, particularly in carriers ofTREM2risk variants, could reflect the presence of AD-associated damage signals in the form of stressed/apoptotic cells and damaged myelin.
Effectiveness of bimodal neuromodulation for tinnitus treatment in a real-world clinical setting in United States: A retrospective chart review
Authors: McMahan Emily E. , Lim Hubert H. 
Published: August 2024

AbstractBimodal neuromodulation combining sound therapy with electrical tongue stimulation using the Lenire device is emerging as an effective treatment for tinnitus. A single-arm retrospective chart review analyzes real-world outcomes for 220 tinnitus patients from the Alaska Hearing and Tinnitus Center for the recently FDA-approved Lenire treatment for the first time in a United States clinic. The primary endpoint examines the responder rate and mean change in Tinnitus Handicap Inventory (THI) after approximately 12 weeks of treatment in eligible patients with moderate or worse tinnitus. A responder represents a THI improvement of greater than seven points (i.e., minimal clinically important difference, MCID). Of 212 patients with available data, there was a high responder rate of 91.5% (95% CI: 86.9%, 94.5%) with a mean improvement of 27.8 ± 1.3 (SEM) points, and no device-related serious adverse events. Furthermore, a THI MCID of seven points represents a consistent criterion for clinical benefit based on real-world evidence.
Evidence of Differences in Diurnal Electrodermal, Temperature, and Heart Rate Patterns by Mental Health Status in Free-Living Data
Authors: McDuff Daniel , Galatzer-Levy Isaac , Thomson Seamus , Barakat Andrew , Heneghan Conor , Abdel-Ghaffar Samy , Sunshine Jake , Zher-Poh Ming , Sunden Lindsey , Hernandez John , Jiang Allen , Liu Xin , Winbush Ari , Nelson Benjamin W. , Allen Nicholas B. 
Published: August 2024

AbstractElectrodermal activity (EDA) is a measure of sympathetic arousal that has been linked to depression in laboratory experiments. However, the inability to measure EDA passively over time and in the real-world has limited conclusions that can be drawn about EDA as an indicator of mental health status outside of controlled settings. Recent smartwatches have begun to incorporate wrist-worn continuous EDA sensors that enable longitudinal measurement of sympathetic arousal in every-day life. This work (N=237, 4-week observation period) examines the association between passively collected, diurnal variations in EDA and symptoms of depression, anxiety and perceived stress in a large community sample. Subjects who exhibited elevated depressive and anxiety symptoms had higher tonic EDA, skin temperature, and heart rate, despite not engaging in greater physical activity, compared to those that were not depressed or anxious. In contrast, subjects who exhibited elevated stress only exhibited higher skin temperature. Most strikingly, differences in EDA between those with high versus low symptoms were most prominent during the early morning. We did not observe amplitude or phase differences in the diurnal patterns. Our work suggests that electrodermal sensors may be practical and useful in measuring the physiological correlates of mental health symptoms in free-living contexts and that recent consumer smartwatches might be a tool for doing so.
Lineage B Genotype III of Dengue Virus Serotype 3 (DENV-3III_B) is responsible for Dengue Outbreak in Dire Dawa City, Ethiopia, 2023
Authors: Aseffa Abebe , Alemayehu Dawit Hailu , Abrahim Saro Abdella , Wolde Tsigereda Kifle , Bulti Gutema , Bedasso Alemnesh Hailemariam , Tsega Danile Hailemariam , Abosex Ebise , Awule Eyilachew Zenebe , Rojas-Gallardo Diana M , Korkiso Asefa Konde , Melaku Kalkidan , Joseph Raffael , Ayele Abaysew , Tsegaye Mesfin Mengesha , Piantadosi Anne Mengesha , Tollera Getachew Mengesha , Abdissa Alemseged , Hailu Mesay , Mihret Adane , Mulu Andargachew , Gelanew Tesfaye 
Published: August 2024

Background: The eastern parts of Ethiopia, including Dire Dawa City, have faced annual dengue fever (DF) outbreaks since 2013, resulting in substantial healthcare and economic consequences. However, there remains a lack of comprehensive evidence regarding the specific dengue virus (DENV) serotypes and genotypes associated with those outbreaks. Methodology: On December 14, 2023, the National Arbovirus Laboratory at the Ethiopian Public Health Institute received seventy serum samples from 70 patients suspected of DF during the outbreak in Dire Dawa City. Samples positive for DENV with adequate volume and CT values underwent sequencing of the CprM region of the DENV genome. The obtained sequences were typed using the Dengue Virus Typing Tool and underwent phylogenetic analysis to evaluate sequence diversity and relationships to DENV strains from other global DENV-endemic regions. Results: Overall, 32 (45.7%) of the patients displayed one or more early warning signs indicative of severe dengue. Among the 13 patients who were hospitalized for 2 to 10 days, all except two had at least one symptom indicative of severe dengue. Out of 67 samples with adequate volume, 44 (65.6%) were positive for DENV RNA using RT-PCR, and 21 successfully underwent CprM sequencing. All of them belonged to DENV-3, genotype III, major lineage B (DENV-3III_B), but represented two different minor lineages (DENV-3III_B.2 and DENV-3III_B.3). Phylogenetic analysis demonstrated that both lineages were closely related to sequences from the Afar region of Ethiopia collected in 2023, indicating that the outbreaks were related and were due to multiple co-circulating lineages. Conclusions: DENV-3III_B was identified as the cause of the 2023 DF outbreak in Dire Dawa City. The identification of two co-circulating lineages in this outbreak underscores the complex nature of DENV transmission in the African continent and highlights the need for increased viral genomic surveillance.
PROgressive struCturEd Simulation-based Surgical training program (PROCESS) - Open Vascular Surgery: Study protocol for triple-arm, randomized, single-blinded educational clinical trial
Authors: Velandia-Sánchez Alejandro , Polanía-Sandoval Camilo A. , Álvarez-Martínez José V. , Uribe-Ramírez Santiago , Tello-Pirateque Juliana , Pérez-Rivera Carlos J. , Ávila-Madrigal Juan P. , Cruz Reyes Danna L. , Cabrera-Rivera Paulo A. , Pérez-Cualtan Camilo E. , Barrera Edgar C. , Bustos-Martínez Yury F. , Galán Sebastián Gómez , Briceño Juan C. , Reijnen Michel M.P.J. , Camacho-Mackenzie Jaime , Mendivil Carlos O. , Barrera-Carvajal Juan G. 
Published: August 2024

AbstractIntroductionVascular surgery has been directed towards endovascular approaches; however, not all patients qualify for these procedures. Open vascular surgery remains crucial, demanding a steep learning curve. Exposure to these procedures has declined, resulting in a need for more standardization in acquiring open vascular surgery skills and potentially contributing to poorer outcomes. Simulation offers a solution, yet the evidence for structured programs in open vascular surgery is limited. This study aims to compare the efficacy of technical skill acquisition between a structured, progressive simulation-based training program and traditional experience-based training in open vascular surgery.MethodsA randomized, single-blinded, triple-arm educational clinical trial will be conducted. A control and intervention phases of three groups with different exposure levels to the simulation program are proposed. Group 1: open abdominal aortic repair, Group 2: vascular anastomosis and open abdominal aortic repair, and Group 3: specific surgical skills, vascular anastomosis, and open abdominal aortic repair. The 3D-printed models from AngioCT will be used for the open abdominal aortic repair simulation. Surgical residents of general, vascular, or cardiothoracic surgery programs will be included. Sample size calculation resulted in 45 participants, 15 per group. Single blinding will involve external evaluators. Randomization will occur as a stratified randomization.DiscussionWe expect that the structured and progressive simulation-based training program would enhance technical surgical skills. Based on the progression through different modules within the program, we aim to evaluate differences in the acquisition of technical surgical skills. We hypothesize that 3D-printed patient-specific models can enhance participants’ vascular surgery training and provide optimal simulated scenarios while prioritizing patient safety. We hope this initiative will impact the formation of future vascular surgeons, shape future training programs, and ensure comprehensive preparation for open vascular surgery.Trial registrationThis study protocol was registered inclinicaltrials.govwith the NCT-ID:NCT06452901.
Future Pandemics: AI-Designed Assays for Detecting Mpox, General and Clade 1b Specific
Authors: Mendoza-Maldonado Lucero , MacSharry John , Garssen Johan , Kraneveld Aletta D. , Tonda Alberto , Lopez-Rincon Alejandro 
Published: August 2024

AbstractThe global outbreak of human monkeypox (mpox) in 2022, declared a Public Health Emergency of International Concern by the WHO, has underscored the urgent need for effective diagnostic tools. In August 2024 WHO again declared mpox as a Public Health Emergency of International Concern. This study presents an innovative approach using artificial intelligence (AI) to design primers for the rapid and accurate detection of mpox. Leveraging evolutionary algorithms, we developed primer sets with high specificity and sensitivity, validated insilicofor mpox main lineage and the Clade 1b. These primers are crucial for distinguishing mpox from other viruses, enabling precise diagnosis and timely public health responses. Our findings highlight the potential of AI-driven methodologies to enhance surveillance, vaccination strategies, and outbreak management, particularly for emerging zoonotic diseases. The emergence of new mpox clades, such as Clade1b, with higher mortality rates, further emphasizes the necessity for continuous monitoring and preparedness for future pandemics. This study advocates for the integration of AI in molecular diagnostics to improve public health outcomes.
Gut microbiota–mediated lipid accumulation as a driver of evolutionary adaptation to blue light toxicity inDrosophila
Authors: Takada Yuta , Ichinose Toshiharu , Fuse Naoyuki , Saito Kokoro , Ikeda-Ohtsubo Wakako , Tanimoto Hiromu , Hori Masatoshi 
Published: August 2024

AbstractSince the origin of life, the sunlight-driven environment on Earth has shaped the evolution of organisms. Blue light (BL), a major component of sunlight, is crucial to the biological processes of many insect species, yet excessive exposure can be highly toxic. BL toxicity can affect fitness and act as a selective pressure in insects. However, tolerance and evolutionary adaptation to this stressor remain largely unexplored. Here, we usedDrosophila melanogasteras a model to select for BL tolerance over 60 generations. The selected line (SL) exhibited an obese phenotype accompanied by midgut elongation. Surprisingly, BL tolerance in the SL was dependent on gut microbiota–mediated lipid accumulation. In addition, we found that parental BL exposure plays a vital role in midgut elongation and increased abundance of beneficial bacteria. Whole-genome and transcriptome analyses consistently highlighted interactions between the microbiota and lipogenesis genes. Remarkably, manipulation of lipogenesis via these genes conferred BL tolerance even in the absence of selection. We hypothesise that the acquisition of BL tolerance occurs through ‘adaptive obesity’ by maximising the benefits of the gut microbiota. Our findings demonstrate that evolutionary adaptations to environmental stress can be driven by optimised interactions with the microbiota.
Carbonic anhydrase inhibitors prevent presymptomatic capillary flow disturbances in a model of cerebral amyloidosis
Authors: Gutiérrez-Jiménez Eugenio , Rasmussen Peter Mondrup , Mikkelsen Irene Klærke , Kura Sreekanth , Fruekilde Signe K. , Hansen Brian , Bordoni Luca , Carlsen Jasper , Palmfeldt Johan , Boas David A. , Sakadžić Sava , Vinogradov Sergei , Khatib Mirna El , Ramos-Cejudo Jaime , Wied Boris , Leduc-Galindo Desiree , Canepa Elisa , Mar Adam C. , Gamallo-Lana Begona , Fossati Silvia , Østergaard Leif 
Published: August 2024

Structured abstractINTRODUCTIONDisturbances in microvascular flow dynamics are hypothesized to precede the symptomatic phase of Alzheimer’s disease (AD). However, evidence in presymptomatic AD remains elusive, underscoring the need for therapies targeting these early vascular changes.METHODSWe employed a multimodal approach, combining in vivo optical imaging, molecular techniques, and ex vivo MRI, to investigate early capillary dysfunction in Tg-SwDI mice without memory impairment. We also assessed the efficacy of carbonic anhydrase inhibitors (CAIs) in preventing capillary flow disturbances.RESULTSOur study revealed capillary flow disturbances associated with alterations in capillary morphology, adhesion molecule expression, and Amyloid-β (Aβ) load in 9–10-month-old Tg-SwDI mice without memory impairment. CAI treatment ameliorated these capillary flow disturbances, enhanced oxygen availability, and reduced Aβ load.DISCUSSIONThese findings underscore the importance of capillary flow disturbances as early biomarkers in presymptomatic AD and highlight the potential of CAIs for preserving vascular integrity in the early stages of AD.