Genomic complexity and clinical significance of the RCCX locus
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.
Shiryagin et al. (2024) Genomic complexity and clinical significance of the RCCX locus. PeerJ 12:e18243 https://doi.org/10.7717/peerj.18243
Dietary reference intake for military operations: a scoping review
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/day is recommended for individuals who sweat a lot or are not accustomed to hot environments. Conclusions The DRI in Australia, the UK, USA, and NATO consider the physical activity and operating environment of military personnel, differing from those of the civilian population in terms of (1) energy requirements, (2) protein, (3) macronutrient energy distribution, and (4) sodium.
Mizushima et al. (2024) Dietary reference intake for military operations: a scoping review. PeerJ 12:e18353 https://doi.org/10.7717/peerj.18353
Common laboratory results-based artificial intelligence analysis achieves accurate classification of plasma cell dyscrasias
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 myeloma, demonstrating sensitivity and specificity above 90% across all subtypes. Conclusions AI models based on routine laboratory results significantly enhance the precision of diagnosing and classifying plasma cell dyscrasias, presenting a promising avenue for early detection and individualized treatment strategies.
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
Application of composite reference intervals in the diagnosis of subclinical hypothyroidism in the elderly: a retrospective study
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.
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
Quality improvement bundles to decrease hypothermia in very low/extremely low birth weight infants at birth: a systematic review and meta-analysis
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.
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
Applying stacking ensemble method to predict chronic kidney disease progression in Chinese population based on laboratory information system: a retrospective study
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 management and treatment.
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
Potential molecular mechanisms of ETV6-RUNX1-positive B progenitor cell cluster in acute lymphoblastic leukemia revealed by single-cell RNA sequencing
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. The key regulator of the B progenitor cell is E2F1, which promotes the migration and invasion ability of the cell line HAP1. The major ligand-receptor pairs that mediate the communication of B progenitor cell cluster 5 with cytotoxic NK/T cells or naive T cells included FAM3C−CLEC2D, CD47−SIRPG, HLAE−KLRC2, and CD47−KLRC2. HLAE−KLRC1 and TGFB1−(TGFBR1+TGFBR2). Conclusion This study outlined the immune cell landscape of ETV6-RUNX1 ALL and identified chromosome 6p amplification in B progenitor cells, described the major B progenitor cell cluster driving cell cycle progression and its potential regulatory mechanisms on NK cells and T cells, providing cellular and molecular insights into ETV6-RUNX1 ALL.
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
Prognosis of hepatitis B virus reactivation in newly diagnosed multiple myeloma in modern era therapy: a retrospective study
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.
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
A novel nomogram to predict the risk of requiring mechanical ventilation in patients with sepsis within 48 hours of admission: a retrospective analysis
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.
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
The behavior patterns of giraffes (Giraffa camelopardalis) housed across 18 US zoos
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.
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