bluuebunny/crossref_metadata_2025_split huggingface dataset:
Modeling Normal and Imbalanced Neural Avalanches: A Computational Approach to Understanding Criticality in the Brain and Its Potential Role in Schizophrenia
Authors: Mongomery Richard Murdoch 
Published: April 2024

AbstractNeural avalanches, characterized by bursts of activity followed by periods of quiescence, have been observed in the brain and are thought to reflect the critical dynamics necessary for optimal information processing. Deviations from normal avalanche behavior have been hypothesized to underlie various neurological disorders, particularly schizophrenia. Schizophrenia is a complex psychiatric disorder associated with altered perception, cognition, and behavior, and recent theories suggest that disruptions in the brain’s critical dynamics may contribute to its pathophysiology. In this study, we present a computational model to investigate the properties of normal and imbalanced neural avalanches, with a focus on understanding the potential role of criticality in schizophrenia. We generate avalanche sizes using the Pareto distribution with a power law exponent of -3/2, which is consistent with experimental observations. The model incorporates increasing avalanche sizes over time to simulate the growth of neural activity. We introduce imbalance by adding lateness or earliness to the avalanche sizes, mimicking the potential disruptions in critical dynamics that may occur in schizophrenia. The mean and standard deviation of avalanche sizes are calculated to characterize the normal and imbalanced behavior. The results are visualized using line plots, with shaded areas representing the standard deviation range. Our model provides a framework for understanding the differences between normal and imbalanced neural avalanches, offering insights into the potential mechanisms underlying the altered critical dynamics in schizophrenia. By exploring the relationship between neural avalanches and schizophrenia, this study contributes to the ongoing efforts to elucidate the neurobiological basis of this disorder and may inform future research on potential diagnostic markers and therapeutic interventions targeting the brain’s critical dynamics.
A scalable CRISPR-Cas9 gene editing system facilitates CRISPR screens in the malaria parasitePlasmodium berghei
Authors: Jonsdottir Thorey K. , Paoletta Martina S. , Ishizaki Takahiro , Hernandez Sophia , Ivanova Maria , Herrera Curbelo Alicia , Saiki Paulina A. , Selinger Martin , Das Debojyoti , Henriksson Johan , Bushell Ellen S.C. 
Published: April 2024

AbstractManyPlasmodiumgenes remain uncharacterised due to low genetic tractability. Previous large scale knockout screens have only been able to target about half of the genome in the more genetically tractable rodent malaria parasitePlasmodium berghei. To overcome this limitation, we have developed a scalable CRISPR system called PbHiT, which uses a single cloning step to generate targeting vectors with 100 bp homology arms physically linked to a guide RNA (gRNA) that effectively integrate into the target locus. We show that PbHiT coupled with gRNA sequencing robustly recapitulates known knockout mutant phenotypes in pooled transfections. Furthermore, we provide vector designs and sequences to target the entireP. bergheigenome and scale-up vector production using a pooled ligation approach. This work presents for the first time a tool for high-throughput CRISPR screens inPlasmodiumfor studying the parasite’s biology at scale.
PREVALENCE OF HUMAN AND ANIMAL AFRICAN TRYPANOSOMIASIS IN NIGERIA: A SCOPING REVIEW
Authors: Odebunmi Elizabeth O. , Ibeachu Chukwuemeka , Chukwudi Chinwe U. 
Published: April 2024

ABSTRACTBackgroundAfrican trypanosomiasis continues to pose a substantial threat to both human and animal health in sub-Saharan Africa. This study examined the prevalence of African trypanosomiasis within human and animal populations in Nigeria, the trypanosome species involved, the spread of animal reservoirs, and the variability in diagnostic methodologies employed.Methodology/principal findingsA scoping review was performed, following the methodological framework outlined in PRISMA-ScR checklist. Eligible studies were explored for disease prevalence, causative parasites, reservoir hosts, and diagnostic techniques used. A total of 16 eligible studies published between 1993 and 2021 were retrieved. 13 studies reported the prevalence of African trypanosomiasis in animals, and 3 for humans. Varying prevalence rates were recorded depending on the host population and diagnostic methods employed. The overall prevalence of Animal African Trypanosomiasis (AAT) and Human African Trypanosomiasis (HAT) was 27.3% (4,404/16,117) and 3.6% (72/1,974) respectively. The highest incidence of HAT was detected using the Card Agglutination Test for Trypanosomiasis (CATT), whereas that of AAT was observed using PCR, followed by microscopy which was the most extensively employed technique in this study. The various diagnostic methods used in various studies showed a variety of sensitivities and specificities, affecting the accuracy of disease detection. Remarkably, domestic animals like cattle, pigs, and dogs were identified as potential reservoirs for the human-infective parasites (T. b. gambiense)in Nigeria.ConclusionThis study highlights the high prevalence rate and complex epidemiology of African trypanosomiasis in humans and animals in Nigeria. These findings underscore the importance of comprehensive measures involving both veterinary and human health sectors to combat African trypanosomiasis effectively in Nigeria. Only few studies have investigated the prevalence of HAT in Nigeria. Hence, adequately coordinated epidemiological surveys are imperative to ascertain the true epidemiological status of HAT in Nigeria and inform targeted intervention policies to achieve WHO’s NTD elimination targets in 2030.
GABA/Glutamate neuron differentiation imbalance and increased AKT/mTOR signalling in CNTNAP2-/-cerebral organoids
Authors: Chalkiadaki Kleanthi , Statoulla Elpida , Zafeiri Maria , Voudouri Georgia , Amvrosiadis Theoklitos , Typou Alexandra , Theodoridou Niki , Moschovas Dimitrios , Avgeropoulos Apostolos , Samiotaki Martina , Mason John O. , Gkogkas Christos G. 
Published: April 2024

AbstractWe developed a human cerebral organoid model derived from induced pluripotent stem cells (iPSCs) with targeted genome editing to abolish protein expression of the Contactin Associated Protein-like 2 (CNTNAP2)autism spectrum disorder (ASD) risk gene, mimicking loss-of-function mutations seen in patients. CNTNAP2-/-cerebral organoids displayed accelerated cell cycle, ventricular zone disorganisation and increased cortical folding. Proteomic analysis revealed disruptions in Glutamatergic/GABAergic synaptic pathways and neurodevelopment, highlighting increased protein expression of corticogenesis and neurodevelopment-related genes such as Forkhead box protein G1 (FOXG1) and Paired box 6 (PAX6). Transcriptomic analysis revealed differentially expressed genes (DEG) belonging to inhibitory neuron-related gene networks. Interestingly, there was a weak correlation between the transcriptomic and proteomic data, suggesting nuanced translational control mechanisms. Along these lines we found upregulated Protein Kinase B (Akt)/mechanistic target of rapamycin (mTOR) signalling in CNTNAP2-/-organoids. Spatial transcriptomics analysis of CNTNAP2-/-ventricular-like zones demonstrated pervasive changes in gene expression, particularly in PAX6-cells, implicating upregulation of cell cycle regulation pathways, synaptic and Glutamatergic/GABAergic pathways. We noted a significant overlap of all D30 cerebral organoids ‘omics datasets with an idiopathic ASD (macrocephaly) iPSC-derived telencephalic organoids DEG dataset, where FOXG1 was upregulated. Moreover, we detected increased Glutamate decarboxylase 1 (GAD1) and decreased T-Box Brain Transcription Factor 1 (TBR1) expressing cells, suggesting altered GABAergic/Glutamatergic neuron development. These findings potentially highlight a shared mechanism in the early cortical development of various forms of ASD, further elucidate the role of CNTNAP2 in ASD pathophysiology and cortical development and pave the way for targeted therapies using cerebral organoids as preclinical models.
Late diagnosis of Marfan syndrome is associated with unplanned aortic surgery and cardiovascular death
Authors: Claus Jason , Schoof Lauritz , Mir Thomas S. , Kammal Anna Lena , Schön Gerhard , Kutsche Kerstin , Behrendt Christian-Alexander , Kallenbach Klaus , Kölbel Tilo , Kubisch Christian , Demal Till Joscha , Petersen Johannes , Brickwedel Jens , Hübler Michael , Detter Christian , Kirchhof Paulus , Debus Eike Sebastian , Rybczynski Meike , von Kodolitsch Yskert 
Published: April 2024

AbstractBackgroundMarfan syndrome (MFS) guidelines recommend optimal pharmacological therapy (OPT) and replacement of the ascending aorta (RAA) at 5.0cm diameters to prevent acute type A aortic dissection (ATAAD) and death. The effect of early MFS diagnosis and initiation of therapy on outcomes is not known.ObjectiveTo evaluate the effect of age at MFS diagnosis and therapy initiation on delayed RAA and death.MethodsThis retrospective observational cohort study with long-term follow-up included consecutive patients with MFS, pathogenicFBN1variant, and regular visits to a European Reference Network Center. We considered MFS diagnosis at age ≥21 years late, and OPT initiation at age <21 years early. Outcomes were delayed RAA with aneurysm diameter >5.0cm or ATAAD, and death from all causes. We used landmark design starting at age 21 years to determine associations with outcomes.ResultsThe study group consisted of 288 patients (45.1% male), including 169 patients with late MFS diagnosis (58.7%) and 63 with early OPT (21.9%). During mean follow-up of 25±14.7 years, 78 patients had delayed RAA, with 42 operations for ATAAD and 36 for aneurysms ≥5.0cm. There were 33 deaths, including 11 deaths late after ATAAD. All deaths were cardiovascular. Late diagnosis, but not early OPT, showed univariate association with delayed RAA (P<0.001) and death (P=0.025). Multivariate Cox regression analysis confirmed late diagnosis as predictor of delayed RAA (hazard ratio (HR)=8.01; 95% confidence interval (95%CI) 2.52-25.45; P<0.001) and death (HR=4.68; 95%CI 1.17-18.80; P=0.029).ConclusionsLate diagnosis of Marfan syndrome is associated with delayed surgery and death.
Large-scale identification of social and behavioral determinants of health from clinical notes: Comparison of Latent Semantic Indexing and Generative Pretrained Transformer (GPT) models
Authors: Roy Sujoy , Morrell Shane , Zhao Lili , Homayouni Ramin 
Published: April 2024

AbstractBackgroundSocial and behavioral determinants of health (SBDH) are associated with a variety of health and utilization outcomes, yet these factors are not routinely documented in the structured fields of electronic health records (EHR). The objective of this study was to evaluate different machine learning approaches for detection of SBDH from the unstructured clinical notes in the EHR.MethodsLatent Semantic Indexing (LSI) was applied to 2,083,180 clinical notes corresponding to 46,146 patients in the MIMIC-III dataset. Using LSI, patients were ranked based on conceptual relevance to a set of keywords (lexicons) pertaining to 15 different SBDH categories. For Generative Pretrained Transformer (GPT) models, API requests were made with a Python script to connect to the OpenAI services in Azure, using gpt-3.5-turbo-1106 and gpt-4-1106-preview models. Prediction of SBDH categories were performed using logistic regression model that included age, gender race and SBDH ICD-9 codes with a natural cubic spline of 2 degrees of freedom for age.ResultsLSI retrieved patients according to 15 SBDH domains, with an overall average PPV≥83%. Using manually curated gold standard (GS) sets for nine SBDH categories, the macro-F1 score of LSI (0.74) was better than ICD-9 (0.71) and GPT-3.5 (0.54), but lower than GPT-4 (0.80). Due to document size limitations, only a subset of the GS cases could be processed by GPT-3.5 (55.8%) and GPT-4 (94.2%), compared to LSI (100%). Using common GS subsets for nine different SBDH categories, the macro-F1 of ICD-9 combined with either LSI (mean 0.88, 95% CI 0.82-0.93), GPT-3.5 (0.86, 0.82-0.91) or GPT-4 (0.88, 0.83-0.94) was not significantly different. After including age, gender, race and ICD-9 in a logistic regression model, the AUC for prediction of six out of the nine SBDH categories was higher for LSI compared to GPT-4.0.ConclusionsThese results demonstrate that the LSI approach performs comparable to more recent large language models, such as GPT-3.5 and GPT-4.0, when using the same set of documents. Importantly, LSI is robust, deterministic, and does not have document-size limitations or cost implications, which make it more amenable to real-world applications in health systems.
Sustained impact of 10-valent pneumococcal conjugate vaccine on invasive pneumococcal disease in Kenya, 2011-2022
Authors: Kagucia E Wangeci , Nyamwaya Brian M , Ongayo Gerald , Kaniu Mary , Sang Samuel , Lucinde Ruth , Karani Angela , Akech Donald , Odiwuor Fredrick , Mataza Christine , Tabu Collins , Mturi Neema , Ndaa Siti , Mulunda Caroline , Etyang Timothy , Aliyan Nadia , Nyaguara Amek , Voller Shirine , Bottomley Christian , Hammitt Laura , Adetifa Ifedayo , Scott J Anthony G 
Published: April 2024

AbstractBackgroundThere are only a few long-term PCV impact assessments in sub-Saharan Africa, and these have been confined to settings using a 13-valent PCV. A 10-valent PCV was introduced in Kenya in 2011 with catchup vaccination among children aged <5 years in Kilifi. We evaluated the impact of PCV10 introduction in Kilifi through 2022.MethodsSurveillance for IPD among residents of the Kilifi Health and Demographic Surveillance System was conducted at the Kilifi County Referral Hospital. Identification of pneumococcus isolated from blood or cerebrospinal fluid and pneumococcal serotyping were conducted according to WHO recommendations. Age– and serotype-specific incidence rate ratios, adjusted for pre-defined confounders (aIRRs), were used to compare annual IPD incidence in the pre-vaccine period to that in 2017-2019 (late post-vaccine) and 2020-2022 (COVID-19).FindingsCompared to the pre-vaccine period, the incidence of vaccine serotype (VT) IPD among children aged <5 years was significantly lower in 2017-2019 (aIRR 0.14; 95%CI 0.04-0.49) and in 2020-2022 (aIRR 0.03; 95%CI 0.00-0.25). It also declined among older children and adults. The incidence of non-VT (NVT) IPD among children aged <15 years was higher during the post-vaccine period. All serotype IPD incidence declined across all age groups. Among individuals with NVT-IPD, serotypes included in new-generation PCVs accounted for about one-third and about one-half of disease among individuals aged <5 years and ≥5 years, respectively.InterpretationDespite potential waning of the effects of catchup vaccination during introduction, reductions in VT-IPD incidence were sustained through 12 years of PCV10 use. All serotype IPD incidence declined across all ages despite serotype replacement among children. New-generation PCVs may enhance IPD control.
Development of a predictive algorithm for patient survival after traumatic injury using a five analyte blood panel
Authors: Fathi Parinaz , Karkanitsa Maria , Rupert Adam , Lin Aaron , Darrah Jenna , Dennis Thomas F. , Lai Jeffrey , Babu Kavita , Neavyn Mark , Kozar Rosemary , Griggs Christopher , Cunningham Kyle W. , Schulman Carl I. , Crandall Marie , Sereti Irini , Ricotta Emily , Sadtler Kaitlyn 
Published: April 2024

ABSTRACTSevere trauma can induce systemic inflammation but also immunosuppression, which makes understanding the immune response of trauma patients critical for therapeutic development and treatment approaches. By evaluating the levels of 59 proteins in the plasma of 50 healthy volunteers and 1000 trauma patients across five trauma centers in the United States, we identified 6 novel changes in immune proteins after traumatic injury and further new variations by sex, age, trauma type, comorbidities, and developed a new equation for prediction of patient survival. Blood was collected at the time of arrival at Level 1 trauma centers and patients were stratified based on trauma level, tissues injured, and injury types. Trauma patients had significantly upregulated proteins associated with immune activation (IL-23, MIP-5), immunosuppression (IL-10) and pleiotropic cytokines (IL-29, IL-6). A high ratio of IL-29 to IL-10 was identified as a new predictor of survival in less severe patients with ROC area of 0.933. Combining machine learning with statistical modeling we developed an equation (“VIPER”) that could predict survival with ROC 0.966 in less severe patients and 0.8873 for all patients from a five analyte panel (IL-6, VEGF-A, IL-21, IL-29, and IL-10). Furthermore, we also identified three increased proteins (MIF, TRAIL, IL-29) and three decreased proteins (IL-7, TPO, IL-8) that were the most important in distinguishing a trauma blood profile. Biologic sex altered phenotype with IL-8 and MIF being lower in healthy women, but higher in female trauma patients when compared to male counterparts. This work identifies new responses to injury that may influence systemic immune dysfunction, serving as targets for therapeutics and immediate clinical benefit in identifying at-risk patients.
Methamphetamine-associated Heart Failure: Clinical phenotypes and Outcomes in a Safety Net Population
Authors: Thakkar Anjali B. , Durstenfeld Matthew S. , Ma Yifei , Win Sithu , Hsue Priscilla Y. 
Published: April 2024

AbstractBackgroundMethamphetamine use has increased dramatically over the past decade and is associated with the development of heart failure (HF). However, clinical characteristics and outcomes have not been well described. This study aimed to compare clinical characteristics and outcomes among individuals with HF who do and do not use methamphetamines in a safety-net hospital.MethodThis retrospective matched cohort study included all individuals with HF with history of methamphetamine use and age, gender-, and year-matched controls without history of methamphetamine use within a municipal health system from 2001-2019. 1,783 individuals with methamphetamine use and HF were identified; 12 were excluded due to inability to identify matched methamphetamine-negative controls. Therefore, 1,771 individuals with methamphetamine use and heart failure and 3,542 age, sex, and year-of-HF-diagnosis matched controls with heart failure without methamphetamine use were included. The primary outcome was all-cause mortality. Secondary outcomes included time to HF hospitalization, and 30-day, 90-day, and 1 year HF and all-cause readmissions.ResultsMedian age of the cohort was 52.1 years and 22.6% were female. There was no significant difference in mortality between the two groups (40% vs 36.6%, HR 1.00, 95% CI 0.91, 1.10, p=1.00). A subset had an index HF hospitalization (n=1,404) during the study period including 637 (35.9%) with history of methamphetamine use and 767 (21.7%) without history of methamphetamine use (relative risk 1.66, 95%CI 1.52-1.81, p<0.0001). Among those ever hospitalized for HF, individuals with methamphetamine use had increased odds of HF and all-cause readmission at 30 days, 90 days, and 1 year.ConclusionDespite having higher risk of both all cause and HF readmissions, individuals with methamphetamine-associated heart failure did not have higher risk of mortality. Measures to address frequent healthcare utilization among people with methamphetamine use and HF are needed.
Diagnostic and prognostic value of ECG-predicted hypertension-mediated left ventricular hypertrophy using machine learning
Authors: Naderi Hafiz , Ramírez Julia , van Duijvenboden Stefan , Pujadas Esmeralda Ruiz , Aung Nay , Wang Lin , Chamling Bishwas , Dörr Marcus , Markus Marcello R P , Chahal C. Anwar A , Lekadir Karim , Petersen Steffen E , Munroe Patricia B 
Published: April 2024

AbstractBackgroundFour hypertension-mediated left ventricular hypertrophy (LVH) phenotypes have been reported using cardiac magnetic resonance (CMR): normal LV, LV remodeling, eccentric and concentric LVH, with varying prognostic implications. The electrocardiogram (ECG) is routinely used to detect LVH, however its capacity to differentiate between LVH phenotypes is unknown. This study aimed to classify hypertension-mediated LVH from the ECG using machine learning (ML) and test for associations of ECG-predicted phenotypes with incident cardiovascular outcomes.MethodsECG biomarkers were extracted from the 12-lead ECG of 20,439 hypertensives in UK Biobank (UKB). Classification models integrating ECG and clinical variables were built using logistic regression, support vector machine (SVM) and random forest. The models were trained in 80% of the participants, and the remaining 20% formed the test set. External validation was sought in 877 hypertensives from the Study of Health in Pomerania (SHIP). In the UKB test set, we tested for associations between ECG-predicted LVH phenotypes and incident major adverse cardiovascular events (MACE) and heart failure.ResultsAmong UKB participants 19,408 had normal LV, 758 LV remodeling, 181 eccentric and 92 concentric LVH. Classification performance of the three models was comparable, with SVM having a slightly superior performance (accuracy 0.79, sensitivity 0.59, specificity 0.87, AUC 0.69) and similar results observed in SHIP. There was superior prediction of eccentric LVH in both cohorts. In the UKB test set, ECG-predicted eccentric LVH was associated with heart failure (HR 3.42, CI 1.06-9.86).ConclusionsECG-based ML classifiers represent a potentially accessible screening strategy for the early detection of hypertension-mediated LVH phenotypes.Graphical abstract
Novel Automatic Classification of Human Adult Lung Alveolar Type II Cells Infected with SARS-CoV-2 through Deep Transfer Learning Approach
Authors: Turki Turki , Al Habib Sarah , Taguchi Y-h. 
Published: April 2024

AbstractSARS-CoV-2 can infect alveoli, inducing a lung injury and thereby impairing the lung function. Healthy alveolar type II (AT2) cells play a major role in lung injury repair as well as keeping alveoli space free from fluids, which is not the case for infected AT2 cells. Unlike previous studies, this novel study aims to automatically differentiate between healthy and infected AT2 cells with SARS-CoV-2 through using efficient AI-based models, which can aid in disease control and treatment. Therefore, we introduce a highly accurate deep transfer learning (DTL) approach that works as follows. First, we downloaded and processed 286 images pertaining to healthy and infected human AT2 (hAT2) cells, obtained from the electron microscopy public image archive. Second, we provided processed images to two DTL computations to induce ten DTL models. The first DTL computation employs five pre-trained models (including DenseNet201 and ResNet152V2) trained on more than million images from ImageNet database to extract features from hAT2 images. Then, flattening and providing the output feature vectors to a trained densely connected classifier with Adam optimizer. The second DTL computation works in a similar manner with a minor difference in which we freeze the first layers for feature extraction in pre-trained models while unfreezing and training the next layers. Compared to TFtDenseNet201, experimental results using five-fold cross-validation demonstrate that TFeDenseNet201 is 12.37 × faster and superior, yielding the highest average ACC of 0.993 (F1 of 0.992 and MCC of 0.986) with statistical significance (p< 2.2 × 10−16from at-test).