Objective: To evaluate the current situation of thermal environment in primary and secondary school classrooms during winter, and to analyze students thermal comfort needs, so as to provide a basis for improving classroom thermal environment. Methods: From December 16 to 26, 2024, a stratified cluster random sampling method was used to select 90 classrooms from 15 primary and secondary schools in centralized/air conditioned heating areas(Liaoning Province, Tianjin City, Shanghai City) and naturally ventilated areas(Anhui Province and Jiangxi Province)for on site environmental measurement. A questionnaire survey was conducted among 743 students. The differences between groups using the χ 2 test were compared. Based on actual measurement data, a predicted mean vote prepared percentage of dissatisfied (PMV-PPD) model for centralized/air conditioned classrooms and an adaptive model for naturally ventilated classrooms were established, and the thermal neutral temperature and comfort interval were calculated. Results: The average outdoor temperature during on site measurement was 4.00(0.20，7.00)℃. In classrooms with centralized or air conditioned heating systems, the measured average temperature was (19.33±2.59)℃, with a thermal comfort range of 20.35-25.35 ℃ and a thermal neutral temperature of 22.85 ℃. And 13.92% of students reported feeling cold, while 80.80% felt comfortable. In classrooms with natural ventilation, the measured average temperature was (12.26±1.83)℃, with a thermal neutral temperature of 19.67 ℃ and a thermal comfort range of 16.17-23.17 ℃. About 48.33% of students reported feeling cold, and 49.81 % felt comfortable.The results of univariate analysis showed that there were statistically significant differences in shoe thickness, temperature sensation, relative humidity sensation and wind speed sensation between centralized/air conditioned heating areas ( χ 2= 7.01 , 31.47, 13.57, 13.80,all P <0.05). There were also statistically significant differences in school stage for primary and secondary school students, body mass index, classroom location for seat, temperature sensation, relative humidity sensation and wind speed sensation between naturally ventilated areas ( χ 2=42.13, 11.13, 11.04, 60.39, 29.27, 38.46,all P <0.05). Conclusions There are differences in thermal environment and students subjective thermal comfort in primary and secondary schools under different ventilation modes in winter. The temperature standards for heated classrooms should be revised, and differentiated environmental regulation strategies should be adopted based on different ventilation methods to improve students health and comfort levels.

Objective To evaluate the short-term efficacy of transcatheter aortic valve replacement (TAVR) using Venus A-Plus valve delivery system in patients with severe aortic stenosis. Methods The clinical data of patients undergoing TAVR in our hospital from August 2018 to March 2022 were collected and they were divided into a Venus A-Plus and a Venus A group according to the type of valve delivery system used. The perioperative data of the two groups were compared. Results A total of 121 patients were included, including 70 patients in the Venus A-Plus group [45 males and 25 females with a mean age of (67.81±6.62) years], and 51 patients in the Venus A group [33 males and 18 females with a mean age of (68.25±7.01) years]. All patients underwent TAVR, and the postoperative hemodynamic features (left ventricular ejection fraction, mean cross-valve pressure difference, peak flow rate) were significantly improved (P<0.05). There was no statistical difference in surgical success rate, all-cause mortality, conversion to thorax opening, valve-in-valve placement, moderate or above perivalvular regurgitation, new left bundle branch block or new right bundle branch block between the two groups (P>0.05). Conclusion TAVR with Venus A-Plus valve delivery system in patients with severe aortic stenosis shows comparable efficacy to the first-generation Venus A system and is satisfactory, safe and reliable.

ObjectiveThis paper aims to investigate and evaluate the staged efficacy and safety of the representative empirical prescription of the “Zhu Ke Jiao” theory， Qijia Rougan prescription， combined with entecavir in the treatment of hepatic fibrosis in chronic hepatitis B. MethodsA multicenter randomized controlled clinical study was conducted， and 101 patients diagnosed with chronic hepatitis B-related hepatic fibrosis （CHB-HF） who met the diagnosis and inclusion criteria were randomly assigned to an observation group （Qijia Rougan prescription + entecavir） and a control group （entecavir）. The treatment duration was 24 weeks. Liver stiffness measurement （LSM）， fibrosis-4 index （FIB-4）， portal vein diameter， hepatitis B serology， biochemical indicators， hepatic fibrosis markers in serum ［hyaluronic acid （HA）， laminin （LN）， procollagen Ⅲ peptide （PⅢP）， and type Ⅳ collagen （Ⅳ-C）］， and traditional Chinese medicine syndrome scores were used as efficacy evaluation indicators. Efficacy assessments and explorations of different staged subgroups of Qijia Rougan prescription were conducted according to LSM values based on the Metavir pathological staging standard. ResultsA total of 98 cases were included for statistical analysis， with 49 cases in the observation group and 49 in the control group. The general data of the patients in both groups were comparable. Compared with the same group before treatment， the observation group showed a significant reduction in LSM and FIB-4 （P<0.01）， as well as notable improvements in LN， Ⅳ-C， and various TCM syndrome scores （P<0.05， P<0.01）. When compared to the control group after treatment， the observation group demonstrated significant improvements in LSM， FIB-4， and various TCM syndrome score indicators （P<0.05， P<0.01）， indicating that the observation group performed better than the control group. Subgroup analysis of the regression of hepatic fibrosis stages showed that compared to the same group before treatment， the observation group had better improvement in regression of stages F2 and F3 （P<0.05）. When compared to the control group after treatment， the observation group exhibited superior improvement in regression of stage F3 （P<0.05）. No adverse events occurred in either group during the treatment period. ConclusionCompared with entecavir alone， the combination of Qijia Rougan prescription and entecavir significantly improves the degree of hepatic fibrosis and clinical TCM symptoms in patients. The optimal intervention period is primarily during stage F3， which is a potential “interception” point of the “Zhu Ke Jiao” theory.

ObjectiveTo address the challenges of unstructured classical Chinese expressions， nested entity relationships， and limited annotated data in famous traditional Chinese medicine（TCM） case records， this study proposes a joint relation extraction framework that integrates data augmentation and entity mapping， aiming to support the construction of TCM diagnostic knowledge graphs and clinical pattern mining. MethodsWe developed an annotation structure for entities and their relationships in TCM case texts and applied a data augmentation strategy by incorporating multiple ancient texts to expand the relation extraction dataset. A cascade binary tagging framework for relation triple extraction（CasRel） model for TCM semantics was designed， integrating a pre-trained bidirectional encoder representations from transformers（BERT） layer for classical TCM texts to enhance semantic representation， and using a head entity-relation-tail entity mapping mechanism to address entity nesting and relation overlapping issues. ResultsExperimental results showed that the CasRel model， combining data augmentation and entity mapping， outperformed the pipeline-based Bert-Radical-Lexicon（BRL）-bidirectional long short-term memory（BiLSTM）-Attention model. The overall precision， recall， and F₁-score across 12 relation types reached 65.73%， 64.03%， and 64.87%， which represent improvements of 14.26%， 7.98%， and 11.21% compared to the BRL-BiLSTM-Attention model， respectively. Notably， the F₁-score for tongue syndrome relations increased by 22.68%（69.32%）， and the prescription-syndrome relations performed the best with the F₁-score of 70.10%. ConclusionThe proposed framework significantly improves the semantic representation and complex dependencies in TCM texts， offering a reusable technical framework for structured mining of TCM case records. The constructed knowledge graph can support clinical syndrome differentiation， prescription optimization， and drug compatibility， providing a methodological reference for TCM artificial intelligence research.

Diabetic Nephropathy (DN) is the leading cause of end-stage renal disease (ESRD) globally, representing a major global health burden with limited disease-modifying therapies. Podocyte injury serves as the core pathological hallmark of DN, and conventional treatments targeting metabolic disorders or hemodynamic abnormalities fail to reverse the progressive decline of renal function. Accumulating evidence over the past decade has established that high glucose-induced podocyte pyroptosis—a pro-inflammatory form of programmed cell death—is a key driving force in DN progression. Its core molecular mechanism hinges on the activation of the TXNIP-NLRP3 inflammasome axis. Under sustained hyperglycemic conditions, excessive reactive oxygen species (ROS) are generated via pathways including the polyol pathway, advanced glycation end products (AGEs) accumulation, and mitochondrial dysfunction. Concurrently, methylglyoxal (a glucose metabolite) mediates post-translational modification of thioredoxin-interacting protein (TXNIP). These events collectively trigger the dissociation of TXNIP from thioredoxin (TRX), a redox-regulating protein. The free TXNIP then translocates to the mitochondria, where it binds to The NACHT, LRR, and PYD domain-containing protein 3 (NLRP3) and promotes inflammasome assembly. This assembly activates cysteine-aspartic acid protease 1 (caspase-1), which cleaves Gasdermin D (GSDMD) to generate its N-terminal fragment (GSDMD-NT). GSDMD-NT oligomerizes to form membrane pores, leading to podocyte swelling, rupture, and the release of pro-inflammatory cytokines interleukin-1β (IL-1β) and interleukin-18 (IL-18). These cytokines amplify local inflammatory responses, induce mesangial cell proliferation, and accelerate extracellular matrix deposition, ultimately exacerbating glomerulosclerosis. MCC950, a highly selective NLRP3 inhibitor, exerts its therapeutic effects through a multi-layered mechanism: it binds to the NACHT domain (NAIP, CIITA, HET-E and TP1 domain) of NLRP3 with nanomolar affinity, forming hydrogen bonds with key residues (Lys-42 and Asp-166) within the ATP-hydrolysis pocket to block ATP hydrolysis, thereby locking NLRP3 in an inactive conformational state. Additionally, MCC950 interferes with the protein-protein interaction between TXNIP and NLRP3 and regulates mitochondrial homeostasis to reduce ROS production. Preclinical studies have demonstrated that MCC950 dose-dependently reduces proteinuria, restores the expression of podocyte-specific markers (nephrin and Wilms tumor 1 protein, WT1), and alleviates podocyte foot process fusion and glomerulosclerosis in both streptozotocin (STZ)-induced type 1 diabetic models (characterized by absolute insulin deficiency) and db/db type 2 diabetic models (driven by insulin resistance). However, discrepancies in therapeutic outcomes exist across different models—some studies report exacerbated renal inflammation and fibrosis in STZ-induced models—which may stem from differences in disease pathogenesis, intervention timing (early vs. mid-stage disease), and dosing duration. Despite its promising preclinical efficacy, MCC950 faces significant translational challenges, including low oral bioavailability, insufficient podocyte targeting, potential hepatotoxicity, and drug-drug interactions with statins (commonly prescribed to diabetic patients for cardiovascular risk management). Furthermore, off-target effects such as the inhibition of carbonic anhydrase 2 have been identified, raising concerns about its safety profile. Nevertheless, its unique mechanism of action—directly blocking podocyte pyroptosis by targeting the TXNIP-NLRP3 axis—endows it with substantial translational value. In the future, strategies to overcome these barriers are expected to advance its clinical application: targeted delivery via nanocarriers (e.g., PLGA-PEG nanoparticles or nephrin antibody-conjugated systems) to enhance renal accumulation and podocyte specificity; precise patient stratification based on biomarkers such as serum IL-18 and renal TXNIP/NLRP3 expression to identify “inflammatory-phenotype” DN patients most likely to benefit; and combination therapy with sodium-glucose cotransporter 2 (SGLT2) inhibitors—whose metabolic benefits synergize with MCC950’s anti-inflammatory effects. These approaches hold great potential to break through clinical translation bottlenecks, offering a novel, precise anti-inflammatory treatment option for DN and addressing an unmet clinical need for therapies targeting the inflammatory underpinnings of the disease.

Diabetic Nephropathy (DN) is the leading cause of end-stage renal disease (ESRD) globally, representing a major global health burden with limited disease-modifying therapies. Podocyte injury serves as the core pathological hallmark of DN, and conventional treatments targeting metabolic disorders or hemodynamic abnormalities fail to reverse the progressive decline of renal function. Accumulating evidence over the past decade has established that high glucose-induced podocyte pyroptosis—a pro-inflammatory form of programmed cell death—is a key driving force in DN progression. Its core molecular mechanism hinges on the activation of the TXNIP-NLRP3 inflammasome axis. Under sustained hyperglycemic conditions, excessive reactive oxygen species (ROS) are generated via pathways including the polyol pathway, advanced glycation end products (AGEs) accumulation, and mitochondrial dysfunction. Concurrently, methylglyoxal (a glucose metabolite) mediates post-translational modification of thioredoxin-interacting protein (TXNIP). These events collectively trigger the dissociation of TXNIP from thioredoxin (TRX), a redox-regulating protein. The free TXNIP then translocates to the mitochondria, where it binds to The NACHT, LRR, and PYD domain-containing protein 3 (NLRP3) and promotes inflammasome assembly. This assembly activates cysteine-aspartic acid protease 1 (caspase-1), which cleaves Gasdermin D (GSDMD) to generate its N-terminal fragment (GSDMD-NT). GSDMD-NT oligomerizes to form membrane pores, leading to podocyte swelling, rupture, and the release of pro-inflammatory cytokines interleukin-1β (IL-1β) and interleukin-18 (IL-18). These cytokines amplify local inflammatory responses, induce mesangial cell proliferation, and accelerate extracellular matrix deposition, ultimately exacerbating glomerulosclerosis. MCC950, a highly selective NLRP3 inhibitor, exerts its therapeutic effects through a multi-layered mechanism: it binds to the NACHT domain (NAIP, CIITA, HET-E and TP1 domain) of NLRP3 with nanomolar affinity, forming hydrogen bonds with key residues (Lys-42 and Asp-166) within the ATP-hydrolysis pocket to block ATP hydrolysis, thereby locking NLRP3 in an inactive conformational state. Additionally, MCC950 interferes with the protein-protein interaction between TXNIP and NLRP3 and regulates mitochondrial homeostasis to reduce ROS production. Preclinical studies have demonstrated that MCC950 dose-dependently reduces proteinuria, restores the expression of podocyte-specific markers (nephrin and Wilms tumor 1 protein, WT1), and alleviates podocyte foot process fusion and glomerulosclerosis in both streptozotocin (STZ)-induced type 1 diabetic models (characterized by absolute insulin deficiency) and db/db type 2 diabetic models (driven by insulin resistance). However, discrepancies in therapeutic outcomes exist across different models—some studies report exacerbated renal inflammation and fibrosis in STZ-induced models—which may stem from differences in disease pathogenesis, intervention timing (early vs. mid-stage disease), and dosing duration. Despite its promising preclinical efficacy, MCC950 faces significant translational challenges, including low oral bioavailability, insufficient podocyte targeting, potential hepatotoxicity, and drug-drug interactions with statins (commonly prescribed to diabetic patients for cardiovascular risk management). Furthermore, off-target effects such as the inhibition of carbonic anhydrase 2 have been identified, raising concerns about its safety profile. Nevertheless, its unique mechanism of action—directly blocking podocyte pyroptosis by targeting the TXNIP-NLRP3 axis—endows it with substantial translational value. In the future, strategies to overcome these barriers are expected to advance its clinical application: targeted delivery via nanocarriers (e.g., PLGA-PEG nanoparticles or nephrin antibody-conjugated systems) to enhance renal accumulation and podocyte specificity; precise patient stratification based on biomarkers such as serum IL-18 and renal TXNIP/NLRP3 expression to identify “inflammatory-phenotype” DN patients most likely to benefit; and combination therapy with sodium-glucose cotransporter 2 (SGLT2) inhibitors—whose metabolic benefits synergize with MCC950’s anti-inflammatory effects. These approaches hold great potential to break through clinical translation bottlenecks, offering a novel, precise anti-inflammatory treatment option for DN and addressing an unmet clinical need for therapies targeting the inflammatory underpinnings of the disease.

ObjectiveThe rapid advancement of bioanalytical technologies has heightened the demand for high-throughput, label-free, and real-time cellular analysis. Electrochemical impedance spectroscopy (EIS) operating in the GHz frequency range (GHz-EIS) has emerged as a promising tool for characterizing cell suspensions due to its ability to rapidly and non-invasively capture the dielectric properties of cells and their microenvironment. Although GHz-EIS enables rapid and label-free detection of cell suspensions, significant challenges remain in interpreting GHz impedance data for complex samples, limiting the broader application of this technique in cellular research. To address these challenges, this study presents a novel method that integrates GHz-EIS with deep learning algorithms, aiming to improve the precision of cell suspension concentration identification and quantification. This method provides a more efficient and accurate solution for the analysis of GHz impedance data. MethodsThe proposed method comprises two key components: dielectric property dataset construction and backpropagation (BP) neural network modeling. Yeast cell suspensions at varying concentrations were prepared and separately introduced into a coaxial sensor for impedance measurement. The dielectric properties of these suspensions were extracted using a GHz-EIS dielectric property extraction method applied to the measured impedance data. A dielectric properties dataset incorporating concentration labels was subsequently established and divided into training and testing subsets. A BP neural network model employing specific activation functions (ReLU and Leaky ReLU) was then designed. The model was trained and tested using the constructed dataset, and optimal model parameters were obtained through this process. This BP neural network enables automated extraction and analytical processing of dielectric properties, facilitating precise recognition of cell suspension concentrations through data-driven training. ResultsThrough comparative analysis with conventional centrifugal methods, the recognized concentration values of cell suspensions showed high consistency, with relative errors consistently below 5%. Notably, high-concentration samples exhibited even smaller deviations, further validating the precision and reliability of the proposed methodology. To benchmark the recognition performance against different algorithms, two typical approaches—support vector machines (SVM) and K-nearest neighbor (KNN)—were selected for comparison. The proposed method demonstrated superior performance in quantifying cell concentrations. Specifically, the BP neural network achieved a mean absolute percentage error (MAPE) of 2.06% and an R² value of 0.997 across the entire concentration range, demonstrating both high predictive accuracy and excellent model fit. ConclusionThis study demonstrates that the proposed method enables accurate and rapid determination of unknown sample concentrations. By combining GHz-EIS with BP neural network algorithms, efficient identification of cell concentrations is achieved, laying the foundation for the development of a convenient online cell analysis platform and showing significant application prospects. Compared to typical recognition approaches, the proposed method exhibits superior capabilities in recognizing cell suspension concentrations. Furthermore, this methodology not only accelerates research in cell biology and precision medicine but also paves the way for future EIS biosensors capable of intelligent, adaptive analysis in dynamic biological research.

To investigate the mechanism of action of Syngnathus extract in treating knee osteoarthritis of rats, forty-eight male SD rats were randomly divided into the blank group, model group, positive drug group, as well as low-dose, medium-dose, and high-dose groups of Syngnathus extract. The rat model of knee osteoarthritis was constructed by intra-articular injection of sodium iodoacetate. After successful modeling, celecoxib(18 mg·kg~(-1)·d~(-1)) and Syngnathus extract(0.4, 0.8, and 1.6 g·kg~(-1)·d~(-1)) were given in different groups by gavage intervention for two weeks. Hematoxylin-eosin(HE) staining was used to observe the histopathological changes of cartilage in knee joints, and enzyme-linked immunosorbent assay(ELISA) was used to detect the expression level of inflammatory factors in serum. Real-time fluorescence quantitative PCR, Western blot, and immunohistochemistry were used to detect the levels of phosphatidylinositol 3-kinase(PI3K)/protein kinase B(Akt)/mammalian target protein of rapamycin(mTOR) pathway-related mRNA and protein expression. The results showed that, comparied with the blank group, the cartilage surface of the knee joints of rats in the model group was uneven, with disorganized levels and defective cartilage tissue. The serum levels of interleukin-1β(IL-1β), interleukin-6(IL-6), and tumor necrosis factor-α(TNF-α) and the mRNA levels of PI3K, Akt, and mTOR in cartilage tissue, as well as the protein expression levels of phosphorylated PI3K(p-PI3K)/PI3K, phosphorylated Akt(p-Akt)/Akt, phosphorylated mTOR(p-mTOR)/mTOR, and P62 were significantly increased. Beclin1 protein expression was decreased. Comparied with the model group, the number of chondrocytes in the knee joint of rats in each group of Syngnathus extract increased, and the arrangement of chondrocytes was relatively neat. The cartilage layer was restored, and the serum levels of IL-1β, IL-6, and TNF-α, as well as the mRNA expression levels of PI3K, Akt, and mTOR in cartilage tissue were significantly reduced. The protein expression levels of p-PI3K/PI3K, p-Akt/Akt, p-mTOR/mTOR, and P62 were significantly reduced in the rats in the middle-dose and high-dose groups of Syngnathus extract, and the Beclin1 protein expression was significantly increased. The protein expression levels of p-PI3K/PI3K, p-Akt/Akt, and P62 in rats in the low-dose group of Syngnathus extract were significantly reduced. In summary, Syngnathus extract may be used to treat knee osteoarthritis by inhibiting the expression of PI3K/Akt/mTOR signaling pathway, so as to alleviate the inflammatory response in the organism, enhance the autophagy activity of chondrocytes, and reduce the apoptosis of chondrocytes.
Animals ; TOR Serine-Threonine Kinases/genetics* ; Male ; Rats, Sprague-Dawley ; Signal Transduction/drug effects* ; Proto-Oncogene Proteins c-akt/genetics* ; Rats ; Osteoarthritis, Knee/metabolism* ; Drugs, Chinese Herbal/administration & dosage* ; Phosphatidylinositol 3-Kinases/genetics* ; Humans

This study predicts the potential mechanism of Hippocampus in the treatment of knee osteoarthritis(KOA) through network pharmacology, with preliminary verification using molecular docking and animal experiments. The database was used to screen the active chemical components of Hippocampus and the targets of KOA, and Gene Ontology(GO) functional analysis, Kyoto Encyclopedia of Genes and Genomes(KEGG) pathway enrichment analysis, and molecular docking were performed on the relevant core targets to preliminarily explore the potential targets and mechanisms of Hippocampus in the treatment of KOA. A rat KOA model was constructed by intra-articular injection of sodium iodoacetate, and the rats were intervened with different doses of Hippocampus decoction and celecoxib. The expression of relevant targets was detected through hematoxylin-eosin(HE) staining, enzyme-linked immunosorbent assay(ELISA), RT-qPCR, and Western blot to further validate the network pharmacology results. A total of 23 drug-like components of the Hippocampus were screened, and 128 common targets with KOA were identified, involving interleukin-17(IL-17) signaling pathway, transcription factor(FoxO) signaling pathway, tumor necrosis factor(TNF) signaling pathway. Molecular docking results showed that the screened core chemical components exhibited good affinity with key targets. HE staining demonstrated that Hippocampus improved the morphology of the cartilage layer. ELISA confirmed that Hippocampus significantly reduced the levels of IL-6 and TNF-α in the serum of KOA rats. Western blot and RT-qPCR analysis showed that Hippocampus significantly reduced the expression of IL-6, TNF-α, matrix metalloproteinase(MMP) 13, IL-17A, nuclear factor κB activator 1(ACT1), tumor necrosis factor receptor-associated factor 6(TRAF6) and nuclear factor κB(NF-κB) in cartilage tissue. The results suggest that Hippocampus can alleviate the degree of joint damage in the KOA rat model induced by sodium iodoacetate. The mechanism of action is related to the inhibition of the IL-17 signaling pathway, reduction of inflammation, and inhibition of extracellular matrix(ECM) degradation.
Animals ; Molecular Docking Simulation ; Rats ; Drugs, Chinese Herbal/administration & dosage* ; Network Pharmacology ; Male ; Osteoarthritis, Knee/metabolism* ; Rats, Sprague-Dawley ; Signal Transduction/drug effects* ; Humans ; Interleukin-17/metabolism* ; Tumor Necrosis Factor-alpha/metabolism* ; Disease Models, Animal ; Hippocampus/chemistry*

Cardiotocography (CTG) is a non-invasive and important tool for diagnosing fetal distress during pregnancy. To meet the needs of intelligent fetal heart monitoring based on deep learning, this paper proposes a TWD-MOAL deep active learning algorithm based on the three-way decision (TWD) theory and multi-objective optimization Active Learning (MOAL). During the training process of a convolutional neural network (CNN) classification model, the algorithm incorporates the TWD theory to select high-confidence samples as pseudo-labeled samples in a fine-grained batch processing mode, meanwhile low-confidence samples annotated by obstetrics experts were also considered. The TWD-MOAL algorithm proposed in this paper was validated on a dataset of 16 355 prenatal CTG records collected by our group. Experimental results showed that the algorithm proposed in this paper achieved an accuracy of 80.63% using only 40% of the labeled samples, and in terms of various indicators, it performed better than the existing active learning algorithms under other frameworks. The study has shown that the intelligent fetal heart monitoring model based on TWD-MOAL proposed in this paper is reasonable and feasible. The algorithm significantly reduces the time and cost of labeling by obstetric experts and effectively solves the problem of data imbalance in CTG signal data in clinic, which is of great significance for assisting obstetrician in interpretations CTG signals and realizing intelligence fetal monitoring.
Humans ; Pregnancy ; Female ; Cardiotocography/methods* ; Deep Learning ; Neural Networks, Computer ; Algorithms ; Fetal Monitoring/methods* ; Heart Rate, Fetal ; Fetal Distress/diagnosis* ; Fetal Heart/physiology*