bluuebunny/crossref_metadata_2025_split huggingface dataset:
Colonization during a key developmental window reveals microbiota-dependent shifts in growth and immunity during undernutrition
Authors: Serrano Matos Yadeliz A. , Cano Jasmine , Shafiq Hamna , Williams Claire , Sunny Julee , Cowardin Carrie A. 
Published: April 2024

Abstract
Background
Childhood undernutrition is a major global health challenge with devastating lifelong consequences. Linear growth stunting due to undernutrition has been linked to poor health outcomes, and mothers who experience growth stunting in childhood are more likely to give birth to stunted children later in life. Based on these findings, we hypothesized that intergenerational colonization of mice with microbiota from human donors with undernutrition may recapitulate certain immune and growth changes observed in this disorder.

Results
To test this hypothesis, we developed a gnotobiotic murine model of undernutrition using microbiota from human infants with healthy or stunted growth trajectories. Intergenerational colonization with microbiota derived from children with growth stunting lead to less linear growth and the development of immune features of undernutrition and enteropathy, including intestinal villus blunting, lower liver IGF-1 and accumulation of intraepithelial lymphocytes and plasma cells in the small intestine. In contrast, colonization after weaning lead to fewer host phenotypic changes between these distinct microbial communities.

Conclusions
These results are broadly consistent with previous findings demonstrating that exposure of the immune system to microbial products during the weaning phase is a critical determinant of later life immune function. Overall, our results suggest intergenerational colonization with human microbiota samples is a useful approach with which to investigate microbiota-dependent changes in growth and immunity in early life. Murine models that capture the intergenerational and multifactorial nature of undernutrition are critical to understanding the underlying biology of this disorder.
Metallothionein-3 is a multifunctional driver that modulates the development of sorafenib-resistant phenotype in hepatocellular carcinoma cells
Authors: Rodrigo Miguel Angel Merlos , Michalkova Hana , Jimenez Ana Maria Jimenez , Petrlak Frantisek , Do Tomas , Sivak Ladislav , Haddad Yazan , Kubickova Petra , de los Rios Vivian , Casal J. Ignacio , Serrano-Macia Marina , Delgado Teresa C. , Boix Loreto , Bruix Jordi , Martinez Chantar Maria L. , Adam Vojtech , Heger Zbynek 
Published: April 2024

Abstract
Background & aims
Metallothionein-3 (hMT3) is a structurally unique member of the metallothioneins family of low-mass cysteine-rich proteins. hMT3 has poorly characterized functions, and its importance for hepatocellular carcinoma (HCC) cells has not yet been elucidated. Therefore, we investigated the molecular mechanisms driven by hMT3 with a special emphasis on susceptibility to sorafenib.

Methods
Intrinsically sorafenib-resistant (BCLC-3) and sensitive (Huh7) cells with or without up-regulated hMT3 were examined using cDNA microarray and methods aimed at mitochondrial flux, oxidative status, cell death, and cell cycle. In addition, in ovo/ex ovo chick chorioallantoic membrane (CAM) assays were conducted to determine a role of hMT3 in resistance to sorafenib and associated cancer hallmarks, such as angiogenesis and metastastic spread. Molecular aspects of hMT3-mediated induction of sorafenib-resistant phenotype were delineated using mass-spectrometry-based proteomics.

Results
The phenotype of sensitive HCC cells can be remodeled into sorafenib-resistant one via up-regulation of hMT3. hMT3 has a profound effect on mitochondrial respiration, glycolysis, and redox homeostasis. Proteomic analyses revealed a number of hMT3-affected biological pathways, including exocytosis, glycolysis, apoptosis, angiogenesis, and cellular stress, which drive resistance to sorafenib.

Conclusions
hMT3 acts as a multifunctional driver capable of inducing sorafenib-resistant phenotype of HCC cells. Our data suggest that hMT3 and related pathways could serve as possible druggable targets to improve therapeutic outcomes in patients with sorafenib-resistant HCC.
Real-world data of cardio-oncologic interventions for cardiovascular adverse events with oral oncolytics
Authors: Abboud Karen , Umoru Godsfavour , Trachtenberg Barry , Ajewole Veronica 
Published: April 2024

Abstract
Background
Oral cancer therapy-related cardiovascular (CV) toxicity has a wide variety of presentations including arrhythmia, cardiomyopathy, and myocardial infarction, but clinical evidence related to its management is limited. The purpose of this IRB-approved, single-center, retrospective, cohort study was to characterize cardio-oncologic interventions for CV adverse events related to oral oncolytics.

Methods
The cohort included 67 patients who were admitted to a multi-hospital health system between June 1, 2016 and July 31, 2021, had at least one medical record order of oral oncolytics considered to have cardiotoxic potential, and had an ICD10 code for a cardiotoxic event added to their electronic medical records after initiation of oral oncolytics.

Results
The majority (97%) had pre-existing cardiovascular disease (CVD) or a CV risk factor. The three most common classes of oral oncolytics were aromatase inhibitors (36%), BCR-ABL inhibitors (16%), and VEGFR inhibitors (13%). New-onset or worsening heart failure (HF) (n = 31), which occurred after a median of 148 days (Interquartile range (IQR) 43–476 days) was the most common cardiotoxic event. The most frequent interventions were pharmacological treatment of the CV adverse event (n = 44) and treatment interruption (n = 18), but guideline-directed medication therapy for HF could be further optimized.

Conclusion
Pre-existing CVD or CV risk factors predispose oncology patients to CV adverse events. Real-world practice reveals that CV adverse events require temporary interruption of treatment and initiation of pharmacologic treatment. A multidisciplinary, patient-centered approach that includes discussion of risks/benefits of treatment continuation, and initiation of guideline-directed treatment is recommended until high-quality, drug-specific data for monitoring and treatment become available.
Locked in and left out: the "prison penalty" for implementation of evidence-based interventions
Authors: Berk Justin , Frank Hannah E. , Drainoni Mari-Lynn 
Published: April 2024

Abstract
Background
While the broader medical community grapples with the widely accepted notion that it takes an average of 17 years for research evidence to be incorporated into clinical practice, the implementation of evidence-based interventions in carceral settings (i.e., jails and prisons) faces longer delays, exacerbating health disparities.

Main body
The “prison implementation penalty” describes the significant delay in and limited adoption of evidence-based healthcare practices in carceral settings. We explore the complex challenges of implementing evidence-based interventions in jails and prisons, environments where healthcare often plays a secondary role under security and discipline. We use specific frameworks to highlight the unique barriers within these settings and propose potential implementation strategies. These challenges have broad implications for health equity due to the disproportionate impact on the marginalized groups affected by mass incarceration. Implementation science has potential to mitigate these disparities.

Conclusion
Bridging the gap between healthcare evidence and practice in carceral settings offers a public health opportunity. Implementation science offers a unique role in improving healthcare standards and reducing health inequities in this environment.
Comparing ChatGPT's and Surgeon's Responses to Thyroid-related Questions From Patients
Authors: Guo Siyin , Li Ruicen , Li Genpeng , Chen Wenjie , Huang Jing , He Linye , Ma Yu , Wang Liying , Zheng Hongping , Tian Chunxiang , Zhao Yatong , Pan Xinmin , Wan Hongxing , Liu Dasheng , Li Zhihui , Lei Jianyong 
Published: April 2024

Abstract

Context
For some common thyroid-related conditions with high prevalence and long follow-up times, ChatGPT can be used to respond to common thyroid-related questions.

Objective
In this cross-sectional study, we assessed the ability of ChatGPT (version GPT-4.0) to provide accurate, comprehensive, compassionate, and satisfactory responses to common thyroid-related questions.

Methods
First, we obtained 28 thyroid-related questions from the Huayitong app, which together with the 2 interfering questions eventually formed 30 questions. Then, these questions were responded to by ChatGPT (on July 19, 2023), a junior specialist, and a senior specialist (on July 20, 2023) separately. Finally, 26 patients and 11 thyroid surgeons evaluated those responses on 4 dimensions: accuracy, comprehensiveness, compassion, and satisfaction.

Results
Among the 30 questions and responses, ChatGPT's speed of response was faster than that of the junior specialist (8.69 [7.53-9.48] vs 4.33 [4.05-4.60]; P &lt; .001) and the senior specialist (8.69 [7.53-9.48] vs 4.22 [3.36-4.76]; P &lt; .001). The word count of the ChatGPT's responses was greater than that of both the junior specialist (341.50 [301.00-384.25] vs 74.50 [51.75-84.75]; P &lt; .001) and senior specialist (341.50 [301.00-384.25] vs 104.00 [63.75-177.75]; P &lt; .001). ChatGPT received higher scores than the junior specialist and senior specialist in terms of accuracy, comprehensiveness, compassion, and satisfaction in responding to common thyroid-related questions.

Conclusion
ChatGPT performed better than a junior specialist and senior specialist in answering common thyroid-related questions, but further research is needed to validate the logical ability of the ChatGPT for complex thyroid questions.
Rescue Lead Implantation After Deep Brain Stimulation for Parkinson's Disease: A Single-Center Experience and Case Series
Authors: Schnurman Zane , Fazl Arash , Feigin Andrew S. , Mogilner Alon Y. , Pourfar Michael 
Published: April 2024

BACKGROUND AND OBJECTIVES:
Despite the well-established efficacy of deep brain stimulation (DBS) of the subthalamic nucleus (STN) for Parkinson's Disease (PD), there remains a subset of patients with only a moderate improvement in symptoms even with appropriate lead placement and optimal programming. In patients with persistent tremor or dyskinesias, one consideration is the addition of a second “rescue lead” to provide dual stimulation to primary and secondary targets to address the refractory component. This study aimed to assess all “rescue lead” cases from our institution and characterize the patients and their outcomes.

METHODS:
Records of all patients with PD treated at our institution between 2005 and 2023 were retrospectively reviewed. Clinical data of all patients treated with a second rescue lead to supplement a positive but inadequate initial DBS response were collected and reviewed.

RESULTS:
Of 670 patients with PD treated at our institution during the study period, 7 were managed with a rescue lead. All 7 were initially treated with STN DBS with a partial improvement in underlying symptoms, had confirmed appropriate lead placement, and underwent thorough programming. Four patients underwent rescue with a globus pallidus interna lead for persistent dyskinesias, all with subsequent improvement in their dyskinesias. Three patients had persistent tremors that were treated with a rescue ventrointermediate thalamus stimulation with subsequent improvement in tremor scores. There were no operative complications, and all patients tolerated dual stimulation.

CONCLUSION:
For a small subset of patients with PD with persistent dyskinesias or tremors after STN DBS despite optimized lead parameters and adequate lead placement, rescue lead placement offers an effective treatment option.
Exploring stem cell frontiers: definitions, challenges, and perspectives for regenerative medicine
Authors: Dardano Miriana , Lebek Tamina , H. C. Tsang Ingrid 
Published: April 2024

ABSTRACT
Each year, the European Summer School on Stem Cell Biology and Regenerative Medicine (SCSS) attracts early-career researchers and actively practicing clinicians who specialise in stem cell and regenerative biology. The 16th edition of this influential course took place from 12th to 19th September 2023 on the charming Greek island of Spetses. Focusing on important concepts and recent advances in stem cells, the distinguished faculty included experts spanning the spectrum from fundamental research to clinical trials to market-approved therapies. Alongside an academically intensive programme that bridges the various contexts of stem cell research, delegates were encouraged to critically address relevant questions in stem cell biology and medicine, including broader societal implications. Here, we present a comprehensive overview and key highlights from the SCSS 2023.
Multiagent Q-Learning Approach for the Recharging Scheduling of Electric Automated Guided Vehicles in Container Terminals
Authors: Zhou Chenhao , Stephen Aloisius , Tan Kok Choon , Chew Ek Peng , Lee Loo Hay 
Published: April 2024

In recent years, advancements in battery technology have led to increased adoption of electric automated guided vehicles in container terminals. Given how critical these vehicles are to terminal operations, this trend requires efficient recharging scheduling for automated guided vehicles, and the main challenges arise from limited charging station capacity and tight vehicle schedules. Motivated by the dynamic nature of the problem, the recharging scheduling problem for an entire vehicle fleet given capacitated stations is formulated as a Markov decision process model. Then, it is solved using a multiagent Q-learning (MAQL) approach to produce a recharging schedule that minimizes the delay of jobs. Numerical experiments show that under a stochastic environment in terms of vehicle travel time, MAQL enables the exploration of better scheduling by coordinating across the entire vehicle fleet and charging facilities and outperforms various benchmark approaches, with an additional improvement of 18.8% on average over the best rule-based heuristic and 5.4% over the predetermined approach. Funding: This work was supported by the National Natural Science Foundation of China [Grant 72101203], the Shaanxi Provincial Key R&D Program, China [Grant 2022KW-02], and the Singapore Maritime Institute [Grant SMI-2017-SP-002]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2022.0113 .
On-Plant Size and Weight Estimation of Tomato Fruits Using Deep Neural Networks and RGB-D Imaging
Authors: Hong Suk-Ju , Kim Sungjay , Lee ChangHyup , Park Seongmin , Kim Kyoung-Chul , Lee Ahyeong , Kim Ghiseok 
Published: April 2024

HighlightsDeep learning-based instance segmentation models were applied and evaluated for tomato fruit detection.Mask R-CNN with vision transformer backbone showed the highest accuracy for tomato instance detection.Size and weight estimation indexes were calculated using tomato region depth data from instance segmentation models.Area-based index has higher accuracy for weight estimation than indexes based on weight and height information.Abstract. The size and weight of fruits are crucial factors in yield prediction and determining harvesting time. Machine vision, including fruit detection, is a key technology in the automated monitoring and harvesting of fruits. In particular, deep learning-based fruit-detection methods have been actively applied. Estimation of fruit size after fruit detection requires depth information, which can be acquired using depth imaging. RGB-D cameras include color and depth information required for fruit detection and size estimation. In this study, the RGB-D imaging technique was used to estimate the size and weight of tomatoes. Furthermore, deep learning-based instance segmentation models, including Mask R-CNN, YOLACT, and RTMDet for tomato fruit detection, were trained and evaluated. The proposed method estimated the fruit width with a root mean square error (RMSE) of 4 mm, a mean absolute percentage error (MAPE) of 4.28%, and a fruit height with an RMSE of 5.12 mm and a MAPE of 6.42%. Furthermore, the weight-prediction model based on the area index estimated the tomato fruit weight with an RMSE of 19.69 g and MAPE of 9.44%. Thus, the method can be used for accurate size and weight estimation and can be applied in growth monitoring and automated tomatoes harvesting. Keywords: Deep learning, Fruit sizing, Instance segmentation, RGB-D, Tomato.
Irrigation Response, Water Use, and Lint Yield of Upland Cotton Cultivars
Authors: Schwartz Robert C. , Witt Travis W. , Ulloa Mauricio , Colaizzi Paul D. , Baumhardt R. Louis 
Published: April 2024

HighlightsIn-season crop water use and canopy temperatures after first square did not differ among cultivars.Lint yield was significantly affected by cultivar in all study years.Cultivar effects on lint yield were significant only at higher irrigation levels.Crop water productivity increased with increasing irrigation in two of the three study years.Abstract. Upland cotton (Gossypium hirsutum L.) production requires less irrigation compared with other crops and thus provides an opportunity to reduce risk and maintain profitability in areas where water is limited. Water use, canopy temperature, lint yield, and crop water productivity were evaluated for four early to medium maturity upland cotton cultivars under three levels (100%, 66%, and 33%) of alternate furrow subsurface drip irrigation (SDI) in a thermally limited environment. Crop evapotranspiration (ET) across cultivars and years averaged 627, 547, and 467 mm for the 100%, 66%, and 33% irrigation levels, respectively, and did not differ among cultivars (P &gt; 0.05). Changes in stored soil water within each irrigation level were similar among cultivars, with significant differences occurring infrequently. Measured canopy temperatures from first white flower to two weeks after cutout did not significantly differ among cultivars (P &gt; 0.10) within each irrigation level. Crop water use during boll maturation, as inferred from the developed crop coefficient curve, was considerably less than reported by other studies, signifying that irrigation could be terminated earlier without reducing lint yield. The cultivar effect on lint yield was significant in all study years (P &lt; 0.001), but only at the 66% and 100% irrigation levels, with one cultivar exceeding the average yields of all evaluated cultivars by 13% across the three study years. Medium maturity cultivars usually yielded less than early maturity cultivars, especially for a year with less accumulation of thermal energy. Crop selection and late season irrigation water management were both key to improving cotton water productivity. Keywords: Upland cotton, Subsurface drip irrigation, Water productivity, Root water uptake, Canopy temperature.
Investigating turbulence distribution in the lower atmosphere using time-lapse imagery from a camera bank
Authors: Wilson Benjamin C. , Bose-Pillai Santasri R. , McCrae Jack E. , Fiorino Steven T. , Freeman Robert P. , Slabaugh Laura R. 
Published: April 2024

The atmosphere’s surface layer (first 50–100 m above the ground)
is extremely dynamic and is influenced by surface radiative
properties, roughness, and atmospheric stability. Understanding the
distribution of turbulence in the surface layer is critical to many
applications, such as directed energy and free space optical
communications. Several measurement campaigns in the past have relied
on weather balloons or sonic detection and ranging (SODAR) to measure
turbulence up to the atmospheric boundary layer. However, these
campaigns had limited measurements near the surface. We have developed
a time-lapse imaging technique to profile atmospheric turbulence from
turbulence-induced differential motion or tilts between features on a
distant target, sensed between pairs of cameras in a camera bank. This
is a low-cost and portable approach to remotely sense turbulence from
a single site without the deployment of sensors at the target
location. It is thus an excellent approach to study the distribution
of turbulence in low altitudes with sufficiently high resolution. In
the present work, the potential of this technique was demonstrated. We
tested the method over a path with constant turbulence. We explored
the turbulence distribution with height in the first 20 m above
the ground by imaging a 30 m water tower over a flat terrain on
three clear days in summer. In addition, we analyzed time-lapse data
from a second water tower over a sloped terrain. In most of the
turbulence profiles extracted from these images, the drop in
turbulence with altitude in the first 15 m or so above the
ground showed a h
m
dependence, where the exponent m varied from −0.3 to −1.0, quite contrary to the widely used
value of −4/3.
Demonstrating the utility of Instrumented Gait Analysis in the treatment of children with cerebral palsy
Authors: Schwartz Michael H. , Ries Andrew J. , Georgiadis Andrew G. , Kainz Hans 
Published: April 2024

Background
Instrumented gait analysis (IGA) has been around for a long time but has never been shown to be useful for improving patient outcomes. In this study we demonstrate the potential utility of IGA by showing that machine learning models are better able to estimate treatment outcomes when they include both IGA and clinical (CLI) features compared to when they include CLI features alone.

Design
We carried out a retrospective analysis of data from ambulatory children diagnosed with cerebral palsy who were seen at least twice at our gait analysis center. Individuals underwent a variety of treatments (including no treatment) between sequential gait analyses. We fit Bayesian Additive Regression Tree (BART) models that estimated outcomes for mean stance foot progression to demonstrate the approach. We built two models: one using CLI features only, and one using CLI and IGA features. We then compared the models’ performance in detail. We performed similar, but less detailed, analyses for a number of other outcomes. All results were based on independent test data from a 70%/30% training/testing split.

Results
The IGA model was more accurate than the CLI model for mean stance-phase foot progression outcomes (RMSEIGA = 11∘, RMSECLI = 13∘) and explained more than 1.5 × as much of the variance (R2IGA = .45, R2CLI = .28). The IGA model outperformed the CLI model for every level of treatment complexity, as measured by number of simultaneous surgeries. The IGA model also exhibited superior performance for estimating outcomes of mean stance-phase knee flexion, mean stance-phase ankle dorsiflexion, maximum swing-phase knee flexion, gait deviation index (GDI), and dimensionless speed.

Interpretation
The results show that IGA has the potential to be useful in the treatment planning process for ambulatory children diagnosed with cerebral palsy. We propose that the results of machine learning outcome estimators—including estimates of uncertainty—become the primary IGA tool utilized in the clinical process, complementing the standard medical practice of conducting a through patient history and physical exam, eliciting patient goals, reviewing relevant imaging data, and so on.