PeerJ
type: channel
link: https://peerj.com/articles/
description: Articles published in PeerJ
publisher: PeerJ Inc.
publication name: PeerJ
issn: 2167-8359
items: Genomic complexity and clinical significance of the RCCX locus
items: Dietary reference intake for military operations: a scoping review
items: Common laboratory results-based artificial intelligence analysis achieves accurate classification of plasma cell dyscrasias
items: Application of composite reference intervals in the diagnosis of subclinical hypothyroidism in the elderly: a retrospective study
items: Quality improvement bundles to decrease hypothermia in very low/extremely low birth weight infants at birth: a systematic review and meta-analysis
items: Applying stacking ensemble method to predict chronic kidney disease progression in Chinese population based on laboratory information system: a retrospective study
items: Potential molecular mechanisms of ETV6-RUNX1-positive B progenitor cell cluster in acute lymphoblastic leukemia revealed by single-cell RNA sequencing
items: Prognosis of hepatitis B virus reactivation in newly diagnosed multiple myeloma in modern era therapy: a retrospective study
items: A novel nomogram to predict the risk of requiring mechanical ventilation in patients with sepsis within 48 hours of admission: a retrospective analysis
items: The behavior patterns of giraffes (Giraffa camelopardalis) housed across 18 US zoos

Genomic complexity and clinical significance of the RCCX locus
type: item
type: AcademicArticle
description: Nearly identical, repetitive elements in the genome contribute to the variability in genetic inheritance patterns, particularly in regions like the RCCX locus, where such repeats can lead to structural variations. In addition, during the formation of gametes as a result of meiosis, variants of loci with repetitive elements that do not code for the required proteins may occur. As a result, an individual with certain genetic rearrangements in this region may have an increased risk of developing a congenital disorder, particularly in cases where the non-functional allele is inherited dominantly. At the same time, there is still no routine or generally recognized diagnostic method to determine the sequence of the repetitive fragments. The functionally important RCCX locus consists of such repetitive fragments. The available knowledge about the genomic variants of the RCCX locus is fragmented, as there is no standardized method to determine its structure. It should be noted that in some structural variants of the RCCX locus, the sequence of protein-coding genes is disrupted, leading to the development of diseases such as congenital adrenal hyperplasia (CAH). Although genetic testing is generally accepted as a gold standard for CAH diagnosis, there are a myriad of strategies on which exact methods to use and in which order. The reason for this inconsistency lies in the complexity of the RCCX locus and the fact that each patient or carrier may have a highly individualized mutation or combination thereof. In this review, we have discussed all known methods that can be used to study the structure of the RCCX locus. As a result, optimal approaches are proposed for the diagnosis of the most common disease caused by lesions in the RCCX–CAH due to CYP21A2 deficiency.
format: application/pdf
format: text/html
creator: Vladimir V. Shiryagin
creator: Andrey A. Devyatkin
creator: Oleg D. Fateev
creator: Ekaterina S. Petriaikina
creator: Viktor P. Bogdanov
creator: Zoia G. Antysheva
creator: Pavel Yu Volchkov
creator: Sergey M. Yudin
creator: Mary Woroncow
creator: Veronika I. Skvortsova
subject: Genetics
subject: Genomics
subject: Medical Genetics
description: doi:10.7717/peerj.18243
publisher: PeerJ Inc.
rights: © 2024 Shiryagin et al.
abstract: Nearly identical, repetitive elements in the genome contribute to the variability in genetic inheritance patterns, particularly in regions like the RCCX locus, where such repeats can lead to structural variations. In addition, during the formation of gametes as a result of meiosis, variants of loci with repetitive elements that do not code for the required proteins may occur. As a result, an individual with certain genetic rearrangements in this region may have an increased risk of developing a congenital disorder, particularly in cases where the non-functional allele is inherited dominantly. At the same time, there is still no routine or generally recognized diagnostic method to determine the sequence of the repetitive fragments. The functionally important RCCX locus consists of such repetitive fragments. The available knowledge about the genomic variants of the RCCX locus is fragmented, as there is no standardized method to determine its structure. It should be noted that in some structural variants of the RCCX locus, the sequence of protein-coding genes is disrupted, leading to the development of diseases such as congenital adrenal hyperplasia (CAH). Although genetic testing is generally accepted as a gold standard for CAH diagnosis, there are a myriad of strategies on which exact methods to use and in which order. The reason for this inconsistency lies in the complexity of the RCCX locus and the fact that each patient or carrier may have a highly individualized mutation or combination thereof. In this review, we have discussed all known methods that can be used to study the structure of the RCCX locus. As a result, optimal approaches are proposed for the diagnosis of the most common disease caused by lesions in the RCCX–CAH due to CYP21A2 deficiency.
bibliographic citation: Shiryagin et al. (2024) Genomic complexity and clinical significance of the RCCX locus. PeerJ 12:e18243 https://doi.org/10.7717/peerj.18243
date submitted: 2024-05-28
date accepted: 2024-09-15
issued: 2024-11-04
is part of: PeerJ
license: https://creativecommons.org/licenses/by/4.0/
author list: Agent
  family name: Shiryagin
  given name: Vladimir V.
author list: Agent
  family name: Devyatkin
  given name: Andrey A.
author list: Agent
  family name: Fateev
  given name: Oleg D.
author list: Agent
  family name: Petriaikina
  given name: Ekaterina S.
author list: Agent
  family name: Bogdanov
  given name: Viktor P.
author list: Agent
  family name: Antysheva
  given name: Zoia G.
author list: Agent
  family name: Volchkov
  given name: Pavel Yu
author list: Agent
  family name: Yudin
  given name: Sergey M.
author list: Agent
  family name: Woroncow
  given name: Mary
author list: Agent
  family name: Skvortsova
  given name: Veronika I.
doi: 10.7717/peerj.18243
volume: 12
issn: 2167-8359
publication name: PeerJ
publication date: 2024-11-04
copyright: ©2024 Shiryagin et al.
attribution name: Shiryagin et al.

Dietary reference intake for military operations: a scoping review
type: item
type: AcademicArticle
description: Background Reports that collect and organize dietary reference intake (DRI) data for military operations in different countries and regions worldwide are limited.This scoping review aimed to collect and organize information on the status of formulating a DRI for military operations in each country. Methodology For the information search, we queried PubMed and Google for literature and reports on the DRI for military operations and summarized the content of the adopted literature and reports. Results The content and rationale for DRI for military operations in Australia, the United Kingdom (UK), the United States of America (USA), and the North Atlantic Treaty Organization (NATO) can be summarized as follows: (1) Energy requirements: Four reports formulated physical activity levels (PALs) and corresponding energy requirements that differed from those for the civilian public. The PAL range for the military was set as high as 1.50–3.20, as opposed to the standard civilian upper PAL set at 1.20–2.20. (2) Protein: Three military reports outside of the UK had different standards than those for the civilian public with an increased intake in accordance with the high PAL while simultaneously preventing excessive intake.In the military, values were formulated 1.2–4.8 times higher than the standards for civilians (45–65 g/day to 55–307 g/day). (3) Macronutrient energy distribution: Four military reports established macronutrient energy distributions that differed from those for the civilian public. The DRI for the Australian and UK militaries was formulated such that as PAL increased, protein decreased, fat decreased or remained unchanged, and carbohydrate increased. (4) Sodium: Considering that military personnel sweat more due to high physical activity and their environment, two Australian and NATO reports were established with sodium levels that were twice as high as that of the civilian public (460–2,300 mg/day to 920–3,200 mg/day). Increasing sodium intake to <4,800 mg/d…
format: application/pdf
format: text/html
creator: Ryoko Mizushima
creator: Motohiko Miyachi
creator: Eiichi Yoshimura
creator: Yoichi Hatamoto
creator: Mai Matsumoto
creator: Yuka Hamada
creator: Mana Hatanaka
creator: Aya Maeno
creator: Chifumi Shimomura
creator: Hidemi Takimoto
subject: Nutrition
subject: Public Health
description: doi:10.7717/peerj.18353
publisher: PeerJ Inc.
rights: © 2024 Mizushima et al.
abstract: Background Reports that collect and organize dietary reference intake (DRI) data for military operations in different countries and regions worldwide are limited.This scoping review aimed to collect and organize information on the status of formulating a DRI for military operations in each country. Methodology For the information search, we queried PubMed and Google for literature and reports on the DRI for military operations and summarized the content of the adopted literature and reports. Results The content and rationale for DRI for military operations in Australia, the United Kingdom (UK), the United States of America (USA), and the North Atlantic Treaty Organization (NATO) can be summarized as follows: (1) Energy requirements: Four reports formulated physical activity levels (PALs) and corresponding energy requirements that differed from those for the civilian public. The PAL range for the military was set as high as 1.50–3.20, as opposed to the standard civilian upper PAL set at 1.20–2.20. (2) Protein: Three military reports outside of the UK had different standards than those for the civilian public with an increased intake in accordance with the high PAL while simultaneously preventing excessive intake.In the military, values were formulated 1.2–4.8 times higher than the standards for civilians (45–65 g/day to 55–307 g/day). (3) Macronutrient energy distribution: Four military reports established macronutrient energy distributions that differed from those for the civilian public. The DRI for the Australian and UK militaries was formulated such that as PAL increased, protein decreased, fat decreased or remained unchanged, and carbohydrate increased. (4) Sodium: Considering that military personnel sweat more due to high physical activity and their environment, two Australian and NATO reports were established with sodium levels that were twice as high as that of the civilian public (460–2,300 mg/day to 920–3,200 mg/day). Increasing sodium intake to <4,800 mg/d…
bibliographic citation: Mizushima et al. (2024) Dietary reference intake for military operations: a scoping review. PeerJ 12:e18353 https://doi.org/10.7717/peerj.18353
date submitted: 2024-03-08
date accepted: 2024-09-27
issued: 2024-11-04
is part of: PeerJ
license: https://creativecommons.org/licenses/by/4.0/
author list: Agent
  family name: Mizushima
  given name: Ryoko
author list: Agent
  family name: Miyachi
  given name: Motohiko
author list: Agent
  family name: Yoshimura
  given name: Eiichi
author list: Agent
  family name: Hatamoto
  given name: Yoichi
author list: Agent
  family name: Matsumoto
  given name: Mai
author list: Agent
  family name: Hamada
  given name: Yuka
author list: Agent
  family name: Hatanaka
  given name: Mana
author list: Agent
  family name: Maeno
  given name: Aya
author list: Agent
  family name: Shimomura
  given name: Chifumi
author list: Agent
  family name: Takimoto
  given name: Hidemi
doi: 10.7717/peerj.18353
volume: 12
issn: 2167-8359
publication name: PeerJ
publication date: 2024-11-04
copyright: ©2024 Mizushima et al.
attribution name: Mizushima et al.

Common laboratory results-based artificial intelligence analysis achieves accurate classification of plasma cell dyscrasias
type: item
type: AcademicArticle
description: Background Plasma cell dyscrasias encompass a diverse set of disorders, where early and precise diagnosis is essential for optimizing patient outcomes. Despite advancements, current diagnostic methodologies remain underutilized in applying artificial intelligence (AI) to routine laboratory data. This study seeks to construct an AI-driven model leveraging standard laboratory parameters to enhance diagnostic accuracy and classification efficiency in plasma cell dyscrasias. Methods Data from 1,188 participants (609 with plasma cell dyscrasias and 579 controls) collected between 2018 and 2023 were analyzed. Initial variable selection employed Kruskal-Wallis and Wilcoxon tests, followed by dimensionality reduction and variable prioritization using the Shapley Additive Explanations (SHAP) approach. Nine pivotal variables were identified, including hemoglobin (HGB), serum creatinine, and β2-microglobulin. Utilizing these, four machine learning models (gradient boosting decision tree (GBDT), support vector machine (SVM), deep neural network (DNN), and decision tree (DT) were developed and evaluated, with performance metrics such as accuracy, recall, and area under the curve (AUC) assessed through 5-fold cross-validation. A subtype classification model was also developed, analyzing data from 380 cases to classify disorders such as multiple myeloma (MM) and monoclonal gammopathy of undetermined significance (MGUS). Results 1. Variable selection: The SHAP method pinpointed nine critical variables, including hemoglobin (HGB), serum creatinine, erythrocyte sedimentation rate (ESR), and β2-microglobulin. 2. Diagnostic model performance: The GBDT model exhibited superior diagnostic performance for plasma cell dyscrasias, achieving 93.5% accuracy, 98.1% recall, and an AUC of 0.987. External validation reinforced its robustness, with 100% accuracy and an F1 score of 98.5%. 3. Subtype Classification: The DNN model excelled in classifying multiple myeloma, MGUS, and light-chain myelom…
format: application/pdf
format: text/html
creator: Bihua Yao
creator: Yicheng Liu
creator: Yuwei Wu
creator: Siyu Mao
creator: Hangbiao Zhang
creator: Lei Jiang
creator: Cheng Fei
creator: Shuang Wang
creator: Jijun Tong
creator: Jianguo Wu
subject: Computational Biology
subject: Hematology
subject: Oncology
subject: Computational Science
subject: Data Mining and Machine Learning
description: doi:10.7717/peerj.18391
publisher: PeerJ Inc.
rights: © 2024 Yao et al.
abstract: Background Plasma cell dyscrasias encompass a diverse set of disorders, where early and precise diagnosis is essential for optimizing patient outcomes. Despite advancements, current diagnostic methodologies remain underutilized in applying artificial intelligence (AI) to routine laboratory data. This study seeks to construct an AI-driven model leveraging standard laboratory parameters to enhance diagnostic accuracy and classification efficiency in plasma cell dyscrasias. Methods Data from 1,188 participants (609 with plasma cell dyscrasias and 579 controls) collected between 2018 and 2023 were analyzed. Initial variable selection employed Kruskal-Wallis and Wilcoxon tests, followed by dimensionality reduction and variable prioritization using the Shapley Additive Explanations (SHAP) approach. Nine pivotal variables were identified, including hemoglobin (HGB), serum creatinine, and β2-microglobulin. Utilizing these, four machine learning models (gradient boosting decision tree (GBDT), support vector machine (SVM), deep neural network (DNN), and decision tree (DT) were developed and evaluated, with performance metrics such as accuracy, recall, and area under the curve (AUC) assessed through 5-fold cross-validation. A subtype classification model was also developed, analyzing data from 380 cases to classify disorders such as multiple myeloma (MM) and monoclonal gammopathy of undetermined significance (MGUS). Results 1. Variable selection: The SHAP method pinpointed nine critical variables, including hemoglobin (HGB), serum creatinine, erythrocyte sedimentation rate (ESR), and β2-microglobulin. 2. Diagnostic model performance: The GBDT model exhibited superior diagnostic performance for plasma cell dyscrasias, achieving 93.5% accuracy, 98.1% recall, and an AUC of 0.987. External validation reinforced its robustness, with 100% accuracy and an F1 score of 98.5%. 3. Subtype Classification: The DNN model excelled in classifying multiple myeloma, MGUS, and light-chain myelom…
bibliographic citation: Yao et al. (2024) Common laboratory results-based artificial intelligence analysis achieves accurate classification of plasma cell dyscrasias. PeerJ 12:e18391 https://doi.org/10.7717/peerj.18391
date submitted: 2024-05-07
date accepted: 2024-10-02
issued: 2024-11-04
is part of: PeerJ
license: https://creativecommons.org/licenses/by-nc/4.0
author list: Agent
  family name: Yao
  given name: Bihua
author list: Agent
  family name: Liu
  given name: Yicheng
author list: Agent
  family name: Wu
  given name: Yuwei
author list: Agent
  family name: Mao
  given name: Siyu
author list: Agent
  family name: Zhang
  given name: Hangbiao
author list: Agent
  family name: Jiang
  given name: Lei
author list: Agent
  family name: Fei
  given name: Cheng
author list: Agent
  family name: Wang
  given name: Shuang
author list: Agent
  family name: Tong
  given name: Jijun
author list: Agent
  family name: Wu
  given name: Jianguo
doi: 10.7717/peerj.18391
volume: 12
issn: 2167-8359
publication name: PeerJ
publication date: 2024-11-04
copyright: © 2024 Yao et al.
attribution name: Yao et al.

Application of composite reference intervals in the diagnosis of subclinical hypothyroidism in the elderly: a retrospective study
type: item
type: AcademicArticle
description: Background Thyroid stimulating hormone releasing hormone (TSH) is a key indicator for diagnosing subclinical hypothyroidism (SCH). We evaluated factors affecting TSH levels in elderly SCH, establishing a composite reference interval, and comparing it with traditional one in diagnosis. Methods We collected data on patients aged ≥60 undergoing physical examinations in Chengdu, screening the influencing factors associated with TSH. Then, a two-dimensional composite reference interval was established for TSH and FT4, and the differences between the new and traditional diagnosing methods were compared. Results The incidence of subclinical thyroid dysfunction was about 14%, with SCH accounting for 97%. Regression analysis found that TSH levels increase as FT4 and uric acid levels decrease. Compared with the two-dimensional composite reference interval, the traditional one has a higher incidence rate of SCH. Conclusion Compared with the two-dimensional composite reference interval, the traditional one is more likely to overestimate the incidence rate of SCH, leading to excessive diagnosis and treatment.
format: application/pdf
format: text/html
creator: Peijuan Li
creator: Wenming Yang
creator: Guohua Tang
creator: Zhipeng Li
subject: Diabetes and Endocrinology
subject: Internal Medicine
description: doi:10.7717/peerj.18417
publisher: PeerJ Inc.
rights: © 2024 Li et al.
abstract: Background Thyroid stimulating hormone releasing hormone (TSH) is a key indicator for diagnosing subclinical hypothyroidism (SCH). We evaluated factors affecting TSH levels in elderly SCH, establishing a composite reference interval, and comparing it with traditional one in diagnosis. Methods We collected data on patients aged ≥60 undergoing physical examinations in Chengdu, screening the influencing factors associated with TSH. Then, a two-dimensional composite reference interval was established for TSH and FT4, and the differences between the new and traditional diagnosing methods were compared. Results The incidence of subclinical thyroid dysfunction was about 14%, with SCH accounting for 97%. Regression analysis found that TSH levels increase as FT4 and uric acid levels decrease. Compared with the two-dimensional composite reference interval, the traditional one has a higher incidence rate of SCH. Conclusion Compared with the two-dimensional composite reference interval, the traditional one is more likely to overestimate the incidence rate of SCH, leading to excessive diagnosis and treatment.
bibliographic citation: Li et al. (2024) Application of composite reference intervals in the diagnosis of subclinical hypothyroidism in the elderly: a retrospective study. PeerJ 12:e18417 https://doi.org/10.7717/peerj.18417
date submitted: 2024-05-19
date accepted: 2024-10-07
issued: 2024-11-01
is part of: PeerJ
license: https://creativecommons.org/licenses/by/4.0/
author list: Agent
  family name: Li
  given name: Peijuan
author list: Agent
  family name: Yang
  given name: Wenming
author list: Agent
  family name: Tang
  given name: Guohua
author list: Agent
  family name: Li
  given name: Zhipeng
doi: 10.7717/peerj.18417
volume: 12
issn: 2167-8359
publication name: PeerJ
publication date: 2024-11-01
copyright: ©2024 Li et al.
attribution name: Li et al.

Quality improvement bundles to decrease hypothermia in very low/extremely low birth weight infants at birth: a systematic review and meta-analysis
type: item
type: AcademicArticle
description: Background Numerous studies have demonstrated that hypothermia in preterm infants correlates with increased morbidity and mortality, especially among those with very low or extremely low birth weights (VLBW/ELBW). An increasing number of healthcare facilities are implementing quality improvement (QI) bundles to lower the incidence of hypothermia at birth in this vulnerable population. However, the effectiveness and safety of these interventions have yet to be fully assessed. A meta-analysis is necessary to evaluate the efficacy and safety of QI bundles in reducing hypothermia at birth among VLBW/ELBW infants. Methods We searched PubMed, Embase, the Cochrane Library and Web of Science through April 22nd, 2024. Study selection, data extraction, quality evaluation and risk bias assessment were performed independently by two investigators. Meta-analysis was performed using Review Manager 5.4.1. Results A total of 18 studies were included for qualitative analysis and 12 for meta-analysis. For VLBW infants, meta-analysis revealed a reduction in hypothermia and an increase in hyperthermia following the introduction of QI bundles (mild hypothermia, OR 0.22, 95% CI [0.13–0.37]; moderate hypothermia, OR 0.18, 95% CI [0.15–0.22]; hyperthermia, OR 2.79, 95% CI [1.53–5.09]). For ELBW infants, meta-analysis showed a decrease in hypothermia but no increase in hyperthermia after implementing QI bundles (mild hypothermia, OR 0.46, 95% CI [0.26–0.81]; moderate hypothermia, OR 0.21, 95% CI [0.08–0.58]; hyperthermia, OR 1.10, 95% CI [0.22–5.43]). Conclusion QI bundles effectively reduce hypothermia in VLBW/ELBW infants, but they may also increase hyperthermia, especially in VLBW infants.
format: application/pdf
format: text/html
creator: Guichao Zhong
creator: Jie Qi
creator: Lijuan Sheng
creator: Jing Zhuang
creator: Zhangbin Yu
creator: Benqing Wu
subject: Nursing
subject: Pediatrics
description: doi:10.7717/peerj.18425
publisher: PeerJ Inc.
rights: © 2024 Zhong et al.
abstract: Background Numerous studies have demonstrated that hypothermia in preterm infants correlates with increased morbidity and mortality, especially among those with very low or extremely low birth weights (VLBW/ELBW). An increasing number of healthcare facilities are implementing quality improvement (QI) bundles to lower the incidence of hypothermia at birth in this vulnerable population. However, the effectiveness and safety of these interventions have yet to be fully assessed. A meta-analysis is necessary to evaluate the efficacy and safety of QI bundles in reducing hypothermia at birth among VLBW/ELBW infants. Methods We searched PubMed, Embase, the Cochrane Library and Web of Science through April 22nd, 2024. Study selection, data extraction, quality evaluation and risk bias assessment were performed independently by two investigators. Meta-analysis was performed using Review Manager 5.4.1. Results A total of 18 studies were included for qualitative analysis and 12 for meta-analysis. For VLBW infants, meta-analysis revealed a reduction in hypothermia and an increase in hyperthermia following the introduction of QI bundles (mild hypothermia, OR 0.22, 95% CI [0.13–0.37]; moderate hypothermia, OR 0.18, 95% CI [0.15–0.22]; hyperthermia, OR 2.79, 95% CI [1.53–5.09]). For ELBW infants, meta-analysis showed a decrease in hypothermia but no increase in hyperthermia after implementing QI bundles (mild hypothermia, OR 0.46, 95% CI [0.26–0.81]; moderate hypothermia, OR 0.21, 95% CI [0.08–0.58]; hyperthermia, OR 1.10, 95% CI [0.22–5.43]). Conclusion QI bundles effectively reduce hypothermia in VLBW/ELBW infants, but they may also increase hyperthermia, especially in VLBW infants.
bibliographic citation: Zhong et al. (2024) Quality improvement bundles to decrease hypothermia in very low/extremely low birth weight infants at birth: a systematic review and meta-analysis. PeerJ 12:e18425 https://doi.org/10.7717/peerj.18425
date submitted: 2024-06-17
date accepted: 2024-10-08
issued: 2024-11-01
is part of: PeerJ
license: https://creativecommons.org/licenses/by/4.0/
author list: Agent
  family name: Zhong
  given name: Guichao
author list: Agent
  family name: Qi
  given name: Jie
author list: Agent
  family name: Sheng
  given name: Lijuan
author list: Agent
  family name: Zhuang
  given name: Jing
author list: Agent
  family name: Yu
  given name: Zhangbin
author list: Agent
  family name: Wu
  given name: Benqing
doi: 10.7717/peerj.18425
volume: 12
issn: 2167-8359
publication name: PeerJ
publication date: 2024-11-01
copyright: © 2024 Zhong et al.
attribution name: Zhong et al.

Applying stacking ensemble method to predict chronic kidney disease progression in Chinese population based on laboratory information system: a retrospective study
type: item
type: AcademicArticle
description: Background and Objective Chronic kidney disease (CKD) is a major public health issue, and accurate prediction of the progression of kidney failure is critical for clinical decision-making and helps improve patient outcomes. As such, we aimed to develop and externally validate a machine-learned model to predict the progression of CKD using common laboratory variables, demographic characteristics, and an electronic health records database. Methods We developed a predictive model using longitudinal clinical data from a single center for Chinese CKD patients. The cohort included 987 patients who were followed up for more than 24 months. Fifty-three laboratory features were considered for inclusion in the model. The primary outcome in our study was an estimated glomerular filtration rate ≤15 mL/min/1.73 m2 or kidney failure. Machine learning algorithms were applied to the modeling dataset (n = 296), and an external dataset (n = 71) was used for model validation. We assessed model discrimination via area under the curve (AUC) values, accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1 score. Results Over a median follow-up period of 3.75 years, 148 patients experienced kidney failure. The optimal model was based on stacking different classifier algorithms with six laboratory features, including 24-h urine protein, potassium, glucose, urea, prealbumin and total protein. The model had considerable predictive power, with AUC values of 0.896 and 0.771 in the validation and external datasets, respectively. This model also accurately predicted the progression of renal function in patients over different follow-up periods after their initial assessment. Conclusions A prediction model that leverages routinely collected laboratory features in the Chinese population can accurately identify patients with CKD at high risk of progressing to kidney failure. An online version of the model can be easily and quickly applied in clinical manageme…
format: application/pdf
format: text/html
creator: Jialin Du
creator: Jie Gao
creator: Jie Guan
creator: Bo Jin
creator: Nan Duan
creator: Lu Pang
creator: Haiming Huang
creator: Qian Ma
creator: Chenwei Huang
creator: Haixia Li
subject: Evidence Based Medicine
subject: Nephrology
subject: Data Mining and Machine Learning
subject: Data Science
description: doi:10.7717/peerj.18436
publisher: PeerJ Inc.
rights: © 2024 Du et al.
abstract: Background and Objective Chronic kidney disease (CKD) is a major public health issue, and accurate prediction of the progression of kidney failure is critical for clinical decision-making and helps improve patient outcomes. As such, we aimed to develop and externally validate a machine-learned model to predict the progression of CKD using common laboratory variables, demographic characteristics, and an electronic health records database. Methods We developed a predictive model using longitudinal clinical data from a single center for Chinese CKD patients. The cohort included 987 patients who were followed up for more than 24 months. Fifty-three laboratory features were considered for inclusion in the model. The primary outcome in our study was an estimated glomerular filtration rate ≤15 mL/min/1.73 m2 or kidney failure. Machine learning algorithms were applied to the modeling dataset (n = 296), and an external dataset (n = 71) was used for model validation. We assessed model discrimination via area under the curve (AUC) values, accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1 score. Results Over a median follow-up period of 3.75 years, 148 patients experienced kidney failure. The optimal model was based on stacking different classifier algorithms with six laboratory features, including 24-h urine protein, potassium, glucose, urea, prealbumin and total protein. The model had considerable predictive power, with AUC values of 0.896 and 0.771 in the validation and external datasets, respectively. This model also accurately predicted the progression of renal function in patients over different follow-up periods after their initial assessment. Conclusions A prediction model that leverages routinely collected laboratory features in the Chinese population can accurately identify patients with CKD at high risk of progressing to kidney failure. An online version of the model can be easily and quickly applied in clinical manageme…
bibliographic citation: Du et al. (2024) Applying stacking ensemble method to predict chronic kidney disease progression in Chinese population based on laboratory information system: a retrospective study. PeerJ 12:e18436 https://doi.org/10.7717/peerj.18436
date submitted: 2024-07-26
date accepted: 2024-10-10
issued: 2024-11-01
is part of: PeerJ
license: https://creativecommons.org/licenses/by-nc/4.0
author list: Agent
  family name: Du
  given name: Jialin
author list: Agent
  family name: Gao
  given name: Jie
author list: Agent
  family name: Guan
  given name: Jie
author list: Agent
  family name: Jin
  given name: Bo
author list: Agent
  family name: Duan
  given name: Nan
author list: Agent
  family name: Pang
  given name: Lu
author list: Agent
  family name: Huang
  given name: Haiming
author list: Agent
  family name: Ma
  given name: Qian
author list: Agent
  family name: Huang
  given name: Chenwei
author list: Agent
  family name: Li
  given name: Haixia
doi: 10.7717/peerj.18436
volume: 12
issn: 2167-8359
publication name: PeerJ
publication date: 2024-11-01
copyright: © 2024 Du et al.
attribution name: Du et al.

Potential molecular mechanisms of ETV6-RUNX1-positive B progenitor cell cluster in acute lymphoblastic leukemia revealed by single-cell RNA sequencing
type: item
type: AcademicArticle
description: Aim This study was to explore role of immune landscape and the immune cells in acute lymphoblastic leukemia (ALL) progression. Background The most prevalent genetic alteration in childhood ALL is the ETV6-RUNX1 fusion. The increased proliferation of B progenitor cells could expedite the disease’s progression due to irregularities in the cell cycle. Nevertheless, the mechanisms by which particular cell clusters influence the cell cycle and promote the advancement of ALL are still not well understood. Objective This study was to explore role of immune landscape and the immune cells in ALL progression. Methods Single-cell RNA sequencing (scRNA-seq) data of ETV6-RUNX1 and healthy pediatric samples obtained from GSE132509 were clustered and annotated using the Seurat package, and differentially highly expressed genes identified in each cluster were analyzed using DAVID for pathway annotation. Chromosome amplification and deletion were analyzed using the inferCNV package. SCENIC evaluated the regulation of transcription factors and target gene formation in cells. cellphoneDB and CellChat were served to infer ligand-receptor pairs that mediate interactions between subpopulations. The role of the target gene in regulating ALL progression was assessed using RT-qPCR, Transwell and scratch healing assays. Results The bone marrow mononuclear cells (BMMCs) from ETV6-RUNX1 and healthy pediatric samples in GSE132509 were divided into 11 clusters, and B cell cluster 1 was identified as B progenitor cell, which was amplified on chromosome 6p. B progenitor cells were divided into seven clusters. Expression levels of amplified genes in chromosome 6p of B progenitor cell cluster 5 were the highest, and its specific highly expressed genes were annotated to pathways promoting cell cycle progression. Regulons formed in B progenitor cell cluster 5 were all involved in promoting cell cycle progression, so it was regarded as the B progenitor cell cluster that drives cell cycle progression. T…
format: application/pdf
format: text/html
creator: Ning Qu
creator: Yue Wan
creator: Xin Sui
creator: Tianyi Sui
creator: Yang Yang
subject: Cell Biology
subject: Immunology
subject: Pediatrics
description: doi:10.7717/peerj.18445
publisher: PeerJ Inc.
rights: © 2024 Qu et al.
abstract: Aim This study was to explore role of immune landscape and the immune cells in acute lymphoblastic leukemia (ALL) progression. Background The most prevalent genetic alteration in childhood ALL is the ETV6-RUNX1 fusion. The increased proliferation of B progenitor cells could expedite the disease’s progression due to irregularities in the cell cycle. Nevertheless, the mechanisms by which particular cell clusters influence the cell cycle and promote the advancement of ALL are still not well understood. Objective This study was to explore role of immune landscape and the immune cells in ALL progression. Methods Single-cell RNA sequencing (scRNA-seq) data of ETV6-RUNX1 and healthy pediatric samples obtained from GSE132509 were clustered and annotated using the Seurat package, and differentially highly expressed genes identified in each cluster were analyzed using DAVID for pathway annotation. Chromosome amplification and deletion were analyzed using the inferCNV package. SCENIC evaluated the regulation of transcription factors and target gene formation in cells. cellphoneDB and CellChat were served to infer ligand-receptor pairs that mediate interactions between subpopulations. The role of the target gene in regulating ALL progression was assessed using RT-qPCR, Transwell and scratch healing assays. Results The bone marrow mononuclear cells (BMMCs) from ETV6-RUNX1 and healthy pediatric samples in GSE132509 were divided into 11 clusters, and B cell cluster 1 was identified as B progenitor cell, which was amplified on chromosome 6p. B progenitor cells were divided into seven clusters. Expression levels of amplified genes in chromosome 6p of B progenitor cell cluster 5 were the highest, and its specific highly expressed genes were annotated to pathways promoting cell cycle progression. Regulons formed in B progenitor cell cluster 5 were all involved in promoting cell cycle progression, so it was regarded as the B progenitor cell cluster that drives cell cycle progression. T…
bibliographic citation: Qu et al. (2024) Potential molecular mechanisms of ETV6-RUNX1-positive B progenitor cell cluster in acute lymphoblastic leukemia revealed by single-cell RNA sequencing. PeerJ 12:e18445 https://doi.org/10.7717/peerj.18445
date submitted: 2024-08-02
date accepted: 2024-10-11
issued: 2024-11-01
is part of: PeerJ
license: https://creativecommons.org/licenses/by/4.0/
author list: Agent
  family name: Qu
  given name: Ning
author list: Agent
  family name: Wan
  given name: Yue
author list: Agent
  family name: Sui
  given name: Xin
author list: Agent
  family name: Sui
  given name: Tianyi
author list: Agent
  family name: Yang
  given name: Yang
doi: 10.7717/peerj.18445
volume: 12
issn: 2167-8359
publication name: PeerJ
publication date: 2024-11-01
copyright: © 2024 Qu et al.
attribution name: Qu et al.

Prognosis of hepatitis B virus reactivation in newly diagnosed multiple myeloma in modern era therapy: a retrospective study
type: item
type: AcademicArticle
description: Studies on the prognosis of hepatitis B virus (HBV) reactivation following modern therapies for newly diagnosed MM (NDMM) are lacking. In this retrospective study, we aimed to assess the incidence, risk factors and prognosis of HBV reactivation in NDMM. A total of 33 of 355 patients with NDMM and HBV reactivation were included in this study. Multivariable analysis showed that hepatitis B surface antigen-positivity, hepatitis B core antibody-positivity, bortezomib-containing regimens, autologous stem cell transplantation, and gain of 1q21 were identified as independent risk factors of HBV reactivation in NDMM patients. The NDMM patients with HBV reactivation had poorer 3-year overall survival (OS) and progression-free survival (PFS) than did those without HBV reactivation, as confirmed by multivariate analysis. In conclusion, HBV reactivation in patients with NDMM constitutes a significant complication, correlating with reduced OS and PFS, and emerges as a potential adverse prognostic factor in the contemporary era of treatment.
format: application/pdf
format: text/html
creator: Weiran Lv
creator: Xiaojin Li
creator: Jingbo Xu
creator: Yun Wang
creator: Hanying Huang
creator: Fang Hu
creator: Yingying Cui
creator: Yuanbin Song
creator: Lezong Chen
creator: Bingyi Wu
creator: Yang Liang
subject: Virology
subject: Gastroenterology and Hepatology
subject: Hematology
subject: Infectious Diseases
subject: Oncology
description: doi:10.7717/peerj.18475
publisher: PeerJ Inc.
rights: © 2024 Lv et al.
abstract: Studies on the prognosis of hepatitis B virus (HBV) reactivation following modern therapies for newly diagnosed MM (NDMM) are lacking. In this retrospective study, we aimed to assess the incidence, risk factors and prognosis of HBV reactivation in NDMM. A total of 33 of 355 patients with NDMM and HBV reactivation were included in this study. Multivariable analysis showed that hepatitis B surface antigen-positivity, hepatitis B core antibody-positivity, bortezomib-containing regimens, autologous stem cell transplantation, and gain of 1q21 were identified as independent risk factors of HBV reactivation in NDMM patients. The NDMM patients with HBV reactivation had poorer 3-year overall survival (OS) and progression-free survival (PFS) than did those without HBV reactivation, as confirmed by multivariate analysis. In conclusion, HBV reactivation in patients with NDMM constitutes a significant complication, correlating with reduced OS and PFS, and emerges as a potential adverse prognostic factor in the contemporary era of treatment.
bibliographic citation: Lv et al. (2024) Prognosis of hepatitis B virus reactivation in newly diagnosed multiple myeloma in modern era therapy: a retrospective study. PeerJ 12:e18475 https://doi.org/10.7717/peerj.18475
date submitted: 2024-05-18
date accepted: 2024-10-16
issued: 2024-11-01
is part of: PeerJ
license: https://creativecommons.org/licenses/by-nc/4.0
author list: Agent
  family name: Lv
  given name: Weiran
author list: Agent
  family name: Li
  given name: Xiaojin
author list: Agent
  family name: Xu
  given name: Jingbo
author list: Agent
  family name: Wang
  given name: Yun
author list: Agent
  family name: Huang
  given name: Hanying
author list: Agent
  family name: Hu
  given name: Fang
author list: Agent
  family name: Cui
  given name: Yingying
author list: Agent
  family name: Song
  given name: Yuanbin
author list: Agent
  family name: Chen
  given name: Lezong
author list: Agent
  family name: Wu
  given name: Bingyi
author list: Agent
  family name: Liang
  given name: Yang
doi: 10.7717/peerj.18475
volume: 12
issn: 2167-8359
publication name: PeerJ
publication date: 2024-11-01
copyright: © 2024 Lv et al.
attribution name: Lv et al.

A novel nomogram to predict the risk of requiring mechanical ventilation in patients with sepsis within 48 hours of admission: a retrospective analysis
type: item
type: AcademicArticle
description: Objective To establish a model that can predict the risk of requiring mechanical ventilation within 48 h after admission in patients with sepsis. Methods Data for patients with sepsis admitted to Dongyang People’s Hospital from October 2011 to October 2023 were collected and divided into a modeling group and a validation group. Independent risk factors in the modeling group were analyzed, and a corresponding predictive nomogram was established. The model was evaluated for discriminative power (the area under the curve of the receiver operating characteristic curve, AUC), calibration degree (Hosmer-Lemeshow test), and clinical benefit (decision curve analysis, DCA). Models based on the Sequential Organ Failure Assessment (SOFA) scores, the National Early Warning Score (NEWS) scores and multiple machine learning methods were also established. Results The independent factors related to the risk of requiring mechanical ventilation in patients with sepsis within 48 h included lactic acid, pro-brain natriuretic peptide (PRO-BNP), and albumin levels, as well as prothrombin time, the presence of lung infection, and D-dimer levels. The AUC values of nomogram model in the modeling group and validation group were 0.820 and 0.837, respectively. The nomogram model had a good fit and clinical value. The AUC values of the models constructed using SOFA scores and NEWSs were significantly lower than those of the nomogram (P < 0.01). The AUC value of the integrated machine-learning model for the validation group was 0.849, comparable to that of the nomogram model (P = 0.791). Conclusion The established nomogram could effectively predict the risk of requiring mechanical ventilation within 48 h of admission by patients with sepsis. Thus, the model can be used for the treatment and management of sepsis.
format: application/pdf
format: text/html
creator: Bin Wang
creator: Jian Ouyang
creator: Rui Xing
creator: Jiyuan Jiang
creator: Manzhen Ying
subject: Emergency and Critical Care
subject: Infectious Diseases
subject: Internal Medicine
subject: Respiratory Medicine
description: doi:10.7717/peerj.18500
publisher: PeerJ Inc.
rights: © 2024 Wang et al.
abstract: Objective To establish a model that can predict the risk of requiring mechanical ventilation within 48 h after admission in patients with sepsis. Methods Data for patients with sepsis admitted to Dongyang People’s Hospital from October 2011 to October 2023 were collected and divided into a modeling group and a validation group. Independent risk factors in the modeling group were analyzed, and a corresponding predictive nomogram was established. The model was evaluated for discriminative power (the area under the curve of the receiver operating characteristic curve, AUC), calibration degree (Hosmer-Lemeshow test), and clinical benefit (decision curve analysis, DCA). Models based on the Sequential Organ Failure Assessment (SOFA) scores, the National Early Warning Score (NEWS) scores and multiple machine learning methods were also established. Results The independent factors related to the risk of requiring mechanical ventilation in patients with sepsis within 48 h included lactic acid, pro-brain natriuretic peptide (PRO-BNP), and albumin levels, as well as prothrombin time, the presence of lung infection, and D-dimer levels. The AUC values of nomogram model in the modeling group and validation group were 0.820 and 0.837, respectively. The nomogram model had a good fit and clinical value. The AUC values of the models constructed using SOFA scores and NEWSs were significantly lower than those of the nomogram (P < 0.01). The AUC value of the integrated machine-learning model for the validation group was 0.849, comparable to that of the nomogram model (P = 0.791). Conclusion The established nomogram could effectively predict the risk of requiring mechanical ventilation within 48 h of admission by patients with sepsis. Thus, the model can be used for the treatment and management of sepsis.
bibliographic citation: Wang et al. (2024) A novel nomogram to predict the risk of requiring mechanical ventilation in patients with sepsis within 48 hours of admission: a retrospective analysis. PeerJ 12:e18500 https://doi.org/10.7717/peerj.18500
date submitted: 2024-08-09
date accepted: 2024-10-18
issued: 2024-11-01
is part of: PeerJ
license: https://creativecommons.org/licenses/by/4.0/
author list: Agent
  family name: Wang
  given name: Bin
author list: Agent
  family name: Ouyang
  given name: Jian
author list: Agent
  family name: Xing
  given name: Rui
author list: Agent
  family name: Jiang
  given name: Jiyuan
author list: Agent
  family name: Ying
  given name: Manzhen
doi: 10.7717/peerj.18500
volume: 12
issn: 2167-8359
publication name: PeerJ
publication date: 2024-11-01
copyright: © 2024 Wang et al.
attribution name: Wang et al.

The behavior patterns of giraffes (Giraffa camelopardalis) housed across 18 US zoos
type: item
type: AcademicArticle
description: Interpreting animal behavior in the context of welfare can be inherently challenging given the limited behavior data available for many species housed in zoos. Describing common behavior patterns may help animal managers by providing additional background when assessing the individuals in their care. Although valuable, these efforts require a large, collaborative approach and have, consequently, been rare. Here, we share the behavior patterns of zoo-housed giraffes, an iconic and commonly housed megafauna in zoos. Behavior data were evaluated for 66 giraffes living across 18 AZA-accredited zoos using the ZooMonitor Community platform. Data were recorded during 10-minute observation sessions. Observations were conducted during daytime hours over the course of approximately one year at each zoo (mean total observed time per individual = 23.2 hr). The most common behaviors observed were feeding/ foraging behaviors, which accounted for 38.6% of the mean visible time budget across giraffes. Time spent in these behaviors varied by individual and ranged from 14.3% to 69.3% of visible time. Stereotypic behaviors occurred in all study individuals, with oral stereotypic behaviors being most common. Although prevalent, stereotypic behaviors varied considerably across giraffes, with some individuals exhibiting these behaviors only on a few occasions to an individual that exhibited these behaviors once every few minutes. This study provides a robust evaluation of giraffe behavior across zoos to present a picture of their common behavior patterns in managed care. We hope these multi-institutional behavior patterns can provide perspective to aid animal managers in evaluating giraffes in their care.
format: application/pdf
format: text/html
creator: Jason D. Wark
creator: Katherine A. Cronin
subject: Animal Behavior
subject: Zoology
description: doi:10.7717/peerj.18164
publisher: PeerJ Inc.
rights: © 2024 Wark and Cronin
abstract: Interpreting animal behavior in the context of welfare can be inherently challenging given the limited behavior data available for many species housed in zoos. Describing common behavior patterns may help animal managers by providing additional background when assessing the individuals in their care. Although valuable, these efforts require a large, collaborative approach and have, consequently, been rare. Here, we share the behavior patterns of zoo-housed giraffes, an iconic and commonly housed megafauna in zoos. Behavior data were evaluated for 66 giraffes living across 18 AZA-accredited zoos using the ZooMonitor Community platform. Data were recorded during 10-minute observation sessions. Observations were conducted during daytime hours over the course of approximately one year at each zoo (mean total observed time per individual = 23.2 hr). The most common behaviors observed were feeding/ foraging behaviors, which accounted for 38.6% of the mean visible time budget across giraffes. Time spent in these behaviors varied by individual and ranged from 14.3% to 69.3% of visible time. Stereotypic behaviors occurred in all study individuals, with oral stereotypic behaviors being most common. Although prevalent, stereotypic behaviors varied considerably across giraffes, with some individuals exhibiting these behaviors only on a few occasions to an individual that exhibited these behaviors once every few minutes. This study provides a robust evaluation of giraffe behavior across zoos to present a picture of their common behavior patterns in managed care. We hope these multi-institutional behavior patterns can provide perspective to aid animal managers in evaluating giraffes in their care.
bibliographic citation: Wark et al. (2024) The behavior patterns of giraffes (Giraffa camelopardalis) housed across 18 US zoos. PeerJ 12:e18164 https://doi.org/10.7717/peerj.18164
date submitted: 2024-07-02
date accepted: 2024-09-02
issued: 2024-10-31
is part of: PeerJ
license: https://creativecommons.org/licenses/by/4.0/
author list: Agent
  family name: Wark
  given name: Jason D.
author list: Agent
  family name: Cronin
  given name: Katherine A.
doi: 10.7717/peerj.18164
volume: 12
issn: 2167-8359
publication name: PeerJ
publication date: 2024-10-31
copyright: ©2024 Wark and Cronin
attribution name: Wark and Cronin

PeerJ
type: Periodical
uri: PeerJ
issn: 2167-8359