Microalgal and activated sludge processing for biodegradation of textile dyes.

The textile industry contributes substantially to water pollution. To investigate bioremediation of dye-containing wastewater, the decolorization and biotransformation of three textile azo dyes, Red HE8B, Reactive Green 27, and Acid Blue 29, were considered using an integrated remediation approach involving the microalga Chlamydomonas mexicana and activated sludge (ACS). At a 5 mg L-1 dye concentration, using C. mexicana and ACS alone, decolorization percentages of 39%-64% and 52%-54%, respectively, were obtained. In comparison, decolorization percentages of 75%-79% were obtained using a consortium of C. mexicana and ACS. The same trend was observed for the decolorization of dyes at higher concentrations, but the potential for decolorization was low. The toxic azo dyes adversely affect the growth of microalgae and at high concentration 50 mg L-1 the growth rate inhibited to 50-60% as compared to the control. The natural textile wastewater was also treated with the same pattern and got promising results of decolorization (90%). Moreover, the removal of BOD (82%), COD (72%), TN (64%), and TP (63%) was observed with the consortium. The HPLC and GC-MS confirm dye biotransformation, revealing the emergence of new peaks and the generation of multiple metabolites with more superficial structures, such as N-hydroxy-aniline, naphthalene-1-ol, and sodium hydroxy naphthalene. This analysis demonstrates the potential of the C. mexicana and ACS consortium for efficient, eco-friendly bioremediation of textile azo dyes.

Comparative analysis of salivary cortisol measurements using different assay methods in relation to serum-free cortisol measurement.

Objectives: Salivary cortisol reflects the biologically active form of serum cortisol, offering a noninvasive evaluation method for the diurnal rhythm of the hypothalamic-pituitary-adrenal (HPA) axis. While liquid chromatography-tandem mass spectrometry (LC-MS/MS) is known for its specificity, immunoassays (IA) are commonly used because of their simplicity. This study aimed to assess the performance of salivary cortisol measurement using both IA and LC-MS/MS in comparison to serum-free cortisol measurement. Methods: Assay results for 188 saliva and 94 serum samples from 47 participants were analyzed. Salivary samples collected at different time points were analyzed using IA and LC-MS/MS. Serum samples were analyzed for cortisol, cortisol-binding globulin, and free cortisol. The statistical analyses included correlations and method comparisons. Results: The diurnal salivary cortisol profiles exhibited a comparable circadian rhythm pattern; however, the concentrations measured using IA were consistently higher than those measured using LC-MS/MS. The correlation analysis revealed robust associations among salivary cortisol (IA), salivary cortisol (LC-MS/MS), and serum-free cortisol levels (LC-MS/MS). However, the method comparison revealed a systematic bias between IA and LC-MS/MS in salivary cortisol measurement. Conclusions: This study contributes to the ongoing debate on assay techniques by affirming the suitability of IA and LC-MS/MS for salivary cortisol measurement to assess dynamic changes in HPA axis activity. The identified systematic bias emphasizes the importance of selecting methods based on specific research or clinical requirements.

Machine learning-based analysis for prediction of surgical necrotizing enterocolitis in very low birth weight infants using perinatal factors: a nationwide cohort study.

Early prediction of surgical necrotizing enterocolitis (sNEC) in preterm infants is important. However, owing to the complexity of the disease, identifying infants with NEC at a high risk for surgical intervention is difficult. We developed a machine learning (ML) algorithm to predict sNEC using perinatal factors obtained from the national cohort registry of very low birth weight (VLBW) infants. Data were collected from the medical records of 16,385 VLBW infants registered in the Korean Neonatal Network (KNN). Infants who underwent surgical intervention were identified with sNEC, and infants who received medical treatment, with medical NEC (mNEC). We used 38 variables, including maternal, prenatal, and postnatal factors that were obtained within 1 week of birth, for training. A total of 1085 patients had NEC (654 with sNEC and 431 with mNEC). VLBW infants showed a higher incidence of sNEC at a lower gestational age (GA) (p < 0.001). Our proposed ensemble model showed an area under the receiver operating characteristic curve of 0.721 for sNEC prediction.    Conclusion: Proposed ensemble model may help predict which infants with NEC are likely to develop sNEC. Through early prediction and prompt intervention, prognosis of sNEC may be improved. What is Known: • Machine learning (ML)-based techniques have been employed in NEC research for prediction, diagnosis, and prognosis, with promising outcomes. • While most studies have utilized abdominal radiographs and clinical manifestations of NEC as data sources, and have demonstrated their usefulness, they may prove weak in terms of early prediction. What is New: • We analyzed the perinatal factors of VLBW infants acquired within 7 days of birth and used ML-based analysis to identify which infants with NEC are vulnerable to clinical deterioration and at high risk for surgical intervention using nationwide cohort data.

Deeper insight into ferroptosis: association with Alzheimer's, Parkinson's disease, and brain tumors and their possible treatment by nanomaterials induced ferroptosis.

Ferroptosis is an emerging and novel type of iron-dependent programmed cell death which is mainly caused by the excessive deposition of free intracellular iron in the brain cells. This deposited free iron exerts a ferroptosis pathway, resulting in lipid peroxidation (LiPr). There are mainly three ferroptosis pathways viz. iron metabolism-mediated cysteine/glutamate, and LiPr-mediated. Iron is required by the brain as a redox metal for several physiological activities. Due to the iron homeostasis balance disruption, the brain gets adversely affected which further causes neurodegenerative diseases (NDDs) like Parkinson's and Alzheimer's disease, strokes, and brain tumors like glioblastoma (GBS), and glioma. Nanotechnology has played an important role in the prevention and treatment of these NDDs. A synergistic effect of nanomaterials and ferroptosis could prove to be an effective and efficient approach in the field of nanomedicine. In the current review, the authors have highlighted all the latest research in the field of ferroptosis, specifically emphasizing on the role of major molecular key players and various mechanisms involved in the ferroptosis pathway. Moreover, here the authors have also addressed the correlation of ferroptosis with the pathophysiology of NDDs and theragnostic effect of ferroptosis and nanomaterials for the prevention and treatment of NDDs.

Use of biogenic silver nanoparticles on the cathode to improve bioelectricity production in microbial fuel cells.

To date, research on microbial fuel cells (MFCs) has. focused on the production of cost-effective, high-performance electrodes and catalysts. The present study focuses on the synthesis of silver nanoparticles (AgNPs) by Pseudomonas sp. and evaluates their role as an oxygen reduction reaction (ORR) catalyst in an MFC. Biogenic AgNPs were synthesized from Pseudomonas aeruginosa via facile hydrothermal synthesis. The physiochemical characterization of the biogenic AgNPs was conducted via scanning electron microscopy (SEM), X-ray diffraction (XRD), and UV-visible spectrum analysis. SEM micrographs showed a spherical cluster of AgNPs of 20-100 nm in size. The oxygen reduction reaction (ORR) ability of the biogenic AgNPs was studied using cyclic voltammetry (CV). The oxygen reduction peaks were observed at 0.43 V, 0.42 V, 0.410 V, and 0.39 V. Different concentrations of biogenic AgNPs (0.25-1.0 mg/cm2) were used as ORR catalysts at the cathode in the MFC. A steady increase in the power production was observed with increasing concentrations of biogenic AgNPs. Biogenic AgNPs loaded with 1.0 mg/cm2 exhibited the highest power density (PDmax) of 4.70 W/m3, which was approximately 26.30% higher than the PDmax of the sample loaded with 0.25 mg/cm2. The highest COD removal and Coulombic efficiency (CE) were also observed in biogenic AgNPs loaded with 1.0 mg/cm2 (83.8% and 11.7%, respectively). However, the opposite trend was observed in the internal resistance of the MFC. The lowest internal resistance was observed in a 1.0 mg/cm2 loading (87 Ω), which is attributed to the high oxygen reduction kinetics at the surface of the cathode by the biogenic AgNPs. The results of this study conclude that biogenic AgNPs are a cost-effective, high-performance ORR catalyst in MFCs.

Bacillus subtilis ER-08, a multifunctional plant growth-promoting rhizobacterium, promotes the growth of fenugreek (Trigonella foenum-graecum L.) plants under salt and drought stress.

Introduction: Sustainable agriculture and meeting the world's food needs face considerable obstacles from abiotic stresses such as soil salinity and drought. This critical issue was addressed by our current study, which sought to uncover multi-trait bioinoculants from hostile ecosystems that could help mitigate salinity and drought stresses at the same time. Methods: The Bacillus subtilis ER-08 (BST) strain was isolated from the halotolerant plant Fagonia cretica which was collected from the Little Rann of Kachchh, India. Various biochemical and molecular approaches were applied for the detailed characterization of the BST isolate. Results and discussion: The BST isolate demonstrated notable plant growth-promoting qualities. Fenugreek seed biopriming was performed using the BST isolate. The effect of BST seed treatment on fenugreek developmental indices as well as abiotic alleviation was examined under greenhouse conditions. The BST produced 83.7 g ml-1 gibberellins (GA3) and 176.1 g ml-1 indole-3 acetic acid. Moreover, hydrogen cyanide, siderophore, exopolysaccharides (EPS), ammonia, cellulase, protease, pectinase, and chitinase were also produced by the BST strain. Interestingly, 52% of Fusarium oxysporum mycelial growth was suppressed by the BST isolate under in vitro conditions. Furthermore, BST isolates functioned well under several abiotic stress conditions, for instance, salinity (4 and 6 ds m-1), pH (5, 7, and 9), drought (PEG6000 at 10%, 20%, and 30%), and temperature (25°C, 35°C, 37°C, and 55°C). This study indicates that the BST strain might serve as an effective bio-inoculant for minimizing the detrimental effects of abiotic stresses.

Psychobiotics and fecal microbial transplantation for autism and attention-deficit/hyperactivity disorder: microbiome modulation and therapeutic mechanisms.

Dysbiosis of the gut microbiome is thought to be the developmental origins of the host's health and disease through the microbiota-gut-brain (MGB) axis: such as immune-mediated, metabolic, neurodegenerative, and neurodevelopmental diseases. Autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) are common neurodevelopmental disorders, and growing evidence indicates the contribution of the gut microbiome changes and imbalances to these conditions, pointing to the importance of considering the MGB axis in their treatment. This review summarizes the general knowledge of gut microbial colonization and development in early life and its role in the pathogenesis of ASD/ADHD, highlighting a promising therapeutic approach for ASD/ADHD through modulation of the gut microbiome using psychobiotics (probiotics that positively affect neurological function and can be applied for the treatment of psychiatric diseases) and fecal microbial transplantation (FMT).

Learning-Based Longitudinal Prediction Models for Mortality Risk in Very-Low-Birth-Weight Infants: A Nationwide Cohort Study.

INTRODUCTION: Prediction models assessing the mortality of very-low-birth-weight (VLBW) infants were confined to models using only pre- and perinatal variables. We aimed to construct a prediction model comprising multifactorial clinical events with data obtainable at various time points. METHODS: We included 15,790 (including 2,045 in-hospital deaths) VLBW infants born between 2013 and 2020 who were enrolled in the Korean Neonatal Network, a nationwide registry. In total, 53 prenatal and postnatal variables were sequentially added into the three discrete models stratified by hospital days: (1) within 24 h (TL-1d), (2) from day 2 to day 7 after birth (TL-7d), (3) from day 8 after birth to discharge from the neonatal intensive care unit (TL-dc). Each model predicted the mortality of VLBW infants within the affected period. Multilayer perception (MLP)-based network analysis was used for modeling, and ensemble analysis with traditional machine learning (ML) analysis was additionally applied. The performance of models was compared using the area under the receiver operating characteristic curve (AUROC) values. The Shapley method was applied to reveal the contribution of each variable. RESULTS: Overall, the in-hospital mortality was 13.0% (1.2% in TL-1d, 4.1% in TL-7d, and 7.7% in TL-dc). Our MLP-based mortality prediction model combined with ML ensemble analysis had AUROC values of 0.932 (TL-1d), 0.973 (TL-7d), and 0.950 (TL-dc), respectively, outperforming traditional ML analysis in each timeline. Birth weight and gestational age were constant and significant risk factors, whereas the impact of the other variables varied. CONCLUSION: The findings of the study suggest that our MLP-based models could be applied in predicting in-hospital mortality for high-risk VLBW infants. We highlight that mortality prediction should be customized according to the timing of occurrence.

A Review of Approaches to the Metallic and Non-Metallic Synthesis of Benzimidazole (BnZ) and Their Derivatives for Biological Efficacy.

Heterocyclic compounds are significant lead drug candidates based on their various structure-activity relationships (SAR), and their use in pharmaceutics is constantly developing. Benzimidazole (BnZ) is synthesized by a condensation reaction between benzene and imidazole. The BnZ structure consists of two nitrogen atoms embedded in a five-membered imide ring which is fused with a benzene ring. This review examines the conventional and green synthesis of metallic and non-metallic BnZ and their derivatives, which have several potential SARs, along with a wide range of pharmacological properties, including anti-cancer, anti-inflammatory, anti-microbial, anti-tubercular, and anti-protozoal properties. These compounds have been proven by pharmacological investigations to be efficient against different strains of microbes. Therefore, in this review, the structural variations of BnZ are listed along with various applications, predominantly related to their biological activities.

Assessment of the Antioxidant and Anti-inflammatory Properties of Aqueous Extract of Rosa sempervirens Leaves.

The antioxidant and anti-inflammatory properties of an aqueous natural extract obtained from Rosa sempervirens leaves were assessed. The ability of the extract to scavenge DPPH, •OH, and H2O2 radicals, chelate ferrous ions, reduce ferric ions, and protect ß-carotene-linoleic acid in emulsion from peroxidation was investigated in vitro. Furthermore, the anti-inflammatory activity of the extract was evaluated by measuring the stability of the membrane of human red blood cells against different hypotonic concentrations of NaCl and heat, as well as by inhibiting the denaturation of albumin. A high total phenolic content (278.38± 11.07 mg GAE/g) and flavonoid content (34.22± 0.12 mg QE /g) were found in the extract. The extract exhibited significant scavenging activity of DPPH (IC50 6.201 ± 0.126 µg/ ml), •OH (IC50 = 894.57 ± 21.18 µg/ml), and H2O2 (IC50= 107±09.58 µg/ml) radicals, and good antioxidant activity by chelating ferrous ions (IC50 = 2499.086 ± 28.267µg/ml), reducing ferric ions (IC50=141.33±2.34 µg/ml), exhibiting total antioxidant capacity (IC50 465.65 ± 9.71 µg/ml), and protecting ß-carotene-linoleic acid against peroxidation (I% = 90.05 ± 1.65% at 1000µg/ml). R. sempervirens displayed anti-inflammatory activity in aqueous extract by inhibiting heat-induced albumin denaturation and stabilizing the membrane of human red blood cells. It was suggested from the results that R. sempervirens aqueous extract could help prevent oxidative and inflammatory processes due to its good antioxidant and anti-inflammatory properties.

Two-stage learning-based prediction of bronchopulmonary dysplasia in very low birth weight infants: a nationwide cohort study.

Introduction: The aim of this study is to develop an enhanced machine learning-based prediction models for bronchopulmonary dysplasia (BPD) and its severity through a two-stage approach integrated with the duration of respiratory support (RSd) using prenatal and early postnatal variables from a nationwide very low birth weight (VLBW) infant cohort. Methods: We included 16,384 VLBW infants admitted to the neonatal intensive care unit (NICU) of the Korean Neonatal Network (KNN), a nationwide VLBW infant registry (2013-2020). Overall, 45 prenatal and early perinatal clinical variables were selected. A multilayer perceptron (MLP)-based network analysis, which was recently introduced to predict diseases in preterm infants, was used for modeling and a stepwise approach. Additionally, we applied a complementary MLP network and established new BPD prediction models (PMbpd). The performances of the models were compared using the area under the receiver operating characteristic curve (AUROC) values. The Shapley method was used to determine the contribution of each variable. Results: We included 11,177 VLBW infants (3,724 without BPD (BPD 0), 3,383 with mild BPD (BPD 1), 1,375 with moderate BPD (BPD 2), and 2,695 with severe BPD (BPD 3) cases). Compared to conventional machine learning (ML) models, our PMbpd and two-stage PMbpd with RSd (TS-PMbpd) model outperformed both binary (0 vs. 1,2,3; 0,1 vs. 2,3; 0,1,2 vs. 3) and each severity (0 vs. 1 vs. 2 vs. 3) prediction (AUROC = 0.895 and 0.897, 0.824 and 0.825, 0.828 and 0.823, 0.783, and 0.786, respectively). GA, birth weight, and patent ductus arteriosus (PDA) treatment were significant variables for the occurrence of BPD. Birth weight, low blood pressure, and intraventricular hemorrhage were significant for BPD ≥2, birth weight, low blood pressure, and PDA ligation for BPD ≥3. GA, birth weight, and pulmonary hypertension were the principal variables that predicted BPD severity in VLBW infants. Conclusions: We developed a new two-stage ML model reflecting crucial BPD indicators (RSd) and found significant clinical variables for the early prediction of BPD and its severity with high predictive accuracy. Our model can be used as an adjunctive predictive model in the practical NICU field.

Gut microbiome profiling of neonates using Nanopore MinION and Illumina MiSeq sequencing.

This study aimed to evaluate the difference in gut microbiomes between preterm and term infants using third-generation long-read sequencing (Oxford Nanopore Technologies, ONT) compared with an established gold standard, Illumina (second-generation short-read sequencing). A total of 69 fecal samples from 51 term (T) and preterm (P) infants were collected at 7 and 28 days of life. Gut colonization profiling was performed by 16S rRNA gene sequencing using ONT. We used Illumina to validate and compare the patterns in 13 neonates. Using bioinformatic analysis, we identified features that differed between P and T. Both T1 and P1 microbiomes were dominated by Firmicutes (Staphylococcus and Enterococcus), whereas sequentially showed dominant transitions to Lactobacillus (p < 0.001) and Streptococcus in T2 (p = 0.001), and pathogenic bacteria (Klebsiella) in P2 (p = 0.001). The abundance of beneficial bacteria (Bifidobacterium and Lactobacillus) increased in T2 (p = 0.026 and p < 0.001, respectively). These assignments were correlated with the abundance at the species-level. Bacterial α-diversity increased in T (p = 0.005) but not in P (p = 0.156), and P2 showed distinct ß-diversity clustering than T2 (p = 0.001). The ONT reliably identified pathogenic bacteria at the genus level, and taxonomic profiles were comparable to those identified by Illumina at the genus level. This study shows that ONT and Illumina are highly correlated. P and T had different microbiome profiles, and the α- and ß-diversity varied. ONT sequencing has potential for pathogen detection in neonates in clinical settings.

Dissemination of sulfonamide resistance genes in digester microbiome during anaerobic digestion of food waste leachate.

The preeminence of sulfonamide drug resistance genes in food waste (FW) and the increased utilization of high-strength organic FW in anaerobic digestion (AD) to enhance methane production have raised severe public health concerns in wastewater treatment plants worldwide. In this regard, the dissemination patterns of different sulfonamide resistance genes (sul1 and sul2) and their impact on the digester core microbiota during AD of FW leachate (FWL) were evaluated. The presence of various sulfonamide antibiotics (SAs) in FWL digesters improved the final methane yield by 37 % during AD compared with FWL digesters without SAs. Microbial population shifts towards hydrolytic, acidogenic, and acetogenic bacteria in the phyla Actinobacteriota, Bacteroidota, Chloroflexi, Firmicutes, Proteobacteria, and Synergistota occurred due to SA induced substrate digestion and absorption through active transport; butanoate, propanoate, and pyruvate metabolism; glycolysis; gluconeogenesis; the citrate cycle; and pentose phosphate pathway. The initial dominance of Methanosaeta (89-96 %) declined to 47-53 % as AD progressed and shifted towards Methanosarcina (40 %) in digesters with the highest SA concentrations at the end of AD. Dissemination of sul1 depended on class 1 integron gene (intl1)-based horizontal gene transfer to pathogenic members of Chloroflexi, Firmicutes, and Patescibacteria, whereas sul2 was transmitted to Synergistota independent of intl1. Low susceptibility and ability to utilize SAs during methanogenesis shielded methanogenic archaea against selection pressure, thus preventing them from interacting with sul or intl1 genes, thereby minimizing the risk of antibiotic resistance development. The observed emergence of cationic antimicrobial peptide, vancomycin, and ß-lactam resistance in the core microbiota during AD of FWL in the presence of SAs suggests that multidrug resistance caused by bacterial transformation could lead to an increase in the environmental resistome through wastewater sludge treatment.

Cystic Periventricular Leukomalacia Worsens Developmental Outcomes of Very-Low-Birth Weight Infants with Intraventricular Hemorrhage-A Nationwide Cohort Study.

Cystic periventricular leukomalacia (cPVL) is a major brain injury involving periventricular white matter that leads to neurodevelopmental impairment in very-low-birth weight (VLBW) infants. We investigated the neurodevelopmental outcomes (motor, cognition, visual, and hearing) of 5734 VLBW infants born between 2013 and 2019 and enrolled in the Korean Neonatal Network. Cranial ultrasound results were stratified by the presence of cPVL and severity of intraventricular hemorrhage (IVH) (no, low-grade [I/II], high-grade [III]). Neurodevelopmental impairment was evaluated using cerebral palsy for motor and Bayley Scales of Infant Development for cognition. cPVL was associated with motor, cognitive, and visual impairments in those without IVH and with low-grade IVH in pairwise comparisons (Cochran−Mantel−Haenszel p < 0.001). Conversely, cPVL was non-significantly correlated with cognitive impairment in high-grade IVH. In regression models adjusted for neonatal variables, isolated cPVL was strongly associated with motor (22.04; 11.39−42.63) and cognitive (3.10; 1.54−6.22) impairments. This study underlines the overall considerable significance of cPVL on NDI with divergent impacts depending on the severity of IVH and developmental indices.

Early screening tool for developmental delay in infancy: Quantified assessment of movement asymmetry using IR-UWB radar.

In the untact COVID-19 era, the feasibility of a noncontact, impulse-radio ultrawideband (IR-UWB) radar sensor has important medical implications. Premature birth is a major risk factor for brain injury and developmental delay; therefore, early intervention is crucial for potentially achieving better developmental outcomes. Early detection and screening tests in infancy are limited to the quantification of differences between normal and spastic movements. This study investigated the quantified asymmetry in the general movements of an infant with hydrocephalus and proposes IR-UWB radar as a novel, early screening tool for developmental delay. To support this state-of-the-art technology, data from actigraphy and video camcorder recordings were adopted simultaneously to compare relevant time series as the infant grew. The data from the three different methods were highly concordant; specifically, the ρz values comparing radar and actigraphy, which served as the reference for measuring movements, showed excellent agreement, with values of 0.66 on the left and 0.56 on the right. The total amount of movement measured by radar over time increased overall; movements were almost dominant on the left at first (75.2% of total movements), but following shunt surgery, the frequency of movement on both sides was similar (54.8% of total movements). As the hydrocephalus improved, the lateralization of movement on radar began to coincide with the clinical features. These results support the important complementary role of this radar system in predicting motor disorders very early in life.

Noncontact assessment for fatigue based on heart rate variability using IR-UWB radar.

Physical fatigue can be assessed using heart rate variability (HRV). We measured HRV at rest and in a fatigued state using impulse-radio ultra wideband (IR-UWB) radar in a noncontact fashion and compared the measurements with those obtained using electrocardiography (ECG) to assess the reliability and validity of the radar measurements. HRV was measured in 15 subjects using radar and ECG simultaneously before (rest for 10 min before exercise) and after a 20-min exercise session (fatigue level 1 for 0-9 min; fatigue level 2 for 10-19 min; recovery for ≥ 20 min after exercise). HRV was analysed in the frequency domain, including the low-frequency component (LF), high-frequency component (HF) and LF/HF ratio. The LF/HF ratio measured using radar highly agreed with that measured using ECG during rest (ICC = 0.807), fatigue-1 (ICC = 0.712), fatigue-2 (ICC = 0.741) and recovery (ICC = 0.764) in analyses using intraclass correlation coefficients (ICCs). The change pattern in the LH/HF ratios during the experiment was similar between radar and ECG. The subject's body fat percentage was linearly associated with the time to recovery from physical fatigue (R2 = 0.96, p < 0.001). Our results demonstrated that fatigue and rest states can be distinguished accurately based on HRV measurements using IR-UWB radar in a noncontact fashion.

Development of artificial neural networks for early prediction of intestinal perforation in preterm infants.

Intestinal perforation (IP) in preterm infants is a life-threatening condition that may result in serious complications and increased mortality. Early Prediction of IP in infants is important, but challenging due to its multifactorial and complex nature of the disease. Thus, there are no reliable tools to predict IP in infants. In this study, we developed new machine learning (ML) models for predicting IP in very low birth weight (VLBW) infants and compared their performance to that of classic ML methods. We developed artificial neural networks (ANNs) using VLBW infant data from a nationwide cohort and prospective web-based registry. The new ANN models, which outperformed all other classic ML methods, showed an area under the receiver operating characteristic curve (AUROC) of 0.8832 for predicting IP associated with necrotizing enterocolitis (NEC-IP) and 0.8797 for spontaneous IP (SIP). We tested these algorithms using patient data from our institution, which were not included in the training dataset, and obtained an AUROC of 1.0000 for NEC-IP and 0.9364 for SIP. NEC-IP and SIP in VLBW infants can be predicted at an excellent performance level with these newly developed ML models. https://github.com/kdhRick2222/Early-Prediction-of-Intestinal-Perforation-in-Preterm-Infants .

Effects of Ponderal Index on Neonatal Mortality and Morbidities in Extremely Premature Infants.

BACKGROUND: To evaluate how intrauterine stress affects extremely premature infants in terms of intrauterine growth restriction. We hypothesized that extremely premature infants with mildly-low ponderal index (MPI) would have better neonatal outcomes. METHODS: We selected 2,721 subjects of 23 to 28 weeks of gestation between 2013 and 2015 from Korean Neonatal Network database. They were divided into 4 groups based on ponderal index (PI) percentile; PI ≤ 3rd as severely-low PI (SPI, n = 82), 3rd < PI ≤ 10th as MPI (n = 190), 10th < PI ≤ 90th as adequate PI (API, n = 2,179), and PI > 90th as high PI (HPI, n = 270). RESULTS: The mortality in MPI and API groups was comparable (16.3% vs. 16.9%). It was significantly lower than that in the SPI and HPI groups (30.5% and 24.9%, respectively; P = 0.001). The MPI and API groups had better neonatal morbidities compared with the SPI and/or HPI groups, while the MPI group (8.2%) showed a lower incidence of severe intraventricular hemorrhage (IVH) than the other groups (SPI, 21.3%; API, 15.0%; HPI, 19.7%, respectively; P = 0.004). The MPI group had a trend of a bottom in neonatal mortality and morbidities in extremely premature infants. CONCLUSION: The MPI and API groups had lower mortality, massive pulmonary hemorrhage, severe bronchopulmonary dysplasia or death, pulmonary hypertension and neonatal seizure rates than the SPI and/or HPI groups, while the MPI group showed a lower incidence of severe IVH than the other groups. We speculate that the lower incidence of neonatal morbidities and mortality in the MPI group indicating mild intrauterine stress might accelerate fetal maturation resulting in better outcomes in extremely premature infants.

Preclinical trial of noncontact anthropometric measurement using IR-UWB radar.

Anthropometric profiles are important indices for assessing medical conditions, including malnutrition, obesity, and growth disorders. Noncontact methods for estimating those parameters could have considerable value in many practical situations, such as the assessment of young, uncooperative infants or children and the prevention of infectious disease transmission. The purpose of this study was to investigate the feasibility of obtaining noncontact anthropometric measurements using the impulse-radio ultrawideband (IR-UWB) radar sensor technique. A total of 45 healthy adults were enrolled, and a convolutional neural network (CNN) algorithm was implemented to analyze data extracted from IR-UWB radar. The differences (root-mean-square error, RMSE) between values from the radar and bioelectrical impedance analysis (BIA) as a reference in the measurement of height, weight, and body mass index (BMI) were 2.78, 5.31, and 2.25, respectively; predicted data from the radar highly agreed with those from the BIA. The intraclass correlation coefficients (ICCs) were 0.93, 0.94, and 0.83. In conclusion, IR-UWB radar can provide accurate estimates of anthropometric parameters in a noncontact manner; this study is the first to support the radar sensor as an applicable method in clinical situations.

Impact of Preterm Birth on Neurodevelopmental Disorders in South Korea: A Nationwide Population-Based Study.

Neurodevelopmental disorder (NDD) in preterm infants has become of great interest. We aimed to investigate the impact of preterm birth on the proportion of NDD using nationwide data provided by the Korean National Health Insurance Service. We included 4894 extremely preterm or extremely low-birth-weight (EP/ELBW; <28 weeks of gestation or birth weight < 1000 g) infants, 70,583 other preterm or low-birth-weight (OP/LBW; 28−36 weeks of gestation or birth weight < 2500 g) infants, and 264,057 full-term infants born between 2008 and 2015. We observed their neurodevelopment until 6 years of age or until the year 2019, whichever occurred first. Diagnoses of NDDs were based on the World Health Organization's International Classification of Diseases 10th revision. An association between preterm birth and NDD was assessed using a multivariable logistic regression model. There was a stepwise increase in the risk of overall NDD with increasing degree of prematurity, from OP/LBW (adjusted odds ratio 4.46; 95% confidence interval 4.34−4.58), to EP/ELBW (16.15; 15.21−17.15). The EP/ELBW group was strongly associated with developmental delay (21.47; 20.05−22.99), cerebral palsy (88.11; 79.89−97.19), and autism spectrum disorder (11.64; 10.37−13.06). Preterm birth considerably increased the risk of NDD by the degree of prematurity.