bluuebunny/crossref_metadata_2025_split huggingface dataset:
Reduced monocyte proportions and responsiveness in convalescent COVID-19 patients
Authors: Ravkov Eugene V. , Williams Elizabeth S. C. P. , Elgort Marc , Barker Adam P. , Planelles Vicente , Spivak Adam M. , Delgado Julio C. , Lin Leo , Hanley Timothy M. 
Published: January 2024

IntroductionThe clinical manifestations of acute severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) infection and coronavirus disease 2019 (COVID-19) suggest a dysregulation of the host immune response that leads to inflammation, thrombosis, and organ dysfunction. It is less clear whether these dysregulated processes persist during the convalescent phase of disease or during long COVID. We sought to examine the effects of SARS-CoV-2 infection on the proportions of classical, intermediate, and nonclassical monocytes, their activation status, and their functional properties in convalescent COVID-19 patients.MethodsPeripheral blood mononuclear cells (PBMCs) from convalescent COVID-19 patients and uninfected controls were analyzed by multiparameter flow cytometry to determine relative percentages of total monocytes and monocyte subsets. The expression of activation markers and proinflammatory cytokines in response to LPS treatment were measured by flow cytometry and ELISA, respectively.ResultsWe found that the percentage of total monocytes was decreased in convalescent COVID-19 patients compared to uninfected controls. This was due to decreased intermediate and non-classical monocytes. Classical monocytes from convalescent COVID-19 patients demonstrated a decrease in activation markers, such as CD56, in response to stimulation with bacterial lipopolysaccharide (LPS). In addition, classical monocytes from convalescent COVID-19 patients showed decreased expression of CD142 (tissue factor), which can initiate the extrinsic coagulation cascade, in response to LPS stimulation. Finally, we found that monocytes from convalescent COVID-19 patients produced less TNF-α and IL-6 in response to LPS stimulation, than those from uninfected controls.ConclusionSARS-CoV-2 infection exhibits a clear effect on the relative proportions of monocyte subsets, the activation status of classical monocytes, and proinflammatory cytokine production that persists during the convalescent phase of disease.
Phytoplankton physiology and functional traits under artificial upwelling with varying Si:N
Authors: Ortiz Joaquin , Arístegui Javier , Goldenberg Silvan Urs , Fernández-Méndez Mar , Taucher Jan , Archer Stephen D. , Baumann Moritz , Riebesell Ulf 
Published: January 2024

IntroductionArtificial upwelling has been discussed as a nature-based solution to fertilize currently unproductive areas of the ocean to enhance food web productivity and atmospheric CO2 sequestration. The efficacy of this approach may be closely tied to the nutrient stoichiometry of the upwelled water, as Si-rich upwelling should benefit the growth of diatoms, who are key players for primary production, carbon export and food web efficiency.MethodsWith a mesocosm experiment in subtropical waters, we assessed the physiological and functional responses of an oligotrophic phytoplankton community to artificial upwelling under varying Si:N ratios (0.07-1.33).ResultsDeep water fertilization led to strongly enhanced primary productivity rates and net autotrophy across Si scenarios. At the community level, Si-rich upwelling50 temporarily increased primary production and consistently enhanced diatom growth, producing up to 10-fold higher abundances compared to Si-deficient upwelling. At the organism level, contrasting effects were observed. On the one hand, silicification and size of diatom cells remained unaffected by Si:N, which is surprising given the direct dependency of these traits on Si. On the other hand, diatom Chlorophyll a density and carbon density were strongly reduced and particulate matter C:N was elevated under Si-rich upwelling.DiscussionThis suggests a reduced nutritional value for higher trophic levels under high Si:N ratios. Despite these strong qualitative changes under high Si, diatom cells appeared healthy and showed high photosynthetic efficiency. Our findings reveal great physiological plasticity and adaptability in phytoplankton under artificial upwelling, with Si-dependent trade-offs between primary producer quantity and quality.
Optimization of virtual design and machining time of the mold master ceramic jewelry products with Indonesian batik motifs
Authors: Bawono B. , Yuniarto T. , Sanusi C. E. P. , Felasari S. , Widyanarka O. K. W. , Anggoro P. W. 
Published: January 2024

Batik has been the cultural heritage of the Indonesian nation since the 17th century. On 2 October 2009, the United Nations Educational, Scientific, and Cultural Organization (UNESCO) designated Batik as part of Indonesia’s traditional clothing as Indonesia’s cultural heritage. This research proves that Batik is an attractive and artistic ornament of ceramic jewelry products. This ornament can increase the selling value and storytelling of the product. Naruna is a local handmade porcelain ceramic factory in Indonesia with excellence in unique colored tableware and consistently trying to develop new designs in ceramic jewelry. Modern technology in computer-based virtual design and machining on dining plate tableware with the Batik Kawung motif is also applied in this paper to obtain a master design for ceramic jewelry prints. The design stage begins with selecting ornament patterns with creative methods to form a 3D CAD model of ceramic jewelry using artistic Art CAM. The optimization of computer-aided manufacturing-based to obtain machining strategy using the software. The software is Autodesk Power Mill, whose NC Code can be directly manufactured on a CNC router machine. This combination can speed up the production process time to meet the production capacity at Naruna and get detailed, accurate, precise, and consistent textures and ornaments, thereby meeting NCS consumer demand for ceramic jewelry products. The simulation results of the two master print patterns are 141 h, 56 min, and 46 s; the machining time is estimated at 153 h. This research saves about 11 h of time processing. The resulting NC Code can be appropriately processed on a CNC router machine with a Mach 3 post-processor.
The clinical course of hospitalized COVID-19 patients and aggravation risk prediction models: a retrospective, multi-center Korean cohort study
Authors: Moon Min Kyong , Ham Hyeonjung , Song Soo Min , Lee Chanhee , Goo Taewan , Oh Bumjo , Lee Seungyeoun , Kim Shin-Woo , Park Taesung 
Published: January 2024

BackgroundUnderstanding the clinical course and pivotal time points of COVID-19 aggravation is critical for enhancing patient monitoring. This retrospective, multi-center cohort study aims to identify these significant time points and associate them with potential risk factors, leveraging data from a sizable cohort with mild-to-moderate symptoms upon admission.MethodsThis study included data from 1,696 COVID-19 patients with mild-to-moderate clinical severity upon admission across multiple hospitals in Daegu-Kyungpook Province (Daegu dataset) between February 18 and early March 2020 and 321 COVID-19 patients at Seoul Boramae Hospital (Boramae dataset) collected from February to July 2020. The approach involved: (1) identifying the optimal time point for aggravation using survival analyses with maximally selected rank statistics; (2) investigating the relationship between comorbidities and time to aggravation; and (3) developing prediction models through machine learning techniques. The models were validated internally among patients from the Daegu dataset and externally among patients from the Boramae dataset.ResultsThe Daegu dataset showed a mean age of 51.0 ± 19.6 years, with 8 days for aggravation and day 5 being identified as the pivotal point for survival. Contrary to previous findings, specific comorbidities had no notable impact on aggravation patterns. Prediction models utilizing factors including age and chest X-ray infiltration demonstrated promising performance, with the top model achieving an AUC of 0.827 in external validation for 5 days aggravation prediction.ConclusionOur study highlights the crucial significance of the initial 5 days period post-admission in managing COVID-19 patients. The identification of this pivotal time frame, combined with our robust predictive models, provides valuable insights for early intervention strategies. This research underscores the potential of proactive monitoring and timely interventions in enhancing patient outcomes, particularly for those at risk of rapid aggravation. Our findings offer a meaningful contribution to understanding the COVID-19 clinical course and supporting healthcare providers in optimizing patient care and resource allocation.
Plasma metabolites distinguish dementia with Lewy bodies from Alzheimer’s disease: a cross-sectional metabolomic analysis
Authors: Pan Xiaobei , Donaghy Paul C. , Roberts Gemma , Chouliaras Leonidas , O’Brien John T. , Thomas Alan J. , Heslegrave Amanda J. , Zetterberg Henrik , McGuinness Bernadette , Passmore Anthony P. , Green Brian D. , Kane Joseph P. M. 
Published: January 2024

BackgroundIn multifactorial diseases, alterations in the concentration of metabolites can identify novel pathological mechanisms at the intersection between genetic and environmental influences. This study aimed to profile the plasma metabolome of patients with dementia with Lewy bodies (DLB) and Alzheimer’s disease (AD), two neurodegenerative disorders for which our understanding of the pathophysiology is incomplete. In the clinical setting, DLB is often mistaken for AD, highlighting a need for accurate diagnostic biomarkers. We therefore also aimed to determine the overlapping and differentiating metabolite patterns associated with each and establish whether identification of these patterns could be leveraged as biomarkers to support clinical diagnosis.MethodsA panel of 630 metabolites (Biocrates MxP Quant 500) and a further 232 metabolism indicators (biologically informative sums and ratios calculated from measured metabolites, each indicative for a specific pathway or synthesis; MetaboINDICATOR) were analyzed in plasma from patients with probable DLB (n = 15; age 77.6 ± 8.2 years), probable AD (n = 15; 76.1 ± 6.4 years), and age-matched cognitively healthy controls (HC; n = 15; 75.2 ± 6.9 years). Metabolites were quantified using a reversed-phase ultra-performance liquid chromatography column and triple-quadrupole mass spectrometer in multiple reaction monitoring (MRM) mode, or by using flow injection analysis in MRM mode. Data underwent multivariate (PCA analysis), univariate and receiving operator characteristic (ROC) analysis. Metabolite data were also correlated (Spearman r) with the collected clinical neuroimaging and protein biomarker data.ResultsThe PCA plot separated DLB, AD and HC groups (R2 = 0.518, Q2 = 0.348). Significant alterations in 17 detected metabolite parameters were identified (q ≤ 0.05), including neurotransmitters, amino acids and glycerophospholipids. Glutamine (Glu; q = 0.045) concentrations and indicators of sphingomyelin hydroxylation (q = 0.039) distinguished AD and DLB, and these significantly correlated with semi-quantitative measurement of cardiac sympathetic denervation. The most promising biomarker differentiating AD from DLB was Glu:lysophosphatidylcholine (lysoPC a 24:0) ratio (AUC = 0.92; 95%CI 0.809–0.996; sensitivity = 0.90; specificity = 0.90).DiscussionSeveral plasma metabolomic aberrations are shared by both DLB and AD, but a rise in plasma glutamine was specific to DLB. When measured against plasma lysoPC a C24:0, glutamine could differentiate DLB from AD, and the reproducibility of this biomarker should be investigated in larger cohorts.
Machine learning hypothesis-generation for patient stratification and target discovery in rare disease: our experience with Open Science in ALS
Authors: Geraci Joseph , Bhargava Ravi , Qorri Bessi , Leonchyk Paul , Cook Douglas , Cook Moses , Sie Fanny , Pani Luca 
Published: January 2024

IntroductionAdvances in machine learning (ML) methodologies, combined with multidisciplinary collaborations across biological and physical sciences, has the potential to propel drug discovery and development. Open Science fosters this collaboration by releasing datasets and methods into the public space; however, further education and widespread acceptance and adoption of Open Science approaches are necessary to tackle the plethora of known disease states.MotivationIn addition to providing much needed insights into potential therapeutic protein targets, we also aim to demonstrate that small patient datasets have the potential to provide insights that usually require many samples (&gt;5,000). There are many such datasets available and novel advancements in ML can provide valuable insights from these patient datasets.Problem statementUsing a public dataset made available by patient advocacy group AnswerALS and a multidisciplinary Open Science approach with a systems biology augmented ML technology, we aim to validate previously reported drug targets in ALS and provide novel insights about ALS subpopulations and potential drug targets using a unique combination of ML methods and graph theory.MethodologyWe use NetraAI to generate hypotheses about specific patient subpopulations, which were then refined and validated through a combination of ML techniques, systems biology methods, and expert input.ResultsWe extracted 8 target classes, each comprising of several genes that shed light into ALS pathophysiology and represent new avenues for treatment. These target classes are broadly categorized as inflammation, epigenetic, heat shock, neuromuscular junction, autophagy, apoptosis, axonal transport, and excitotoxicity. These findings are not mutually exclusive, and instead represent a systematic view of ALS pathophysiology. Based on these findings, we suggest that simultaneous targeting of ALS has the potential to mitigate ALS progression, with the plausibility of maintaining and sustaining an improved quality of life (QoL) for ALS patients. Even further, we identified subpopulations based on disease onset.ConclusionIn the spirit of Open Science, this work aims to bridge the knowledge gap in ALS pathophysiology to aid in diagnostic, prognostic, and therapeutic strategies and pave the way for the development of personalized treatments tailored to the individual’s needs.
Effect of a processing delay between direct and delayed sound in simulated open fit hearing aids on speech intelligibility in noise
Authors: Roth Sebastian , Müller Franz-Ullrich , Angermeier Julian , Hemmert Werner , Zirn Stefan 
Published: January 2024

IntroductionSubjects with mild to moderate hearing loss today often receive hearing aids (HA) with open-fitting (OF). In OF, direct sound reaches the eardrums with minimal damping. Due to the required processing delay in digital HA, the amplified HA sound follows some milliseconds later. This process occurs in both ears symmetrically in bilateral HA provision and is likely to have no or minor detrimental effect on binaural hearing. However, the delayed and amplified sound are only present in one ear in cases of unilateral hearing loss provided with one HA. This processing alters interaural timing differences in the resulting ear signals.MethodsIn the present study, an experiment with normal-hearing subjects to investigate speech intelligibility in noise with direct and delayed sound was performed to mimic unilateral and bilateral HA provision with OF.ResultsThe outcomes reveal that these delays affect speech reception thresholds (SRT) in the unilateral OF simulation when presenting speech and noise from different spatial directions. A significant decrease in the median SRT from –18.1 to –14.7 dB SNR is observed when typical HA processing delays are applied. On the other hand, SRT was independent of the delay between direct and delayed sound in the bilateral OF simulation.DiscussionThe significant effect emphasizes the development of rapid processing algorithms for unilateral HA provision.
Development and validation of a nomogram to predict cervical lymph node metastasis in head and neck squamous cell carcinoma
Authors: Chen Xiaohan , Zhang Lu , Lu Haijun , Tan Ye , Li Bo 
Published: January 2024

BackgroundHead and neck cancers are a heterogeneous, aggressive, and genetically complex collection of malignancies of the oral cavity, nasopharynx, oropharynx, hypopharynx, larynx, paranasal sinuses and salivary glands, which are difficult to treat. Regional lymph nodes metastasis is a significant poor prognosis factor for head and neck squamous cell carcinoma. Metastasis to the regional lymph nodes reduces the 5-year survival rate by 50% compared with that of patients with early-stage disease. Accurate evaluation of cervical lymph node is a vital component in the overall treatment plan for patients with squamous cell carcinoma of the head and neck. However, current models are struggle to accurately to predict cervical lymph node metastasis. Here, we analyzed the clinical, imaging, and pathological data of 272 patients with HNSCC confirmed by postoperative pathology and sought to develop and validate a nomogram for prediction of lymph node metastasis in patients with head and neck squamous cell carcinoma.MethodsWe retrospectively analyzed the clinical, imaging, and pathological data of 272 patients with head and neck squamous cell carcinoma (HNSCC) confirmed by postoperative pathology at the Affiliated Hospital of Qingdao University from June 2017 to June 2021. Patients were randomly divided into the training and validation cohorts in a 3:1 ratio, and after screening risk factors by logistic regression, nomogram was developed for predicting lymph nodes metastasis, then the prediction model was verified by C-index, area under curve (AUC), and calibration curve.ResultsOf the 272 patients, seven variables were screened to establish the predictive model, including the differentiation degree of the tumor [95% confidence interval(CI):1.224~6.735, P=0.015], long-to-short axis ratio of the lymph nodes (95%CI: 0.019~0.217, P&lt;0.001), uneven/circular enhancement (95%CI: 1.476~16.715, P=0.010), aggregation of lymph nodes (95%CI:1.373~10.849, P=0.010), inhomogeneous echo (95%CI: 1.337~23.389, P=0.018), unclear/absent medulla of lymph nodes (95%CI: 2.514~43.989, P=0.001), and rich blood flow (95%CI: 1.952~85.632, P=0.008). The C-index was 0.910, areas under the curve of training cohort and verification cohort were 0.953 and 0.938 respectively, indicating the discriminative ability of this nomogram. The calibration curve showed a favorable compliance between the prediction of the model and actual observations. The clinical decision curve showed this model is clinically useful and had better discriminative ability between 0.25 and 0.9 for the probability of cervical LNs metastasis.ConclusionsWe established a good prediction model for cervical lymph node metastasis in head and neck squamous cell carcinoma patients which can provide reference value and auxiliary diagnosis for clinicians in making neck management decisions of HNSCC patients.
Functional information guided adaptive radiation therapy
Authors: Herndon R. Craig 
Published: January 2024

IntroductionFunctional informaton is introduced as the mechanism to adapt cancer therapies uniquely to individual patients based on changes defined by qualified tumor biomarkers.MethodsTo demonstrate the methodology, a tumor volume biomarker model, characterized by a tumor volume reduction rate coefficient, is used to adapt a tumor cell survival bioresponse radiotherapy model in terms of therapeutic radiation dose. Tumor volume, acquired from imaging data, serves as a surrogate measurement for tumor cell death, but the biomarker model derived from this data cannot be used to calculate the radiation dose absorbed by the target tumor. However, functional information does provide a mathematical connection between the tumor volume biomarker model and the tumor cell survival bioresponse model by quantifying both data sets in the units of information, thus creating an analytic conduit from bioresponse to biomarker.ResultsThe information guided process for individualized dose adaptations using information values acquired from the tumor cell survival bioresponse model and the tumor volume biomarker model are presented in detailed form by flowchart and tabular data. Clinical data are used to generate a presentation that assists investigator application of the information guided methodology to adaptive cancer therapy research.ConclusionsInformation guided adaptation of bioresponse using surrogate data is extensible across multiple research fields because functional information mathematically connects disparate bioresponse and biomarker data sets. Thus, functional information offers adaptive cancer therapy by mathematically connecting immunotherapy, chemotherapy, and radiotherapy cancer treatment processes to implement individualized treatment plans.
A meta-analysis of the clinicopathological significance of the lncRNA MALAT1 in human gastric cancer
Authors: Bai Shaoxiong , Guo Jiansheng , Zhang Haofan 
Published: January 2024

BackgroundDysregulation of the long non-coding RNA metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) has been linked to some oncogenic pathways that induce cancer initiation and progression. This meta-analysis was conducted to specifically summarize the most recent research on MALAT1 function in human gastric cancer (GC).MethodsThe eligible studies were first identified by searching HowNet, Web of Science, PubMed, The Cochrane Library, Embase, and Nature databases for studies published as of April 1, 2023. The meta-analysis included 14 studies assessing MALAT1 expression and presenting clinical parameters and survival outcomes.ResultsThe results illustrated that high MALAT1 expression is predictive of lymph node metastasis (pooled odds ratio [OR] = 2.99, 95% confidence interval [CI] = 1.97–4.54, P &lt; 0.001) and distant metastasis in GC (OR = 3.11, 95% CI = 1.68–5.75, P &lt; 0.001). In addition, MALAT1 was associated with GC tumor invasion (T3/T4 vs. T1/T2: OR = 2.90, 95% CI = 1.90- 4.41, P &lt;0.001) and TNM stage (III/IV vs I/II: OR = 2.93, 95% CI: 1.80-4.77, P &lt;0.001). Additionally, higher MALAT-1 expression predicted poorer overall survival in patients with GC (hazard ratio = 1.64, 95% CI = 1.20–2.09, P &lt; 0.001).ConclusionsThe current findings suggest that the high MALAT1 expression is an adverse biomarker for prognostic outcomes, lymph node metastasis, TNM stage, and distant metastasis in GC and MALAT1 could be a prognostic biomarker for GC.