bluuebunny/crossref_metadata_2025_split huggingface dataset:
Impact of depression and antidepressant use on clinical outcomes of hepatitis B and C: a population-based study
Authors: Shaheen Abdel Aziz , Kaplan Gilaad G. , Sharkey Keith A. , Lethebe B. Cord , Swain Mark G. 
Published: February 2023

Background:
Depression is common in patients with chronic viral hepatitis. We evaluated the impact of major depressive disorder (MDD) and antidepressant use on survival among patients with HBV and HCV.

Methods:
We used The Health Improvement Network database, the largest medical database in the UK, to identify incident HBV (n=1401) and HCV (n=1635) in patients between 1986 and 2017. Our primary composite outcome was the development of decompensated cirrhosis or death. MDD and each class of antidepressants were assessed in multivariate Cox proportional hazards models. Models were adjusted for age, sex, and clinical comorbidities.

Results:
The prevalence of MDD among HCV patients was higher compared with HBV patients (23.5% vs. 9.0%, p<0.001, respectively). Similarly, HCV patients were more likely to use antidepressants (59.6%) compared with HBV patients (27.1%), p>0.001. MDD was not an independent predictor for decompensated cirrhosis-free survival or mortality. However, the use of tricyclic and tetracyclic antidepressants (TCAs) was associated with poor decompensated cirrhosis-free survival in HBV and HCV cohorts (adjusted HR: 1.80, 95% CI, 1.00–3.26 and 1.56, 95% CI, 1.13–2.14, respectively). Both TCAs in the HBV cohort and selective serotonin reuptake inhibitors among the HCV cohort were associated with poor overall survival (adjusted HR: 2.18, 95% CI, 1.16–4.10; 1.48, 95% CI, 1.02–2.16, respectively).

Conclusions:
Although prevalent among viral hepatitis patients, MDD did not affect disease progression or survival in either HBV or HCV cohorts. TCA use was associated with poor decompensated cirrhosis-free survival. Therefore, its use should be further studied among viral hepatitis patients.
A candidate locus in the renalase gene and susceptibility to blood pressure responses to the dietary salt
Authors: Heydarpour Mahyar , Parksook Wasita W. , Hopkins Paul N. , Pojoga Luminita H. , Williams Gordon H. , Williams Jonathan S. 
Published: February 2023

Background:
High dietary salt confers a risk of elevating blood pressure (BP) and the development of hypertension. BP to salt intake may be determined in part by individual genetic predisposition. Identifying these genetic underpinnings will enhance our understanding of the biological mechanisms of BP regulation. This study aims to assess the genetic association with salt sensitivity of BP (SSBP) within two well-phenotyped multinational cohorts.

Methods:
A total of 720 white participants from the HyperPATH consortium program were selected and genotyped using a multiethnic genotyping array. Individuals consumed two study diets containing high (>200 mEq/day) and low (<10 mEq/day) sodium content, after which SSBP, aldosterone, and plasma renin activity (PRA) were assessed in a controlled inpatient research setting.

Results:
A top signal (rs10887801; beta = 4.57, P = 5.03E − 07) at the renalase gene (RNLS) region was significantly associated with SSBP. We also identified seven single nucleotide variants with linkage disequilibrium to the top signal at this region that comprised a significant haplotype (TCTTAGTT, P = 0.00081). Homozygous carriers of the T-risk allele of the key single nucleotide variant had higher SSBP (P ≤ 0.00001) and lower PRA (P = 0.0076) compared with the nonrisk allele.

Conclusion:
We identified significant associations between genetic variants of the RNLS gene and BP responses to dietary salt intervention and PRA that suggest susceptibility to volume-driven hypertension. These findings may contribute to a better understanding of the genetic mechanisms underlying BP regulation, support the role of RNLS in the pathogenesis of SSBP, and identify individuals who may be at risk from excess dietary salt intake.
Reliability of Postoperative Free Flap Monitoring with a Novel Prediction Model Based on Supervised Machine Learning
Authors: Huang Ren-Wen , Tsai Tzong-Yueh , Hsieh Yun-Huan , Hsu Chung-Chen , Chen Shih-Heng , Lee Che-Hsiung , Lin Yu-Te , Kao Huang-Kai , Lin Cheng-Hung 
Published: February 2023

Background:
Postoperative free flap monitoring is a critical part of reconstructive microsurgery. However, postoperative clinical assessments rely heavily on specialty-trained staff. Therefore, in regions with limited specialists, the feasibility of performing microsurgery is restricted. This study aims to apply artificial intelligence in postoperative free flap monitoring and validates the ability of machine learning in predicting and differentiating types of postoperative free flap circulation.

Methods:
Postoperative data from 176 patients who received free flap surgery were prospectively collected, including free flap photos and clinical evaluation parameters. The flap circulation outcome variables are normal, arterial insufficiency, and venous insufficiency. The Synthetic Minority Oversampling Technique plus Tomek Links (SMOTE-Tomek) was applied for data balance. Data were divided into 80%: 20% for model training and validation. SHapley Additive exPlanations was used for prediction interpretations of the model.

Results:
A total of 805 data were collected, 555 (69%) were normal flaps, 97 (12%) were flaps with arterial insufficiency, and 153 (19%) were flaps with venous insufficiency. The most effective prediction model was developed based on Random Forest with an accuracy of 98.4%. Temperature and color differences between the flap and the surrounding skin were the most significant contributing factors to predict a vascular compromised flap.

Conclusion:
This study demonstrated the reliability of our machine learning model in differentiating various types of postoperative flap circulation. This novel technique may reduce the burden of free flap monitoring and encourage the broader use of reconstructive microsurgeries in regions with limited staff specialists.
Weight Change Following Switch to Dolutegravir for HIV Treatment in Rural Kenya During Country Roll-Out
Authors: Hickey Matthew D. , Wafula Erick , Ogachi Sabina M. , Ojwando Hellen , Orori Gordon , Adede Richard O. , Garraza Lucas Godoy , Petersen Maya L. , Havlir Diane V. , Balzer Laura B. , Ayieko James 
Published: February 2023

Introduction:
Switch to dolutegravir (DTG) in treatment-experienced people living with HIV (PLH) is associated with excess weight gain in some settings; data are limited from rural low-income settings with low obesity prevalence.

Methods:
In rural Kenya, we conducted a retrospective cohort study at 8 HIV clinics and a single-site prospective cohort study including adults switching to DTG during countrywide transition to DTG/tenofovir DF(TDF)/emtricitabine as first-line HIV treatment. In the retrospective analysis, we used preswitch data to model postswitch weight trajectory had each participant not switched to DTG and contrasted observed vs. predicted postswitch weight. In the prospective analysis, we measured weight post-DTG switch and evaluated predictors of 6-month weight change.

Results:
Our retrospective cohort included 4445 PLH who switched to DTG between 2018 and 2020. Mean 12-month weight change was 0.6 kg preswitch and 0.8 kg postswitch. Among those on TDF throughout (n = 3374; 83% on efavirenz preswitch), 12-month postswitch weight was 0.7 kg more than predicted for women (95% CI: 0.4, 1.0) and similar among men (0.04 kg; 95% CI −0.3, 0.4). In our prospective cohort (n = 135, 100% female), mean 6-month weight change was +0.4 kg (IQR −1.1, 2.0 kg). Predicted gain varied by baseline food insecurity: +1.1 kg (95% CI: 0.34, 1.87) among food secure, −0.09 kg (95% CI −0.71, 0.54) among moderate insecure, and +0.27 kg (95% CI −0.82, 1.36) among severe insecurity.

Conclusion:
In contrast to some reports of large weight gain following switch to DTG, we observed small weight increases in women and no weight change in men following DTG switch when on TDF throughout. Weight gain may be attenuated by food insecurity, though was modest even among food secure.
Acute care disruptions due to information technology failures in the Netherlands from 2000 to 2020
Authors: van Boven Liselotte S. , Kusters Renske W.J. , Klokman Vincent W. , Dameff Christian , Barten Dennis G. 
Published: February 2023

ABSTRACTBackgroundHealthcare organizations are becoming increasingly dependent on Information technology (IT) for the provision of routine services. IT failures within hospital systems can impact acute patient care, including emergency department (ED) closure and the cancellation of operations. This study aims to gain insight into the impact of hospital IT failures in the Netherlands on acute care delivery and to provide recommendations for future preparedness.MethodsThis is a systematic scoping review of major acute care disruptions in Dutch hospitals between 2000 and 2020. Incidence of etiology, duration, ED diversion, and closure of multiple locations was evaluated. IT failures were included when associated with the closure of hospital departments and/or evacuations.ResultsThirty-nine IT failures resulting in acute care disruption were identified. The majority occurred between 2010 and 2020 (n=37, 95%). Of the 39 events, 33 (85%) were primary IT failures and were mainly caused by computer network and/or hospital software failure. Secondary events predominantly resulted from power failure. Most events (n= 36, 92%) were resolved within minutes to hours. All events were associated with an ED closure, 27 (69%) with an operating room (OR) stop and two (5%) with external hospital evacuation of one or more patients. Furthermore, 17 incidents (44%) involved multiple hospital locations, and seven (41%) of these involved closure of multiple locations with an ED.ConclusionThe impact of IT failures on acute care disruptions in the Netherlands has considerably increased since 2010. This stresses the urge to improve IT security and business continuity in today’s hospitals.PUBLIC INTEREST SUMMARYAs the healthcare industry is becoming increasingly digitalized, technological failure potentially has a major impact on hospitals and patient care continuity. The current study found that Information Technology (IT) failures in Dutch hospitals between 2000 and 2020 are increasingly associated with acute care disruptions. IT failures were mainly caused by software or hardware failures or by power outages. Preventive measures and emergency planning may lessen the impact and ensure improved business- and patient care continuity.
Designed Transmembrane Proteins Inhibit the Erythropoietin Receptor in a Custom Binding Topology
Authors: Mravic Marco , He Li , Kratochvil Huong , Hu Hailin , Nick Sarah E. , Bai Weiya , Edwards Anne , Jo Hyunil , Wu Yibing , DiMaio Daniel , DeGrado William F. 
Published: February 2023

AbstractTransmembrane (TM) domains as simple as a single span can perform complex biological functions using entirely lipid-embedded chemical features. Computational design has potential to generate custom tool molecules directly targeting membrane proteins at their functional TM regions. Thus far, designed TM domain-targeting agents have been limited to mimicking binding modes and motifs of natural TM interaction partners. Here, we demonstrate the design ofde novoTM proteins targeting the erythropoietin receptor (EpoR) TM domain in a custom binding topology competitive with receptor homodimerization. The TM proteins expressed in mammalian cells complex with EpoR and inhibit erythropoietin-induced cell proliferation.In vitro,the synthetic TM domain complex outcompetes EpoR homodimerization. Structural characterization reveals that the complex involves the intended amino acids and agrees with our designed molecular model of antiparallel TM helices at 1:1 stoichiometry. Thus, membrane protein TM regions can now be targeted in custom designed topologies.
CellWalker: A user-friendly and modular computational pipeline for morphological analysis of microscopy images
Authors: Khare Harshavardhan , Dongo Mendoza Nathaly , Zurzolo Chiara 
Published: February 2023

AbstractThe implementation of computational tools for analysis of microscopy images has been one of the most important technological innovations in biology, providing researchers unmatched capabilities to comprehend cell shape and connectivity. Most available tools frequently focus either on segmentation or morphological analysis, thus not providing an inclusive pipeline. We introduce CellWalker, a computational pipeline that streamlines and connects the segmentation step with the morphological analysis in a modular manner. This python-based pipeline starts with ‘visible-source’ IPython notebooks for segmentation of 2D/3D microscopy images using deep learning and visualization of the segmented images. The next module of CellWalker runs inside Blender, an open-source computer graphics software. This addon provides several morphometric analysis tools that can be used to calculate distances, volume, surface areas and to determine cross-sectional properties. It also includes tools to build skeletons, calculate distributions of sub-cellular organelles. Overall, CellWalker provides practical tools for segmentation and morphological analysis of microscopy images in the form of an open-source and modular pipeline which allows a complete access to fine-tuning of algorithms through visible source code while still retaining a result-oriented interface.Contactharshkhare@gmail.com,chiara.zurzolo@pasteur.frAvailability and implementationCellWalker source code is available on GitHub (https://github.com/utraf-pasteur-institute/CellWalker-notebooksandhttps://github.com/utraf-pasteur-institute/CellWalker-blender) under a GPL-3 license.
Robustness of Hill’s overlapping-generation method for calculatingNeto extreme patterns of reproductive success
Authors: Waples Robin S. 
Published: February 2023

AbstractFor species with overlapping generations, the most widely-used method to calculate effective population size (Ne) is Hill’s, the key parameter for which is lifetime variance in offspring number (Vk•). Hill’s model assumes stable age structure and constant abundance, and sensitivity to those assumptions has been evaluated previously. Here I evaluate robustness of Hill’s model to extreme patterns of reproductive success, whose effects have not been previously examined: 1) very strong reproductive skew; 2) strong temporal autocorrelations in individual reproductive success; and 3) strong covariance of individual reproduction and survival. Genetic drift (loss of heterozygosity and increase in allele-frequency variance) was simulated in age-structured populations using methods that: generated no autocorrelations or covariances (Model NoCor); or created strong positive (Model Positive) or strong negative (Model Negative) temporal autocorrelations in reproduction and covariances between reproduction and survival. Compared to Model NoCor, the other models led to greatly elevated or reducedVk•, and hence greatly reduced or elevatedNe, respectively. A new index is introduced (ρα,α+), which is the correlation between 1) the number of offspring produced by each individual at the age at maturity (α), and 2) the total number of offspring produced during the rest of their lifetimes. Mean ρα,α+was ≈0 under Model NoCor, strongly positive under Model Positive, and strongly negative under Model Negative. Even under the most extreme reproductive scenarios in Models Positive and Negative, whenVk•was calculated from the realized population pedigree and used to calculateNein Hill’s model, the result accurately predicted the rate of genetic drift in simulated populations. These results held for scenarios where age-specific reproductive skew was random (variance≈mean) and highly overdispersed (variance up to 20 times the mean). Collectively, these results are good news for researchers as they demonstrate the robustness of Hill’s model even to extreme repro0ductive scenarios.
Mapping Neurophysiological Subtypes of Major Depressive Disorder Using Normative Models of the Functional Connectome
Authors: Sun Xiaoyi , Liu Jin , Ma Qing , Wang Xiaoqin , Wei Dongtao , Chen Yuan , Liu Bangshan , Huang Chu-Chung , Zheng Yanting , Wu Yankun , Chen Taolin , Cheng Yuqi , Xu Xiufeng , Gong Qiyong , Si Tianmei , Qiu Shijun , Lin Ching-Po , Cheng Jingliang , Tang Yanqing , Wang Fei , Qiu Jiang , Xie Peng , Li Lingjiang , Wang Wenxu , He Yong ,   DIDA-MDD Working Group, Xia Mingrui 
Published: February 2023

AbstractMajor depressive disorder (MDD) is the most burdensome psychiatric disorder characterized by remarkably heterogeneous clinical phenotypes. It remains challenging to delineate the heterogeneity of neurobiological abnormalities underlying the clinical variance and, on this basis, to identify neurophysiological subtypes of MDD patients. Here, using a large multisite resting-state functional MRI data from 1,148 MDD patients and 1,079 healthy controls, we generated lifespan normative models of functional connectivity strengths, mapped the heterogeneity of patients’ individual deviations, and identified neurobiological MDD subtypes. MDD patients showed positive deviations mainly in the default mode and subcortical areas, and negative deviations widely distributed over the cortex. However, there was a great inter-subject heterogeneity as indicated by that no more than 3.14% of patients deviated from the normative range for any brain region. Two neurophysiological MDD subtypes were identified. Subtype 1 showed severe deviations with positive deviations in the default mode, limbic, and subcortical areas, and negative deviations in the sensorimotor, dorsal and ventral attention areas, while subtype 2 showed a moderate but conversed deviation pattern. The severe-deviation subtype had older age, higher medicated proportion, and higher Suicide item score, while the moderate-deviation subtype showed higher Work and Activities and Depressed Mood item scores. Moreover, the baseline deviations in the severe-deviation subtype were predictive of 6-month antidepressant treatment effects in a subsample. To our knowledge, the current study is the largest multisite analysis of neurophysiological MDD subtyping to date and the findings shed light on our understanding of the biological mechanisms underlying the intersubject heterogeneity of clinical phenotypes, which are informative for the development of personalized treatments for this disorder.
Single-cell transcriptomic analysis of mouse liver reveals nonparenchymal cells’ intricate responses to PCB126 exposure
Authors: Xu Fengjun , Fu Yulong , Yang Jiaxuan , Yu Chunna , Shen Chaofeng 
Published: February 2023

AbstractPolychlorinated biphenyls (PCBs) are ubiquitous and representative pollutants that pose great health risks. While cells’ responses to dioxin-like PCBs tend to be studied on a bulk scale, few studies have been made from a single-cell level. Here, by using single-cell RNA sequencing, we depicted a detailed landscape of hepatic nonparenchymal cells’ intricate responses to PCB126 exposure. A total of 13 clusters were identified. Notably, PCB126 exposure resulted in cell-type-specific gene expression profiles and genetic pathways. By analyzing genes related to aryl hydrocarbon receptors, we discovered that PCB126 induced the canonical genomic AhR pathway mainly in endothelial cells. In contrast, other cell types showed little induction. Enrichment pathway analysis indicated that immune cells changed their transcriptional patterns in response to PCB126. ScRNA-seq is a powerful tool to dissect underlying mechanisms of chemical toxicity regarding biological heterogeneity. Taken together, our study not only extends our current understanding of PCB126 toxicity, but also emphasizes the importance ofin vivocell heterogeneity in environmental toxicology.
Neural Systems Underlying the Implementation of Working Memory Removal Operations
Authors: DeRosa Jacob , Kim Hyojeong , Lewis-Peacock Jarrod , Banich Marie T. 
Published: February 2023

AbstractRecently multi-voxel pattern analysis has verified the removal of information from working memory (WM) via three distinct operationsreplacement, suppression, orclearingcompared to information beingmaintained(Kim et al., 2020). Univariate analyses and classifier importance maps indicate that some brain regions commonly contribute to these operations. This study aimed to use multivariate approaches to determine whether, within these commonly activated brain regions, each of these operations is being represented in a similar or distinct manner. To do so, we usedLeiden community detectionto identify brain networks that are characterized by similar multi-voxel patterns of activity with regard to these WM operations. Four networks were identified. The Visual Network shows similar multi-voxel patterns formaintainandreplace, which are highly dissimilar fromsuppressandclear, suggesting this network differentiates whether an item is held in WM or not. The Somatomotor Network shows distinct multi-voxel patterns forclearrelative to the other operations, suggesting that this network diff in clearing information from WM. The Default Mode Network has distinct patterns forsuppressandclear, also suggesting that clearing information from WM is distinct from suppressing it. The Frontoparietal Control Network displays distinct multi-voxel patterns for each of the four operations, suggesting that this network has high involvement in regulating the flow of information within WM. These results indicate that how information is removed from WM depends on distinct brain networks that each have a particular manner in which their co-activation patterns represent these operations.SIGNIFICANCE STATEMENTThe ability to actively remove, manipulate and maintain information in working memory (WM) is required for encoding of new information and for controlling thoughts. This study revealed that different brain networks show characteristic multi-voxel activity patterns across four distinct WM operations: maintenance of information, replacement of one item by another, suppression of a specific item, and clearing the mind of all thought. One network, the Frontoparietal Control Network, differentiates all four operations, suggesting it may play a critical role in the controlled removal of information from WM.
A dynamic inflammation model for neutrophil and monocyte responses in sepsis, trauma and surgery patient clusters
Authors: Browning B. , Couenne F. , Bordes C. , Fayolle L. , Venet F. , Textoris J. , Monneret G. , Tayakout-Fayolle M. 
Published: February 2023

AbstractTime series clustering is applied to inflammation and neutrophil cell development markers, CD16 and CD10, in sepsis, trauma and surgery patients and a dynamical model with an inflammation function,F, is used to represent their evolution over a two month period. Five patient clusters are identified, characterised and evaluated against medical assessment scores and the literature. A dynamical model for neutrophil and monocyte cell counts and maturity has been constructed based on mass balances and cell kinetics in both blood and bone marrow. Cell proliferation and flow rates, as well as expression of monocyte HLA-DR, depend on concentrations of pro- and anti- inflammatory cytokines, IL6 and IL10, viaF. A good fit with the data is obtained for each cluster and the estimated parameters correlate to illness severity. The model is a potential tool for simulation of immunomodulatory therapies.