Histone phosphorylation integrates the hepatic glucagon-PKA-CREB gluconeogenesis program in response to fasting.

The glucagon-PKA signal is generally believed to control hepatic gluconeogenesis via the CREB transcription factor. Here we uncovered a distinct function of this signal in directly stimulating histone phosphorylation for gluconeogenic gene regulation in mice. In the fasting state, CREB recruited activated PKA to regions near gluconeogenic genes, where PKA phosphorylated histone H3 serine 28 (H3S28ph). H3S28ph, recognized by 14-3-3ζ, promoted recruitment of RNA polymerase II and transcriptional stimulation of gluconeogenic genes. In contrast, in the fed state, more PP2A was found near gluconeogenic genes, which counteracted PKA by dephosphorylating H3S28ph and repressing transcription. Importantly, ectopic expression of phosphomimic H3S28 efficiently restored gluconeogenic gene expression when liver PKA or CREB was depleted. These results together highlight a different functional scheme in regulating gluconeogenesis by the glucagon-PKA-CREB-H3S28ph cascade, in which the hormone signal is transmitted to chromatin for rapid and efficient gluconeogenic gene activation.

Induction of mouse totipotent stem cells by a defined chemical cocktail.

In mice, only the zygotes and blastomeres from 2-cell embryos are authentic totipotent stem cells (TotiSCs) capable of producing all the differentiated cells in both embryonic and extraembryonic tissues and forming an entire organism1. However, it remains unknown whether and how totipotent stem cells can be established in vitro in the absence of germline cells. Here we demonstrate the induction and long-term maintenance of TotiSCs from mouse pluripotent stem cells using a combination of three small molecules: the retinoic acid analogue TTNPB, 1-azakenpaullone and the kinase blocker WS6. The resulting chemically induced totipotent stem cells (ciTotiSCs), resembled mouse totipotent 2-cell embryo cells at the transcriptome, epigenome and metabolome levels. In addition, ciTotiSCs exhibited bidirectional developmental potentials and were able to produce both embryonic and extraembryonic cells in vitro and in teratoma. Furthermore, following injection into 8-cell embryos, ciTotiSCs contributed to both embryonic and extraembryonic lineages with high efficiency. Our chemical approach to totipotent stem cell induction and maintenance provides a defined in vitro system for manipulating and developing understanding of the totipotent state and the development of multicellular organisms from non-germline cells.

Optical manipulation of electronic dimensionality in a quantum material.

Exotic phenomena can be achieved in quantum materials by confining electronic states into two dimensions. For example, relativistic fermions are realized in a single layer of carbon atoms1, the quantized Hall effect can result from two-dimensional (2D) systems2,3, and the superconducting transition temperature can be considerably increased in a one-atomic-layer material4,5. Ordinarily, a 2D electronic system can be obtained by exfoliating the layered materials, growing monolayer materials on substrates, or establishing interfaces between different materials. Here we use femtosecond infrared laser pulses to invert the periodic lattice distortion sectionally in a three-dimensional (3D) charge density wave material (1T-TiSe2), creating macroscopic domain walls of transient 2D ordered electronic states with unusual properties. The corresponding ultrafast electronic and lattice dynamics are captured by time-resolved and angle-resolved photoemission spectroscopy6 and ultrafast electron diffraction at energies of the order of megaelectronvolts7. Moreover, in the photoinduced 2D domain wall near the surface we identify a phase with enhanced density of states and signatures of potential opening of an energy gap near the Fermi energy. Such optical modulation of atomic motion is an alternative path towards realizing 2D electronic states and will be a useful platform upon which novel phases in quantum materials may be discovered.

Extracellular matrix physical properties govern the diffusion of nanoparticles in tumor microenvironment.

Nanoparticles (NPs) are confronted with limited and disappointing delivery efficiency in tumors clinically. The tumor extracellular matrix (ECM), whose physical traits have recently been recognized as new hallmarks of cancer, forms a main steric obstacle for NP diffusion, yet the role of tumor ECM physical traits in NP diffusion remains largely unexplored. Here, we characterized the physical properties of clinical gastric tumor samples and observed limited distribution of NPs in decellularized tumor tissues. We also performed molecular dynamics simulations and in vitro hydrogel experiments through single-particle tracking to investigate the diffusion mechanism of NPs and understand the influence of tumor ECM physical properties on NP diffusion both individually and collectively. Furthermore, we developed an estimation matrix model with evaluation scores of NP diffusion efficiency through comprehensive analyses of the data. Thus, beyond finding that loose and soft ECM with aligned structure contribute to efficient diffusion, we now have a systemic model to predict NP diffusion efficiency based on ECM physical traits and provide critical guidance for personalized tumor diagnosis and treatment.

Construction of Gold/Rhodium Freestanding Superstructures as Antenna-Reactor Photocatalysts for Plasmon-Driven Nitrogen Fixation.

Precisely controlling the architecture and spatial arrangement of plasmonic heterostructures offers unique opportunities to tailor the catalytic property, whereas the lack of a wet-chemistry synthetic approach to fabricating nanostructures with high-index facets limits their practical applications. Herein, we describe a universal synthetic strategy to construct Au/Rh freestanding superstructures (SSs) through the selective growth of ordered Rh nanoarrays on high-index-faceted Au nanobipyramids (NBPs). This synthetic strategy works on various metal nanocrystal substrates and can yield diverse Au/Rh and Pd/Rh SSs. Especially, the obtained Au NBP/Rh SSs exhibit high photocatalytic activity toward N2 fixation as a result of the spatially separated architecture, local electric field enhancement, and the antenna-reactor mechanism. Both theoretical and experimental results reveal that the Au NBPs can function as nanoantennas for light-harvesting to generate hot charge carriers for driving N2 fixation, while the Rh nanoarrays can serve as the active sites for N2 adsorption and activation to synergistically promote the overall catalytic activity in the Au NBP/Rh SSs. This work offers new avenues to rationally designing and constructing spatially separated plasmonic photocatalysts for high-efficiency catalytic applications.

Impaired distal renal potassium handling in streptozotocin-induced diabetic mice.

Diabetes is closely associated with K+ disturbances during disease progression and treatment. However, it remains unclear whether K+ imbalance occurs in diabetes with normal kidney function. In this study, we examined the effects of dietary K+ intake on systemic K+ balance and renal K+ handling in streptozotocin (STZ)-induced diabetic mice. The control and STZ mice were fed low or high K+ diet for 7 days to investigate the role of dietary K+ intake in renal K+ excretion and K+ homeostasis and to explore the underlying mechanism by evaluating K+ secretion-related transport proteins in distal nephrons. K+-deficient diet caused excessive urinary K+ loss, decreased daily K+ balance, and led to severe hypokalemia in STZ mice compared with control mice. In contrast, STZ mice showed an increased daily K+ balance and elevated plasma K+ level under K+-loading conditions. Dysregulation of the NaCl cotransporter (NCC), epithelial Na+ channel (ENaC), and renal outer medullary K+ channel (ROMK) was observed in diabetic mice fed either low or high K+ diet. Moreover, amiloride treatment reduced urinary K+ excretion and corrected hypokalemia in K+-restricted STZ mice. On the other hand, inhibition of SGLT2 by dapagliflozin promoted urinary K+ excretion and normalized plasma K+ levels in K+-supplemented STZ mice, at least partly by increasing ENaC activity. We conclude that STZ mice exhibited abnormal K+ balance and impaired renal K+ handling under either low or high K+ diet, which could be primarily attributed to the dysfunction of ENaC-dependent renal K+ excretion pathway, despite the possible role of NCC.NEW & NOTEWORTHY Neither low dietary K+ intake nor high dietary K+ intake effectively modulates renal K+ excretion and K+ homeostasis in STZ mice, which is closely related to the abnormality of ENaC expression and activity. SGLT2 inhibitor increases urinary K+ excretion and reduces plasma K+ level in STZ mice under high dietary K+ intake, an effect that may be partly due to the upregulation of ENaC activity.

Metabolic Labeling and Digital Microfluidic Single-Cell Sequencing for Single Bacterial Genotypic-Phenotypic Analysis.

Accurate assessment of phenotypic and genotypic characteristics of bacteria can facilitate comprehensive cataloguing of all the resistance factors for better understanding of antibiotic resistance. However, current methods primarily focus on individual phenotypic or genotypic profiles across different colonies. Here, a Digital microfluidic-based automated assay for whole-genome sequencing of single-antibiotic-resistant bacteria is reported, enabling Genotypic and Phenotypic Analysis of antibiotic-resistant strains (Digital-GPA). Digital-GPA can efficiently isolate and sequence antibiotic-resistant bacteria illuminated by fluorescent D-amino acid (FDAA)-labeling, producing high-quality single-cell amplified genomes (SAGs). This enables identifications of both minor and major mutations, pinpointing substrains with distinctive resistance mechanisms. Digital-GPA can directly process clinical samples to detect and sequence resistant pathogens without bacterial culture, subsequently provide genetic profiles of antibiotic susceptibility, promising to expedite the analysis of hard-to-culture or slow-growing bacteria. Overall, Digital-GPA opens a new avenue for antibiotic resistance analysis by providing accurate and comprehensive molecular profiles of antibiotic resistance at single-cell resolution.

Formation of E-band luminescence-active centers in bismuth-doped silica fiber via atomic layer deposition.

In this study, a Si defect structure was added into the silica network in order to activate the bismuth and silica structure active center. TD-DFT theoretical simulations show that the Bi and Si ODC(I) models can excite the active center of the E-band at 1408 nm. Additionally, the Bi-doped silica fiber (BDSF) with improved fluorescence was fabricated using atomic layer deposition (ALD) combined with the modified chemical vapor deposition (MCVD) technique. Some tests were used to investigate the structural and optical properties of BDSF. The UV-VIS spectral peak of the BDSF preform is 424 cm-1, and the binding energy of XPS is 439.3 eV, indicating the presence of Bi° atom in BDSF. The Raman peak near 811 cm-1 corresponds to the Bi-O bond. The Si POL defect lacks a Bi-O structure, and the reason for the absence of simulated active center from the E-band is explained. A fluorescence spectrometer was used to analyze the emission peak of a BDSF at 1420 nm. The gain of the BDSF based optical amplifier was measured 28.8 dB at 1420 nm and confirmed the effective stimulation of the bismuth active center in the E-band.

Consolidation chemotherapy after definitive concurrent chemoradiotherapy in patients with inoperable esophageal squamous cell carcinoma: a multicenter non-inferiority phase III randomized clinical trial.

BACKGROUND: Definitive concurrent chemoradiotherapy (dCCRT) is the gold standard for the treatment of locally advanced esophageal squamous cell carcinoma (ESCC). However, the potential benefits of consolidation chemotherapy after dCCRT in patients with esophageal cancer remain debatable. Prospective randomized controlled trials comparing the outcomes of dCCRT with or without consolidation chemotherapy in patients with ESCC are lacking. In this study, we aim to generate evidence regarding consolidation chemotherapy efficacy in patients with locally advanced, inoperable ESCC. METHODS: This is a multicenter, prospective, open-label, phase-III randomized controlled trial comparing non-inferiority of dCCRT alone to consolidation chemotherapy following dCCRT. In total, 600 patients will be enrolled and randomly assigned in a 1:1 ratio to receive either consolidation chemotherapy after dCCRT (Arm A) or dCCRT alone (Arm B). Overall survival will be the primary endpoint, whereas progression-free survival, locoregional progression-free survival, distant metastasis-free survival, and treatment-related toxicity will be the secondary endpoints. DISCUSSION: This study aid in further understanding the effects of consolidation chemotherapy after dCCRT in patients with locally advanced, inoperable ESCC. TRIAL REGISTRATION: ChiCTR1800017646.

Multicenter investigation of preoperative distinction between primary central nervous system lymphomas and glioblastomas through interpretable artificial intelligence models.

OBJECTIVE: Research into the effectiveness and applicability of deep learning, radiomics, and their integrated models based on Magnetic Resonance Imaging (MRI) for preoperative differentiation between Primary Central Nervous System Lymphoma (PCNSL) and Glioblastoma (GBM), along with an exploration of the interpretability of these models. MATERIALS AND METHODS: A retrospective analysis was performed on MRI images and clinical data from 261 patients across two medical centers. The data were split into a training set (n = 153, medical center 1) and an external test set (n = 108, medical center 2). Radiomic features were extracted using Pyradiomics to build the Radiomics Model. Deep learning networks, including the transformer-based MobileVIT Model and Convolutional Neural Networks (CNN) based ConvNeXt Model, were trained separately. By applying the "late fusion" theory, the radiomics model and deep learning model were fused to produce the optimal Max-Fusion Model. Additionally, Shapley Additive exPlanations (SHAP) and Grad-CAM were employed for interpretability analysis. RESULTS: In the external test set, the Radiomics Model achieved an Area under the receiver operating characteristic curve (AUC) of 0.86, the MobileVIT Model had an AUC of 0.91, the ConvNeXt Model demonstrated an AUC of 0.89, and the Max-Fusion Model showed an AUC of 0.92. The Delong test revealed a significant difference in AUC between the Max-Fusion Model and the Radiomics Model (P = 0.02). CONCLUSION: The Max-Fusion Model, combining different models, presents superior performance in distinguishing PCNSL and GBM, highlighting the effectiveness of model fusion for enhanced decision-making in medical applications. CLINICAL RELEVANCE STATEMENT: The preoperative non-invasive differentiation between PCNSL and GBM assists clinicians in selecting appropriate treatment regimens and clinical management strategies.

Association of exposure to multiple heavy metals during pregnancy with the risk of gestational diabetes mellitus and insulin secretion phase after glucose stimulation.

BACKGROUND: Epidemiological evidence for the association between heavy metals exposure during pregnancy and gestational diabetes mellitus (GDM) is still inconsistent. Additionally, that is poorly understood about the potential cause behind the association, for instance, whether heavy metal exposure is related to the change of insulin secretion phase is unknown. OBJECTIVES: We aimed to explore the relationships of blood levels of arsenic (As), lead (Pb), thallium (Tl), nickel (Ni), cadmium (Cd), cobalt (Co), barium (Ba), chromium (Cr), mercury (Hg) and copper (Cu) during early pregnancy with the odds of GDM, either as an individual or a mixture, as well as the association of the metals with insulin secretion phase after glucose stimulation. METHODS: We performed a nested case-control study consisting of 302 pregnant women with GDM and 302 controls at the First Affiliated Hospital of Anhui Medical University in Hefei, China. Around the 12th week of pregnancy, blood samples of pregnant women were collected and levels of As, Pb, Tl, Ni, Cd, Co, Ba, Cr, Hg and Cu in blood were measured. An oral glucose tolerance test (OGTT) was done in each pregnant woman during the 24-28th week of pregnancy to diagnose GDM and C-peptide (CP) levels during OGTT were measured simultaneously. The four metals (As, Pb, Tl and Ni) with the highest effect on odds of GDM were selected for the subsequent analyses via the random forest model. Conditional logistic regression models were performed to analyze the relationships of blood As, Pb, Tl and Ni levels with the odds of GDM. The weighted quantile sum (WQS) regression and bayesian kernel machine regression (BKMR) were used to assess the joint effects of levels of As, Pb, Tl and Ni on the odds of GDM as well as to evaluate which metal level contributed most to the association. Latent profile analysis (LPA) was conducted to identify profiles of glycemic and C-peptide levels at different time points. Multiple linear regression models were employed to explore the relationships of metals with glycaemia-related indices (fasting blood glucose (FBG), 1-hour blood glucose (1h BG), 2-hour blood glucose (2h BG), fasting C-peptide (FCP), 1-hour C-peptide (1h CP), 2-hour C-peptide (2h CP), FCP/FBG, 1h CP/1h BG, 2h CP/2h BG, area under the curve of C-peptide (AUCP), area under the curve of glucose (AUCG), AUCP/AUCG and profiles of BGs and CPs, respectively. Mixed-effects models with repeated measures data were used to explore the relationship between As (the ultimately selected metal) level and glucose-stimulated insulin secretion phase. The mediation effects of AUCP and AUCG on the association of As exposure with odds of GDM were investigated using mediation models. RESULTS: The odds of GDM in pregnant women increased with every ln unit increase in blood As concentration (odds ratio (OR) = 1.46, 95% confidence interval (CI) = 1.04-2.05). The joint effects of As, Pb, Tl and Ni levels on the odds of GDM was statistically significant when blood levels of four metals were exceeded their 50th percentile, with As level being a major contributor. Blood As level was positively associated with AUCG and the category of glucose latent profile, the values of AUCG were much higher in GDM group than those in non-GDM group, which suggested that As exposure associated with the odds of GDM may be due to that As exposure was related to the impairment of glucose tolerance among pregnant women. The significant and positive relationships of As level with AUCP, CP latent profile category, 2h CP and 2h CP/2h BG were observed, respectively; and the values of 1h CP/1h BG and AUCP/AUCG were much lower in GDM group than those in non-GDM group, which suggested that As exposure may not relate to the impairment of insulin secretion (pancreatic ß-cell function) among pregnant women. The relationships between As level and 2h CP as well as 2h CP/2h BG were positive and significant; additionally, the values of 2h CP/2h BG in GDM group were comparable with those in non-GDM group; the peak value of CP occurred at 2h in GDM group, as well as the values of 2h CP/2h BG in high As exposure group were much higher than those in low As exposure group, which suggested that As exposure associated with the increased odds of GDM may be due to that As exposure was related to the change of insulin secretion phase (delayment of the peak of insulin secretion) among pregnant women. In addition, AUCP mediated 11% (p < 0.05) and AUCG mediated 43% (p < 0.05) of the association between As exposure and the odds of GDM. CONCLUSION: Our results suggested that joint exposure to As, Pb, Tl and Ni during early pregnancy was positively associated with the odds of GDM, As was a major contributor; and the association of environmental As exposure with the increased odds of GDM may be due to that As exposure was related to the impairment of glucose tolerance and change of insulin secretion phase after glucose stimulation (delayment of the peak of insulin secretion) among pregnant women.

An evidence-based model for predicting conversion to open surgery in minimally invasive distal pancreatectomy.

BACKGROUND: Intraoperative conversion to open surgery is an adverse event during minimally invasive distal pancreatectomy (MIDP), associated with poor postoperative outcomes. The aim of this study was to develop a model capable of predicting conversion in patients undergoing MIDP. METHODS: A total of 352 patients who underwent MIPD were included in this retrospective analysis and randomly assigned to training and validation cohorts. Potential risk factors related to open conversion were identified through a literature review, and data on these factors in our cohort was collected accordingly. In the training cohort, multivariate logistic regression analysis was performed to adjust the impact of confounding factors to identify independent risk factors for model building. The constructed model was evaluated using the receiver operating characteristics curve, decision curve analysis (DCA), and calibration curves. RESULTS: Following an extensive literature review, a total of ten preoperative risk factors were identified, including sex, BMI, albumin, smoker, size of lesion, tumor close to major vessels, type of pancreatic resection, surgical approach, MIDP experience, and suspicion of malignancy. Multivariate analysis revealed that sex, tumor close to major vessels, suspicion of malignancy, type of pancreatic resection (subtotal pancreatectomy or left pancreatectomy), and MIDP experience persisted as significant predictors for conversion to open surgery during MIDP. The constructed model offered superior discrimination ability compared to the existing model (area under the curve, training cohort: 0.921 vs. 0.757, P < 0.001; validation cohort: 0.834 vs. 0.716, P = 0.018). The DCA and the calibration curves revealed the clinical usefulness of the nomogram and a good consistency between the predicted and observed values. CONCLUSION: The evidence-based prediction model developed in this study outperformed the previous model in predicting conversions of MIDP. This model could contribute to decision-making processes surrounding the selection of surgical approaches and facilitate patient counseling on the conversion risk of MIDP.

Predicting Outcome of Patients With Cerebral Hemorrhage Using a Computed Tomography-Based Interpretable Radiomics Model: A Multicenter Study.

OBJECTIVE: The aim of this study was to develop and validate an interpretable and highly generalizable multimodal radiomics model for predicting the prognosis of patients with cerebral hemorrhage. METHODS: This retrospective study involved 237 patients with cerebral hemorrhage from 3 medical centers, of which a training cohort of 186 patients (medical center 1) was selected and 51 patients from medical center 2 and medical center 3 were used as an external testing cohort. A total of 1762 radiomics features were extracted from nonenhanced computed tomography using Pyradiomics, and the relevant macroscopic imaging features and clinical factors were evaluated by 2 experienced radiologists. A radiomics model was established based on radiomics features using the random forest algorithm, and a radiomics-clinical model was further trained by combining radiomics features, clinical factors, and macroscopic imaging features. The performance of the models was evaluated using area under the curve (AUC), sensitivity, specificity, and calibration curves. Additionally, a novel SHAP (SHAPley Additive exPlanations) method was used to provide quantitative interpretability analysis for the optimal model. RESULTS: The radiomics-clinical model demonstrated superior predictive performance overall, with an AUC of 0.88 (95% confidence interval, 0.76-0.95; P < 0.01). Compared with the radiomics model (AUC, 0.85; 95% confidence interval, 0.72-0.94; P < 0.01), there was a 0.03 improvement in AUC. Furthermore, SHAP analysis revealed that the fusion features, rad score and clinical rad score, made significant contributions to the model's decision-making process. CONCLUSION: Both proposed prognostic models for cerebral hemorrhage demonstrated high predictive levels, and the addition of macroscopic imaging features effectively improved the prognostic ability of the radiomics-clinical model. The radiomics-clinical model provides a higher level of predictive performance and model decision-making basis for the risk prognosis of cerebral hemorrhage.

A Novel Interpretable Radiomics Model to Distinguish Nodular Goiter From Malignant Thyroid Nodules.

OBJECTIVES: The purpose of this study is to inquire about the potential association between radiomics features and the pathological nature of thyroid nodules (TNs), and to propose an interpretable radiomics-based model for predicting the risk of malignant TN. METHODS: In this retrospective study, computed tomography (CT) imaging and pathological data from 141 patients with TN were collected. The data were randomly stratified into a training group (n = 112) and a validation group (n = 29) at a ratio of 4:1. A total of 1316 radiomics features were extracted by using the pyradiomics tool. The redundant features were removed through correlation testing, and the least absolute shrinkage and selection operator (LASSO) or the minimum redundancy maximum relevance standard was used to select features. Finally, 4 different machine learning models (RF Hybrid Feature, SVM Hybrid Feature, RF, and LASSO) were constructed. The performance of the 4 models was evaluated using the receiver operating characteristic curve. The calibration curve, decision curve analysis, and SHapley Additive exPlanations method were used to evaluate or explain the best radiomics machine learning model. RESULTS: The optimal radiomics model (RF Hybrid Feature model) demonstrated a relatively high degree of discrimination with an area under the receiver operating characteristic curve (AUC) of 0.87 (95% CI, 0.70-0.97; P < 0.001) for the validation cohort. Compared with the commonly used LASSO model (AUC, 0.78; 95% CI, 0.60-0.91; P < 0.01), there is a significant improvement in AUC in the validation set, net reclassification improvement, 0.79 (95% CI, 0.13-1.46; P < 0.05), and integrated discrimination improvement, 0. 20 (95% CI, 0.10-0.30; P < 0.001). CONCLUSION: The interpretable radiomics model based on CT performs well in predicting benign and malignant TNs by using quantitative radiomics features of the unilateral total thyroid. In addition, the data preprocessing method incorporating different layers of features has achieved excellent experimental results. CLINICAL RELEVANCE STATEMENT: As the detection rate of TNs continues to increase, so does the diagnostic burden on radiologists. This study establishes a noninvasive, interpretable and accurate machine learning model to rapidly identify the nature of TN found in CT.

Bibliometric Analyses of the Research Trends of Female Pelvic Organ Prolapse.

INTRODUCTION AND HYPOTHESIS: The study was aimed at systematically analyzing the research status and trends of pelvic organ prolapse (POP) using bibliometrics. METHODS: We retrieved documents published between 1975 and 2022 from the Web of Science Core Collection (WoSCC) database, and manually selected them for bibliometric analyses of country, institution, journal, highly locally cited documents and research trends based on co-citation clustering and keywords using the R Bibliometricx package and CiteSpace software. RESULTS: A total of 5,703 publications were included. Although the number of annual publications on POP increased, the trend of annual publication reached an obvious plateau in the first half of the 2010s. The USA, China, the UK, the University of Michigan, the University of Pittsburgh, and the University of Sydney were the top three countries and institutions with the most publications respectively. International Urogynecology Journal, American Journal of Obstetrics and Gynecology, and Obstetrics and Gynecology were the journals with the most extensive academic influence on the field of POP research. The international cooperation was lacking and the highly cited documents focused on high-level, evidence-based studies. Epidemiological studies and surgical treatment have achieved a plateau or decline. Recent studies have focused on conservative treatment, physical therapy, and minimally invasive surgery. In addition to evidence-based medicine studies, tissue engineering is the future direction of POP. CONCLUSIONS: This study used bibliometric analyses to provide insights into the status and potential research directions of POP. More high-quality, evidence-based medicine studies and in-depth tissue engineering research should be propelled forward.

Amnioinfusion compared with expectant management in oligohydramnios with intact amnions in the second and early third trimesters.

INTRODUCTION: Treatment of oligohydramnios in the mid-trimester is challenging, because of the high incidence of adverse perinatal outcomes mainly due to bronchopulmonary dysplasia. Antenatal amnioinfusion has been proposed as a possible treatment for oligohydramnios with intact amnions, but there are few relevant studies. This study aimed to evaluate the effectiveness of transabdominal amnioinfusion in the management of oligohydramnios without fetal lethal malformations in the second and early third trimesters. MATERIAL AND METHODS: It is a historical cohort study. A total of 79 patients diagnosed with oligohydramnios at 18-32 weeks gestation were enrolled. In the amnioinfusion group (n = 39), patients received transabdominal amnioinfusion with the assistance of real-time ultrasound guidance. In the expectant group (n = 41), patients were treated with 3000 mL of intravenous isotonic fluids daily. The perioperative complications and perinatal outcomes were analyzed. RESULTS: Compared with the expectant group, the delivery latency was significantly prolonged, and the rate of cesarean delivery was significantly reduced in the amnioinfusion group (p < 0.05). Although the rate of intrauterine fetal death was significantly reduced, the incidence of spontaneous miscarriage, premature rupture of membranes (PROMs), and threatened preterm labor were significantly higher in the amnioinfusion group than in the expectant group (p < 0.05). There was no significant difference in terms of perinatal mortality (28.9% vs. 41.4%, p > 0.05). Multivariate logistic regression revealed that amnioinfusion (odds ratio [OR] 0.162, 95% confidence interval [CI] 0.04-0.61, p = 0.008) and gestational age at diagnosis (OR 0.185, 95% CI 0.04-0.73, p = 0.016) were independently associated with neonatal adverse outcomes. Further subgrouping showed that amnioinfusion significantly reduced the frequency of bronchopulmonary hypoplasia for patients ≤26 weeks (26.7% vs. 75.0%, p = 0.021). The rates of other neonatal complications were similar in both groups. CONCLUSIONS: Amnioinfusion has no significant effect on improving the perinatal mortality of oligohydramnios in the second and early third trimesters. It may lead to a relatively high rate of PROM and spontaneous abortion. However, amnioinfusion may significantly improve the latency period, the rate of cesarean delivery, and neonatal outcomes of oligohydramnios, especially for women ≤26 weeks with high risk of neonatal bronchopulmonary hypoplasia.

The degree of risk factor and accumulation effect for large niche in individuals after cesarean section.

BACKGROUND: The risk factors associated with niche on the cesarean scar have been reported, however, the degree of these factors associated with large niche and the accumulation effects of these risk factors on the development of large niche are unclear. METHODS: Large niche was evaluated by transvaginal sonography during mid-follicular phase. Logistic regression model was used to assess 32 risk factors by univariate analysis. Then, a scoring model based on the screened risk factors was generated. The performance of this model was evaluated by area under curve (AUC). Finally, the scoring model was applied in 123 women to assess the external validation. RESULT(S): In the training cohort study, 163 women were diagnosed with large niche. The final scoring model involves eight risk factors with the rating scores including age at delivery (30-34 years: 1 point; ≥ 35 years: 4.5 points), retroflexed uterus (8.5 points), meconium-stained amniotic fluid (4.5 points), twice CSs (4.0 points), postpartum endometritis (4.5 points), premature rupture of membranes (2.5 points), intrahepatic cholestasis of pregnancy (mild to moderate: 3 points; severe: 6.5 points), and cervical dilatation (1-3 cm: 2.0 points; 4-10 cm: 4.5 points). The accumulation effect with a cut-off value of 8.0 in the scoring was associated with the large niche after CS. CONCLUSION(S): This is the first scoring model to objectively quantify the risk of a large niche after CS. Optimal risk factors control by avoiding high score factors and multiple factors accumulation may eliminate the risk of large niche development.

Associations between different insulin resistance indices and the risk of all-cause mortality in peritoneal dialysis patients.

BACKGROUND: Insulin resistance (IR) is prevalent in individuals undergoing peritoneal dialysis (PD) and is related to increased susceptibility to coronary artery disease and initial peritonitis. In recent investigations, correlations have been found between indices of IR and the incidence of all-cause mortality in various populations. However, such correlations have not been detected among individuals undergoing PD. Hence, the present study's aim was to explore the connections between IR indices and the incidence of all-cause mortality in PD patients. METHODS: Peritoneal dialysis patients (n = 1736) were recruited from multiple PD centres between January 2010 and December 2021. Cox proportional hazards and restricted cubic spline regression models were used to evaluate the connections between the triglyceride-glucose (TyG) index, triglyceride-glucose/body mass index (TyG-BMI), and triglyceride/high-density lipoprotein cholesterol (TG/HDL-C) ratio and the occurrence of all-cause mortality. All three IR indices were integrated into the same model to assess the predictive stability. Furthermore, a forest plot was employed to display the findings of the subgroup analysis of PD patients. RESULTS: Overall, 378 mortality events were recorded during a median follow-up time of 2098 days. Among PD patients, a higher TyG index, TyG-BMI, and TG/HDL-C ratio were identified as independent risk factors for all-cause mortality according to Cox proportional hazards analyses (hazard ratio (HR) 1.588, 95% confidence interval (CI) 1.261-2.000; HR 1.428, 95% CI 1.067-1.910; HR 1.431, 95% CI 1.105-1.853, respectively). In a model integrating the three IR indices, the TyG index showed the highest predictive stability. According to the forest plot for the TyG index, no significant interactions were observed among the subgroups. CONCLUSION: Significant associations were found between the TyG index, TyG-BMI, and TG/HDL-C ratio and the incidence of all-cause mortality among PD patients. The TyG index may be the most stable of the three surrogate IR markers. Finally, a correlation was identified between IR and the risk of all-cause mortality in patients undergoing PD.

Comparison of traditional and suctioning ureteral access sheath during retrograde intrarenal surgery in the treatment of renal calculi.

PURPOSE: This study aims to compare the efficiency and clinical outcomes between the suctioning ureteral access sheath (UAS) group and the traditional UAS group during retrograde intrarenal surgery (RIRS) for kidney stones and explore the impact of suctioning UAS on postoperative infectious complications. METHODS: We retrospectively reviewed the clinical data of 162 patients with kidney stones who underwent RIRS with a traditional UAS (n = 74) or a suctioning UAS (n = 71) between March 2021 and May 2023. RESULTS: The mean operative time in suctioning UAS group (39.03 ± 18.01 s) was significantly shorter than that (49.73 ± 20.77 s) in the traditional UAS group (P = 0.037). The mean postoperative hospital stay was significantly shorter in the suctioning UAS group (1.57 ± 0.82d) compared with the traditional UAS group (2.30 ± 1.6 2 d) (P = 0.032). The instant SFRs were significantly higher in the suctioning UAS group (88.73%) than in the traditional UAS group (75.68%) (P = 0.040). The overall SFR in suctioning UAS group (92.96%) was slightly higher than the traditional UAS group (85.14%). The incidence of overall complications was significantly higher in the traditional UAS group (35.14%) than in the suctioning UAS group (16.90%) (P = 0.013). In multivariate analysis, female patients (OR 0.053, P = 0.018), positive urine WBC (OR 10.382, P = 0.034), operative time > 60 min (OR 20.231, P = 0.032), and the application of traditional UAS (OR 0.042, P = 0.017) were independent risk factors associated with infectious complications. CONCLUSION: We demonstrated that suctioning UAS provided a higher instant SFR and fewer postoperative infectious complications during RIRS, and patients with predictable risk factors for infectious complications could potentially benefit from the use of the suctioning UAS.

An interpretable artificial intelligence model based on CT for prognosis of intracerebral hemorrhage: a multicenter study.

OBJECTIVES: To develop and validate a novel interpretable artificial intelligence (AI) model that integrates radiomic features, deep learning features, and imaging features at multiple semantic levels to predict the prognosis of intracerebral hemorrhage (ICH) patients at 6 months post-onset. MATERIALS AND METHODS: Retrospectively enrolled 222 patients with ICH for Non-contrast Computed Tomography (NCCT) images and clinical data, who were divided into a training cohort (n = 186, medical center 1) and an external testing cohort (n = 36, medical center 2). Following image preprocessing, the entire hematoma region was segmented by two radiologists as the volume of interest (VOI). Pyradiomics algorithm library was utilized to extract 1762 radiomics features, while a deep convolutional neural network (EfficientnetV2-L) was employed to extract 1000 deep learning features. Additionally, radiologists evaluated imaging features. Based on the three different modalities of features mentioned above, the Random Forest (RF) model was trained, resulting in three models (Radiomics Model, Radiomics-Clinical Model, and DL-Radiomics-Clinical Model). The performance and clinical utility of the models were assessed using the Area Under the Receiver Operating Characteristic Curve (AUC), calibration curve, and Decision Curve Analysis (DCA), with AUC compared using the DeLong test. Furthermore, this study employs three methods, Shapley Additive Explanations (SHAP), Grad-CAM, and Guided Grad-CAM, to conduct a multidimensional interpretability analysis of model decisions. RESULTS: The Radiomics-Clinical Model and DL-Radiomics-Clinical Model exhibited relatively good predictive performance, with an AUC of 0.86 [95% Confidence Intervals (CI): 0.71, 0.95; P < 0.01] and 0.89 (95% CI: 0.74, 0.97; P < 0.01), respectively, in the external testing cohort. CONCLUSION: The multimodal explainable AI model proposed in this study can accurately predict the prognosis of ICH. Interpretability methods such as SHAP, Grad-CAM, and Guided Grad-Cam partially address the interpretability limitations of AI models. Integrating multimodal imaging features can effectively improve the performance of the model. CLINICAL RELEVANCE STATEMENT: Predicting the prognosis of patients with ICH is a key objective in emergency care. Accurate and efficient prognostic tools can effectively prevent, manage, and monitor adverse events in ICH patients, maximizing treatment outcomes.