Optimizing chlorine dioxide treatment for enhanced post-harvest storage quality of Toona Sinensis
This study investigated the impact of chlorine dioxide (ClO2) treatment on the storage quality of Toona sinensis after harvesting. Toona sinensis samples treated with different concentrations (0.2, 0.4, 0.8, 1.6 mg/L) of chlorine dioxide and stored at (4 ± 1) °C, with sampling test every 2nd day. The changes in sensory, nutritional, and chlorine dioxide residues of T. sinensis were checked regularly. Results revealed that moderate (0.4~0.8 mg/L) chlorine dioxide concentrations maintained sensory quality, inhibited T. sinensis weight loss, slowed flavonoid and vitamin C content depletion, reduced nitrite content, and enhanced superoxide dismutase activity. The chlorine dioxide resides in T. sinensis were below the national standards (<2.0 mg/kg; GB 5009.244-2016). Overall, 0.4~0.8 mg/L chlorine dioxide treatment showed optimal effects on T. sinensis, providing a scientific basis for extended storage and preservation of T. sinensis.
Zeng et al. (2024) Optimizing chlorine dioxide treatment for enhanced post-harvest storage quality of Toona Sinensis. PeerJ 12:e18346 https://doi.org/10.7717/peerj.18346
Long-term alfalfa planting mediates the coupling of soil water and organic carbon storage in a semi-arid area of the Loess Plateau, China
The key to restoring arid and semi-arid ecosystems is maintaining soil water and organic carbon contents. Alfalfa (Medicago sativa L.) is a high-yield perennial forage crop and performs ecological functions as a drought-resistance leguminous herb. It has been widely planted for reconstruction of degraded soils in the Loess Plateau in northwestern China, but long-term planting may affect soil carbon–water coupling and lead to crop yield reduction. To maximize the benefits of reconstructed grassland, this study explored the couplings of soil water, organic carbon, and alfalfa productivity along a reconstruction chronosequence in a semi-arid area of the Loess Plateau. Space-for-time substitution approach was used to select different-aged stands of reconstructed grassland (1, 5, 7, 10, 15, 20, 30 years old). Alfalfa above-ground biomass (AGB), soil water storage (SWS), organic carbon storage (SOCS), and carbon–water coupling coordination degree (D) were measured in the 0–100 cm soil profile. Alfalfa AGB reached a peak in the 7th year, and the degradation began in the 10th year. Both SWS and SOCS varied nonlinearly with stand age. The greatest loss of SWS occurred in the 15th year (80–100 cm depth), whereas the largest increase of SOCS occurred in the 30th year (0–20 cm depth). There was a negative feedback relationship between AGB and SWS over the 30-year study period (Pearson r = −0.835, P = 0.098). AGB and SOCS initially showed a trade-off within the first 10 years (Pearson r = −0.7431, P = 0.2569), in contrast to their positive feedback in the 20–30th years (Pearson r = 0.9978, P = 0.0421). A decoupling between SWS and SOCS (D < 0.6) was observed after 12 years of alfalfa planting. For agricultural production, a greater supply of water and organic fertilizer is required from the 7th year of alfalfa planting, and reseeding may be needed around the 10th year to prolong the life of alfalfa community. Alfalfa should be planted for no more than 12 consecutive years in the study area for ecological protection.
Ma et al. (2024) Long-term alfalfa planting mediates the coupling of soil water and organic carbon storage in a semi-arid area of the Loess Plateau, China. PeerJ 12:e18373 https://doi.org/10.7717/peerj.18373
Citizen science enhances understanding of sea turtle distribution in the Gulf of California
Citizen science is a valuable tool for addressing spatial distribution gaps in endangered species, especially in data-limited regions. Given the logistical and financial challenges of studying migratory species, this cost-effective approach contributes to strategic conservation planning. The Bay of La Paz, located in Baja California Sur, México, is considered an ecologically important region within the larger Gulf of California. Due to its rich biodiversity and abundant natural resources, the region serves as a hub for ecotourism and fishing, affording diverse opportunities for community-based scientific initiatives. This paper examines the value of citizen science contributions from three diverse community groups (i.e., coastal residents, SCUBA divers, and artisanal fishers) to help scientists obtain information on sea turtle distribution in the Bay of La Paz. Our findings represent the first records of loggerhead turtles (Caretta caretta) in the bay based on ten citizen science reports that include live and dead turtles (either observed swimming, as bycatch, or stranded), providing new information on an endangered pelagic species identified in a coastal bay. Although the sample size is small, our diversity of sources and sighting types highlight the value of collaborative citizen science initiatives in complementing traditional research methods.
Rousso et al. (2024) Citizen science enhances understanding of sea turtle distribution in the Gulf of California. PeerJ 12:e18203 https://doi.org/10.7717/peerj.18203
Effect of habitual physical activity on motor performance and prefrontal cortex activity during implicit motor learning
Background Acute bouts of exercise have been shown to improve motor learning. However, whether these benefits can be observed from habitual physical activity (PA) levels remains unclear and has important implications around PA guidelines to promote motor learning across the lifespan. This study investigated the effect of habitual PA levels on brain activity within the dorsolateral prefrontal cortex (DLPFC) during procedural motor skill acquisition. Methods Twenty-six right-handed healthy young adults had physical activity levels quantified by calculating the metabolic equivalent of task (METs) in minutes per week, derived from the International Physical Activity Questionnaire (IPAQ). Functional near-infrared spectroscopy (fNIRS) over the DLPFC was recorded to measure neural activation during a serial reaction time task (SRTT). Behavioural indicators of procedural motor skill acquisition were quantified as reaction time and accuracy of correct trials during the SRTT. DLPFC activation was characterised as task-related changes in oxyhaemoglobin (∆[HbO2]). Results Findings showed that higher PA levels were associated with improvements in reaction time during procedural motor skill acquisition (p = 0.03). However, no significant effects of PA levels on accuracy or ∆[HbO2] during procedural motor skill acquisition were observed. These findings show that while habitual PA may promote motor performance in young adults, this is not reflected by changes in the DLPFC area of the brain.
Tan et al. (2024) Effect of habitual physical activity on motor performance and prefrontal cortex activity during implicit motor learning. PeerJ 12:e18217 https://doi.org/10.7717/peerj.18217
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