Preparation and characterization of intelligent and active bi-layer film based on carrageenan/pectin for monitoring freshness of salmon.

This work aimed to develop and characterize a novel bi-layer film (BIF) for monitoring the freshness of salmon. The indicator layer consists of carrageenan (Car), pectin (PEC) and purple sweet potato anthocyanin (PSPA), and the antibacterial layer consists of Car and magnolol (Mag). The results showed that the Car/Mag2 had the optimal water resistance: the static water contact angle of 80.36 ± 0.92 °, moisture content of 31.38 ± 0.86 %, swelling degree of 92.96 ± 0.46 %, and water solubility of 40.08 ± 1.17 %, and showed excellent antibacterial properties against E. coli and S. aureus with antibacterial rate of 86.13 % ± 0.10 % and 97.53 % ± 0.02 %, respectively. Then BIFs with different PSPA concentration were tested. The morphology, mechanical and water vapor properties (WVP) of the BIFs were studied, and its application in salmon preservation was evaluated. The mechanical properties and WVP test results showed that the BIF0.2 had the optimal Tensile strength (TS) and WVP values. The BIFs showed distinguishable color changes between the pH ranges of 3-10. The shelf life of salmon packaged by BIF0.2 was prolonged by 2 days. Moreover, the BIF0.2 was able to effectively monitor salmon freshness. In conclusion, the BIF has great potential for monitoring salmon meat freshness.

Multimodal data-driven, vertical visualization prediction model for early prediction of atherosclerotic cardiovascular disease in patients with new-onset hypertension.

BACKGROUND: : Hypertension is an important contributing factor to atherosclerotic cardiovascular disease (ASCVD), and multiple risk factors, many of which are implicated in metabolic disorders, contribute to the cause of hypertension. Despite the promise of multimodal data-driven prediction model, no such prediction model was available to predict the risk of ASCVD in Chinese individuals with new-onset hypertension and no history of ASCVD. METHODS: : A total of 514 patients were randomly allocated to training and verification cohorts (ratio, 7 : 3). We employed Boruta feature selection and conducted multivariate Cox regression analyses to identify variables associated with ASCVD in these patients, which were subsequently utilized for constructing the predictive model. The performance of prediction model was assessed in terms of discriminatory power (C-index), calibration (calibration curves), and clinical utility [decision curve analysis (DCA)]. RESULTS: : This model was derived from four clinical variables: 24-h SBP coefficient of variation, 24-h DBP coefficient of variation, urea nitrogen and the triglyceride-glucose (TyG) index. Bootstrapping with 500 iterations was conducted to adjust the C-indexes were C-index = 0.731, 95% confidence interval (CI) 0.620-0.794 and C-index: 0.799, 95% CI 0.677-0.892 in the training and verification cohorts, respectively. Calibration plots with 500 bootstrapping iterations exhibited a strong correlation between the predicted and observed occurrences of ASCVD in both the training and verification cohorts. DCA analysis confirmed the clinical utility of this prediction model. The constructed nomogram demonstrated significant additional prognostic utility for ASCVD, as evidenced by improvements in the C-index, net reclassification improvement, integrated discrimination improvement, and DCA compared with the overall ASCVD risk assessment. CONCLUSION: The developed longitudinal prediction model based on multimodal data can effectively predict ASCVD risk in individuals with an initial diagnosis of hypertension. TRIAL REGISTRATION: : The trial was registered in the Chinese Clinical Trial Registry (ChiCTR2300074392).

MpoxNet: dual-branch deep residual squeeze and excitation monkeypox classification network with attention mechanism.

While the world struggles to recover from the devastation wrought by the widespread spread of COVID-19, monkeypox virus has emerged as a new global pandemic threat. In this paper, a high precision and lightweight classification network MpoxNet based on ConvNext is proposed to meet the need of fast and safe detection of monkeypox classification. In this method, a two-branch depth-separable convolution residual Squeeze and Excitation module is designed. This design aims to extract more feature information with two branches, and greatly reduces the number of parameters in the model by using depth-separable convolution. In addition, our method introduces a convolutional attention module to enhance the extraction of key features within the receptive field. The experimental results show that MpoxNet has achieved remarkable results in monkeypox disease classification, the accuracy rate is 95.28%, the precision rate is 96.40%, the recall rate is 93.00%, and the F1-Score is 95.80%. This is significantly better than the current mainstream classification model. It is worth noting that the FLOPS and the number of parameters of MpoxNet are only 30.68% and 31.87% of those of ConvNext-Tiny, indicating that the model has a small computational burden and model complexity while efficient performance.

Association between Ureaplasma colonization and bronchopulmonary dysplasia defined by different criteria in very low birth weight infants: A retrospective cohort study.

OBJECTIVE: To study the association between Ureaplasma colonization and bronchopulmonary dysplasia (BPD) with different definitions in very low birth weight (VLBW) infants. METHODS: A retrospective cohort study was performed with VLBW infants admitted from January 2019 to October 2021. Neonates with a positive respiratory tract Ureaplasma culture were included in the study group. Control group infants, matched for gestational age (±1 week), birth weight (±100 g), and birth year, had a negative respiratory tract Ureaplasma culture during the same period. The primary outcomes included the incidence and severity of BPD, defined by various criteria. RESULTS: The study included 302 neonates (151 in the study group and 151 in the control group). After adjusting for confounders, Ureaplasma colonization was not associated with BPD as defined by the National Institutes of Health (NIH) in 2001 (adjusted odds ratio [aOR]: 0.820, 95% confidence interval [CI]: 0.362-1.860, p = .635). However, it was associated with BPD as defined by the NIH in 2018 (aOR: 2.490, 95% CI: 1.128-5.497, p = .024) and the Neonatal Research Network (NRN) in 2019 (aOR: 2.352, 95% CI: 1.077-5.134, p = .032). Additionally, VLBW infants with Ureaplasma colonization had a higher risk of moderate-severe BPD according to the NIH 2001 (aOR: 2.352, 95% CI: 1.077-5.134, p = .032), NIH 2018 (aOR: 6.339, 95% CI: 1.686-23.836, p = .006), and NRN 2019 definitions (aOR: 3.542, 95% CI: 1.267-9.904, p = .016). CONCLUSIONS: Ureaplasma colonization is not associated with BPD by the NIH 2001 definition, but is associated with an increased incidence by the NIH 2018 or NRN 2019 definitions.

Analysis of Clinical Outcome and Prognosis of C-reactive Protein Combined with Albumin in Patients with Acute Myocardial Infarction.

Objective: This study aims to assess the combined predictive value of C-reactive protein (CRP) and albumin (ALB) for major adverse cardiovascular events (MACE) post-percutaneous coronary intervention (PCI) in patients with acute myocardial infarction (AMI). Methods: We analyzed data from continuously enrolled AMI patients who underwent emergency PCI at the First Affiliated Hospital of Xinjiang Medical University over six years, employing logistic regression to derive a predictive equation for in-hospital mortality and out-of-hospital MACE events. Primary endpoints: In-hospital death and out-of-hospital major adverse cardiovascular events. The patients were followed up for 1, 3, 6, and 12 months after discharge. The average follow-up time was 41 months. Results: Among the 601 patients studied, we observed 16 in-hospital deaths and 131 out-of-hospital MACE events. Multivariate logistic regression analysis showed that the independent predictors of out-of-hospital MACE events were age (OR=1.067, 95% CI 1.013-1.124, P = .028), C-reactive protein (OR=1.012, 95% CI 1.000-1.025, P = .045) and albumin (OR=0.874, 95% CI 0.785-0.973, P = .014). Our multivariate logistic regression analysis identified age, CRP, and albumin as independent predictors, with the combined equation yielding an ROC curve area of 0.85, effectively stratifying patients into high-risk and low-risk groups. Subsequent follow-up results validated this risk stratification approach. Conclusion: The study underscores the efficacy of combining CRP and albumin levels as a predictive measure for in-hospital death and out-of-hospital MACE events in AMI patients post-PCI.

Research on the innovative application of Shen Embroidery cultural heritage based on convolutional neural network.

In order to protect intangible cultural heritage and promote outstanding cultural works, this article introduces innovative research on Shen Embroidery using convolutional neural networks. The dataset of Shen Embroidery was preprocessed to augment the data required for experimentation. Moreover, the approach of transfer learning was introduced to fine-tune the recognition network. Specifically, Spatial Pyramid Pooling (SPP) is employed by replacing the avg pool in the MobileNet V1 network, achieving the fusion of local and global features. The experimental results showed that the improved MobileNet V1 achieved a recognition accuracy of 98.45%, which was 2.3% higher than the baseline MobileNet V1 network. The experiments demonstrated that the improved convolutional neural network can efficiently recognize Shen Embroidery and provide technical support for the intelligent development of intangible cultural heritage.

The effect of in-hospital breast milk intake on the gut microbiota of preterm infants.

OBJECTIVE: The aim of this study was to explore the effect of in-hospital breast milk intake on the development of early gut microbiota in preterm infants in two dimensions: longitudinal over time and cross-sectional between groups. METHODS: Researchers collected preterm infants' general data baseline characteristics, recorded their daily breast milk intake, probiotics, and antibiotics use, and collected their stool specimens at 1st week, 2 nd week, 3rd week and 4th week after birth. The researchers analyzed the effect of breast milk on gut microbiota of preterm infants by bioinformatics methods of intra-group longitudinal variation of gut microbiota structure and diversity in preterm infants and cross-sectional differences between >70 % in-hospital breast milk intake (BM) group and ≤70 % (PF) group. RESULTS: A total of 60 preterm infants were included in this study, and a total of 213 stool specimens were retained. BM had statistically different Shannon and Simpson indices between the first and fourth week after admission (P < 0.05), both of them showed a lower diversity in the later week than in the previous week. The Shannon index and Simpson index of BM from week 3 onwards were statistically different from PF (P < 0.05), and the Shannon index and Simpson index of BM were lower than those of PF. Significantly statistical differences (P < 0.05) were found in the beta diversity of gut microbiota in preterm infants as time progressed, and both showed a lower beta diversity in the later week than in the preceding week. The dominant taxa of PF in the first postnatal week were Bifidobacterium animalis, etc., the dominant taxa of BM in the third postnatal week were Clostridium_sensu_stricto _1, etc. CONCLUSIONS: The development and evolution of gut microbiota in preterm infants' in-hospital period was a continuous, non-random process, and similar trends in species composition and changes in gut microbes emerged in preterm infants with different ratio of breast milk intake. In the NICU setting, alpha diversity was lower in preterm infants in the >70 % breast milk intake group than in the ≤70 % group when compared between groups at the same time, which may be related to delayed maturation of gut microbes and represents a more developmental gut time window.

An MRI brain tumor segmentation method based on improved U-Net.

In order to improve the segmentation effect of brain tumor images and address the issue of feature information loss during convolutional neural network (CNN) training, we present an MRI brain tumor segmentation method that leverages an enhanced U-Net architecture. First, the ResNet50 network was used as the backbone network of the improved U-Net, the deeper CNN can improve the feature extraction effect. Next, the Residual Module was enhanced by incorporating the Convolutional Block Attention Module (CBAM). To increase characterization capabilities, focus on important features and suppress unnecessary features. Finally, the cross-entropy loss function and the Dice similarity coefficient are mixed to compose the loss function of the network. To solve the class unbalance problem of the data and enhance the tumor area segmentation outcome. The method's segmentation performance was evaluated using the test set. In this test set, the enhanced U-Net achieved an average Intersection over Union (IoU) of 86.64% and a Dice evaluation score of 87.47%. These values were 3.13% and 2.06% higher, respectively, compared to the original U-Net and R-Unet models. Consequently, the proposed enhanced U-Net in this study significantly improves the brain tumor segmentation efficacy, offering valuable technical support for MRI diagnosis and treatment.

Large-Scale Synthesis of High Energy Thermal Battery Cathode Ni0.5Co0.5S2 by a Simple Sintering Technique.

With the synergies of multiple elements, bimetallic sulfides exhibit excellent performance as splendid electrode materials and effective catalysts. However, large-scale synthesis of high-performance single-phase multicomponent sulfides has always been a challenge. Based on thermodynamic calculations, the intermediate phases NiS2 and Co3S4 are devoted to the synthesis of single-phase Ni0.5Co0.5S2. Because the reaction from NiS2 and Co3S4 to Ni0.5Co0.5S2 goes through a lower energy, it thermodynamically contributes to achieving a single-phase structure. Thus, single-phase Ni0.5Co0.5S2 can be simply and quickly prepared by two-step sintering and successfully scalable for mass production. This technique can extend to the whole ingredients Ni1-xCoxS2. Ni0.5Co0.5S2 demonstrates excellent thermal stability and good conductivity. It delivers a specific capacity of 671 mAh·g-1 and a specific energy of 1173 Wh·kg-1 when applied to a thermal battery cathode, which are increased by 18.6% and 25.0%, respectively, compared to pristine NiS2 (566 mAh·g-1) and CoS2 (537 mAh·g-1). This work proposes an innovative sintering method, which is applicable for cost-efficient and large-scale synthesis of single-phase multicomponent sulfides.

The redox requirement and regulation during cell proliferation.

The intracellular metabolic network comprises a variety of reduction-oxidation (redox) reactions that occur in a temporally and spatially distinct manner. In order to coordinate these redox processes, mammalian cells utilize a collection of electron-carrying molecules common to many redox reactions, including NAD, NADP, coenzyme Q (CoQ), and glutathione (GSH). This review considers the metabolic basis of redox regulation in the context of cell proliferation by analyzing how cells acquire and utilize electron carriers to maintain directional carbon flux, sustain reductive biosynthesis, and support antioxidant defense. Elucidating the redox requirement during cell proliferation can advance the understanding of human diseases such as cancer, and reveal effective therapeutic opportunities in the clinic.

SARS-CoV-2 infection causes dopaminergic neuron senescence.

COVID-19 patients commonly present with signs of central nervous system and/or peripheral nervous system dysfunction. Here, we show that midbrain dopamine (DA) neurons derived from human pluripotent stem cells (hPSCs) are selectively susceptible and permissive to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. SARS-CoV-2 infection of DA neurons triggers an inflammatory and cellular senescence response. High-throughput screening in hPSC-derived DA neurons identified several FDA-approved drugs that can rescue the cellular senescence phenotype by preventing SARS-CoV-2 infection. We also identified the inflammatory and cellular senescence signature and low levels of SARS-CoV-2 transcripts in human substantia nigra tissue of COVID-19 patients. Furthermore, we observed reduced numbers of neuromelanin+ and tyrosine-hydroxylase (TH)+ DA neurons and fibers in a cohort of severe COVID-19 patients. Our findings demonstrate that hPSC-derived DA neurons are susceptible to SARS-CoV-2, identify candidate neuroprotective drugs for COVID-19 patients, and suggest the need for careful, long-term monitoring of neurological problems in COVID-19 patients.

Diagnostic status and epidemiological characteristics of community-acquired bacterial meningitis in children from 2019 to 2020: a multicenter retrospective study.

Community-acquired bacterial meningitis (CABM) is the main cause of morbidity and mortality in children. The epidemiology of CABM is regional and highly dynamic. To clarify the diagnostic status and epidemiological characteristics of children with CABM in this region, and pay attention to the disease burden, so as to provide evidence for the prevention and treatment of CABM. By retrospective case analysis, the clinical data of 918 CABM cases in children aged 0-14 years in Zhejiang Province from January, 2019 to December, 2020 were collected. The etiological diagnosis rate of CABM in children was 23.1%, the annual incidence rate 4.42-6.15/100,000, the annual mortality rate 0.06-0.09/100,000,the cure and improvement rate 94.4%, and the case fatality rate 1.4%. The total incidence of neuroimaging abnormalities was 20.6%. The median length of stay for CABM children was 20(16) days, with an average cost of 21,531(24,835) yuan. In addition, the incidence rate was decreased with age. Escherichia coli(E.coli) and group B Streptococcus agalactiae(GBS) were the principal pathogens in CABM infant<3 months(43.3%, 34.1%), and Streptococcus pneumoniae(S. pneumoniae) was the most common pathogen in children ≥ 3 months(33.9%). In conclusion, the annual incidence and mortality of CABM in children aged 0-14 years in Zhejiang Province are at intermediate and low level. The distribution of CABM incidence and pathogen spectrum are different in age; the incidence of abnormal neuroimaging is high; and the economic burden is heavy.

Detecting Abnormality of Battery Lifetime from First-Cycle Data Using Few-Shot Learning.

The service life of large battery packs can be significantly influenced by only one or two abnormal cells with faster aging rates. However, the early-stage identification of lifetime abnormality is challenging due to the low abnormal rate and imperceptible initial performance deviations. This work proposes a lifetime abnormality detection method for batteries based on few-shot learning and using only the first-cycle aging data. Verified with the largest known dataset with 215 commercial lithium-ion batteries, the method can identify all abnormal batteries, with a false alarm rate of only 3.8%. It is also found that any capacity and resistance-based approach can easily fail to screen out a large proportion of the abnormal batteries, which should be given enough attention. This work highlights the opportunities to diagnose lifetime abnormalities via "big data" analysis, without requiring additional experimental effort or battery sensors, thereby leading to extended battery life, increased cost-benefit, and improved environmental friendliness.

Dynamically Cross-Linked, Self-Healable, and Stretchable All-Hydrogel Supercapacitor with Extraordinary Energy Density and Real-Time Pressure Sensing.

Wearable electronics with flexible, integrated, and self-powered multi-functions are becoming increasingly attractive, but their basic energy storage units are challenged in simultaneously high energy density, self-healing, and real-time sensing capability. To achieve this, a fully flexible and omni-healable all-hydrogel, that is dynamically crosslinked PVA@PANI hydrogel, is rationally designed and constructed via aniline/DMSO-emulsion-templated in situ freezing-polymerization strategy. The PVA@PANI sheet, not only possesses a honeycombed porous conductive mesh configuration with superior flexibility that provides numerous channels for unimpeded ions/electron transport and maximizes the utilization efficiency of pseudocapacitive PANI, but also can conform to complicated body surface, enabling effective detection and discrimination of body movements. As a consequence, the fabricated flexible PVA@PANI sheet electrode demonstrates an unprecedented specific capacitance (936.8 F g-1 ) and the assembled symmetric flexible all-solid-state supercapacitor delivers an extraordinary energy density of 40.98 Wh kg-1 , outperforming the previously highest-reported values of stretchable PVA@PANI hydrogel-based supercapacitors. What is more, such a flexible supercapacitor electrode enables precisely monitoring the full-range human activities in real-time, and fulfilling a quick response and excellent self-recovery. These outstanding flexible sensing and energy storage performances render this emerging PVA@PANI hydrogel highly promising for the next-generation wearable self-powered sensing electronics.

Multifunctional Regioisomeric Passivation Strategy for Fabricating Self-Driving, High Detectivity All-Inorganic Perovskite Photodetectors.

The fluorination of the aromatic multifunctional Lewis base passivation strategy has been demonstrated recently as an effective approach to markedly enhance the performance of perovskite photovoltaic devices. However, the regulation mechanisms of the passivation efficiency by varying the functional group position of fluorine (F) in the regioisomers have received little attention and inadequate research. Herein, a pair of bifluorine-substituted aminobenzoic acid regioisomers [3-amino-2,6-difluorobenzoic acid (13-FABA) and 4-amino-3,5-difluorobenzoic acid (14-FABA)] were employed to investigate the passivation effects of Lewis bases dependent on behaviors of the ortho/meta-substituted position of fluorine. The density functional theory calculation on electron cloud density, interaction energy, and the basicity of Lewis bases combined with experimental evidence reveal that the ortho-effect induced by fluorine substitution weakens the passivating effect of 13-FABA Lewis base and induces its molecular propensity to form internal salts, accelerating the degradation and deterioration of the device performance. Conversely, 14-FABA with meta-connected fluorine atoms exhibit superior efficacy in suppressing defects and enhancing hydrophobicity. Eventually, the 14-FABA-modified photodetectors (PDs) achieved a high detectivity of 1.69 × 1013 Jones, the comparatively lower dark current density of 2.2 × 10-10 A/cm2 among all-inorganic perovskite PD systems. Our work has not only clarified the fundamental mechanisms of the F-substituted position effects of Lewis base on suppressing defects but also provided a promising passivation strategy for perovskite films via designing the regioisomeric atoms in a multifunctional Lewis base molecule.

Development and accuracy of artificial intelligence-generated prediction of facial changes in orthodontic treatment: a scoping review. / äººå·¥æ™ºèƒ½é¢„æµ‹æ­£ç•¸é¢éƒ¨å˜åŒ–çš„ç ”ç©¶è¿›å±•å’Œå‡†ç¡®åº¦ï¼šæ¦‚å†µæ€§ç³»ç»Ÿç»¼è¿°.

Artificial intelligence (AI) has been utilized in soft-tissue analysis and prediction in orthodontic treatment planning, although its reliability has not been systematically assessed. This scoping review was conducted to outline the development of AI in terms of predicting soft-tissue changes after orthodontic treatment, as well as to comprehensively evaluate its prediction accuracy. Six electronic databases (PubMed, EBSCOhost, Web of Science, Embase, Cochrane Library, and Scopus) were searched up to March 14, 2023. Clinical studies investigating the performance of AI-based systems in predicting post-orthodontic soft-tissue alterations were included. The Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) and Joanna Briggs Institute (JBI) appraisal checklist for diagnostic test accuracy studies were applied to assess risk of bias, while the Grading of Recommendation, Assessment, Development, and Evaluation (GRADE) assessment was conducted to evaluate the certainty of outcomes. After screening 2500 studies, four non-randomized clinical trials were finally included for full-text evaluation. We found a low level of evidence indicating an estimated high overall accuracy of AI-generated prediction, whereas the lower lip and chin seemed to be the least predictable regions. Furthermore, the facial morphology simulated by AI via the fusion of multimodality images was considered to be reasonably true. Since all of the included studies that were not randomized clinical trials (non-RCTs) showed a moderate to high risk of bias, more well-designed clinical trials with sufficient sample size are needed in future work.

Functional interrogation of twenty type 2 diabetes-associated genes using isogenic human embryonic stem cell-derived ß-like cells.

Genetic studies have identified numerous loci associated with type 2 diabetes (T2D), but the functional roles of many loci remain unexplored. Here, we engineered isogenic knockout human embryonic stem cell lines for 20 genes associated with T2D risk. We examined the impacts of each knockout on ß cell differentiation, functions, and survival. We generated gene expression and chromatin accessibility profiles on ß cells derived from each knockout line. Analyses of T2D-association signals overlapping HNF4A-dependent ATAC peaks identified a likely causal variant at the FAIM2 T2D-association signal. Additionally, the integrative association analyses identified four genes (CP, RNASE1, PCSK1N, and GSTA2) associated with insulin production, and two genes (TAGLN3 and DHRS2) associated with ß cell sensitivity to lipotoxicity. Finally, we leveraged deep ATAC-seq read coverage to assess allele-specific imbalance at variants heterozygous in the parental line and identified a single likely functional variant at each of 23 T2D-association signals.

A predictive model for preterm infants with bronchopulmonary dysplasia based on ferroptosis-related lncRNAs.

BACKGROUND: Bronchopulmonary dysplasia (BPD) is the most challenging chronic lung disease for prematurity, with difficulties in early identification. Given lncRNA emerging as a novel biomarker and the regulator of ferroptosis, this study aims to develop a BPD predictive model based on ferroptosis-related lncRNAs (FRLs). METHODS: Using a rat model, we firstly explored mRNA levels of ferroptosis-related genes and ferrous iron accumulation in BPD rat lungs. Subsequently, a microarray dataset of umbilical cord tissue from 20 preterm infants with BPD and 34 preterm infants without BPD were downloaded from the Gene Expression Omnibus databases. Random forest and LASSO regression were conducted to identify diagnostic FRLs. Nomogram was used to construct a predictive BPD model based on the FRLs. Finally, umbilical cord blood lymphocytes of preterm infants born before 32 weeks gestational age and term infants were collected and determined the expression level of diagnostic FRLs by RT-qPCR. RESULTS: Increased iron accumulation and several dysregulated ferroptosis-associated genes were found in BPD rat lung tissues, indicating that ferroptosis was participating in the development of BPD. By exploring the microarray dataset of preterm infants with BPD, 6 FRLs, namely LINC00348, POT1-AS1, LINC01103, TTTY8, PACRG-AS1, LINC00691, were determined as diagnostic FRLs for modeling. The area under the receiver operator characteristic curve of the model was 0.932, showing good discrimination of BPD. In accordance with our analysis of microarray dataset, the mRNA levels of FRLs were significantly upregulated in umbilical cord blood lymphocytes from preterm infants who had high risk of BPD. CONCLUSION: The incorporation of FRLs into a predictive model offers a non-invasive approach to show promise in improving early detection and management of this challenging chronic lung disease in premature infant, enabling timely intervention and personalized treatment strategies.