An integrated imaging sensor for aberration-corrected 3D photography.

Planar digital image sensors facilitate broad applications in a wide range of areas1-5, and the number of pixels has scaled up rapidly in recent years2,6. However, the practical performance of imaging systems is fundamentally limited by spatially nonuniform optical aberrations originating from imperfect lenses or environmental disturbances7,8. Here we propose an integrated scanning light-field imaging sensor, termed a meta-imaging sensor, to achieve high-speed aberration-corrected three-dimensional photography for universal applications without additional hardware modifications. Instead of directly detecting a two-dimensional intensity projection, the meta-imaging sensor captures extra-fine four-dimensional light-field distributions through a vibrating coded microlens array, enabling flexible and precise synthesis of complex-field-modulated images in post-processing. Using the sensor, we achieve high-performance photography up to a gigapixel with a single spherical lens without a data prior, leading to orders-of-magnitude reductions in system capacity and costs for optical imaging. Even in the presence of dynamic atmosphere turbulence, the meta-imaging sensor enables multisite aberration correction across 1,000 arcseconds on an 80-centimetre ground-based telescope without reducing the acquisition speed, paving the way for high-resolution synoptic sky surveys. Moreover, high-density accurate depth maps can be retrieved simultaneously, facilitating diverse applications from autonomous driving to industrial inspections.

Identifying new cancer genes based on the integration of annotated gene sets via hypergraph neural networks.

MOTIVATION: Identifying cancer genes remains a significant challenge in cancer genomics research. Annotated gene sets encode functional associations among multiple genes, and cancer genes have been shown to cluster in hallmark signaling pathways and biological processes. The knowledge of annotated gene sets is critical for discovering cancer genes but remains to be fully exploited. RESULTS: Here, we present the DIsease-Specific Hypergraph neural network (DISHyper), a hypergraph-based computational method that integrates the knowledge from multiple types of annotated gene sets to predict cancer genes. First, our benchmark results demonstrate that DISHyper outperforms the existing state-of-the-art methods and highlight the advantages of employing hypergraphs for representing annotated gene sets. Second, we validate the accuracy of DISHyper-predicted cancer genes using functional validation results and multiple independent functional genomics data. Third, our model predicts 44 novel cancer genes, and subsequent analysis shows their significant associations with multiple types of cancers. Overall, our study provides a new perspective for discovering cancer genes and reveals previously undiscovered cancer genes. AVAILABILITY AND IMPLEMENTATION: DISHyper is freely available for download at https://github.com/genemine/DISHyper.

Pan-cancer analysis of CDKN2A alterations identifies a subset of gastric cancer with a cold tumor immune microenvironment.

BACKGROUND: Although CDKN2A alteration has been explored as a favorable factor for tumorigenesis in pan-cancers, the association between CDKN2A point mutation (MUT) and intragenic deletion (DEL) and response to immune checkpoint inhibitors (ICIs) is still disputed. This study aims to determine the associations of CDKN2A MUT and DEL with overall survival (OS) and response to immune checkpoint inhibitors treatment (ICIs) among pan-cancers and the clinical features of CDKN2A-altered gastric cancer. METHODS: This study included 45,000 tumor patients that underwent tumor sequencing across 33 cancer types from four cohorts, the MSK-MetTropism, MSK-IMPACT, OrigiMed2020 and TCGA cohorts. Clinical outcomes and genomic factors associated with response to ICIs, including tumor mutational burden, copy number alteration, neoantigen load, microsatellite instability, tumor immune microenvironment and immune-related gene signatures, were collected in pan-cancer. Clinicopathologic features and outcomes were assessed in gastric cancer. Patients were grouped based on the presence of CDKN2A wild type (WT), CDKN2A MUT, CDKN2A DEL and CDKN2A other alteration (ALT). RESULTS: Our research showed that CDKN2A-MUT patients had shorter survival times than CDKN2A-WT patients in the MSK MetTropism and TCGA cohorts, but longer OS in the MSK-IMPACT cohort with ICIs treatment, particularly in patients having metastatic disease. Similar results were observed among pan-cancer patients with CDKN2A DEL and other ALT. Notably, CDKN2A ALT frequency was positively related to tumor-specific objective response rates to ICIs in MSK MetTropism and OrigiMed 2020. Additionally, individuals with esophageal carcinoma or stomach adenocarcinoma who had CDKN2A MUT had poorer OS than patients from the MSK-IMPACT group, but not those with adenocarcinoma. We also found reduced levels of activated NK cells, T cells CD8 and M2 macrophages in tumor tissue from CDKN2A-MUT or DEL pan-cancer patients compared to CDKN2A-WT patients in TCGA cohort. Gastric cancer scRNA-seq data also showed that CDKN2A-ALT cancer contained less CD8 T cells but more exhausted T cells than CDKN2A-WT cancer. A crucial finding of the pathway analysis was the inhibition of three immune-related pathways in the CDKN2A ALT gastric cancer patients, including the interferon alpha response, inflammatory response, and interferon gamma response. CONCLUSIONS: This study illustrates the CDKN2A MUT and DEL were associated with a poor outcome across cancers. CDKN2A ALT, on the other hand, have the potential to be used as a biomarker for choosing patients for ICI treatment, notably in esophageal carcinoma and stomach adenocarcinoma.

High-Performance Ultraviolet Organic Light-Emitting Diodes Enabled by Double Boron-Oxygen-Embedded Benzo[m]tetraphene Emitters.

Ultraviolet organic light-emitting diodes (UV OLEDs) have attracted increasing attention because of their promising applications in healthcare, industry, and agriculture; however, their development has been hindered by the shortage of robust UV emitters. Herein, we embedded double boron-oxygen units into nonlinear polycyclic aromatic hydrocarbons (BO-PAHs) to regulate their molecular configurations and excited-state properties, enabling novel bent BO-biphenyl (BO-bPh) and helical BO-naphthyl (BO-Nap) emitters with hybridized local and charge-transfer (HLCT) characteristics. They could be facilely synthesized in gram-scale amounts via a highly efficient two-step route. BO-bPh and BO-Nap showed strong UV and violet-blue photoluminescence in toluene with full width at half-maximum values of 25 and 37 nm, along with quantum efficiencies of 98 and 99%, respectively. A BO-bPh-based OLED showed high color purity UV electroluminescence peaking at 394 nm with Commission Internationale de l'Eclairage (CIE) coordinates of (0.166, 0.021). Moreover, the device demonstrated a record-high maximum external quantum efficiency (EQE) of 11.3%, achieved by successful hot exciton utilization. This work demonstrates the promising potential of double BO-PAHs as robust emitters for future UV OLEDs.

An integrated brain-specific network identifies genes associated with neuropathologic and clinical traits of Alzheimer's disease.

Alzheimer's disease (AD) has a strong genetic predisposition. However, its risk genes remain incompletely identified. We developed an Alzheimer's brain gene network-based approach to predict AD-associated genes by leveraging the functional pattern of known AD-associated genes. Our constructed network outperformed existing networks in predicting AD genes. We then systematically validated the predictions using independent genetic, transcriptomic, proteomic data, neuropathological and clinical data. First, top-ranked genes were enriched in AD-associated pathways. Second, using external gene expression data from the Mount Sinai Brain Bank study, we found that the top-ranked genes were significantly associated with neuropathological and clinical traits, including the Consortium to Establish a Registry for Alzheimer's Disease score, Braak stage score and clinical dementia rating. The analysis of Alzheimer's brain single-cell RNA-seq data revealed cell-type-specific association of predicted genes with early pathology of AD. Third, by interrogating proteomic data in the Religious Orders Study and Memory and Aging Project and Baltimore Longitudinal Study of Aging studies, we observed a significant association of protein expression level with cognitive function and AD clinical severity. The network, method and predictions could become a valuable resource to advance the identification of risk genes for AD.

Care, control and complications of hospitalised patients with type 1 diabetes in China: A nationwide-based registry study.

AIMS: To evaluate the status quo of type 1 diabetes (T1D) management and characteristics of hospitalised patients with T1D in China through a nationwide multicentre registry study, the China Diabetes Type 1 Study (CD1S). MATERIALS AND METHODS: Clinical data from the electronic hospital records of all people with T1D were retrospectively collected in 13 tertiary hospitals across 7 regions of China from January 2016 to December 2021. Patients were defined as newly diagnosed who received a diagnosis of diabetes for less than 3 months. RESULTS: Among the 4993 people with T1D, the median age (range) at diagnosis was 23.0 (1.0-87.0) years and the median disease duration was 2.0 years. The median haemoglobin A1c (HbA1c) level was 10.7%. The prevalence of obesity, overweight, dyslipidemia, and hypertension were 2.5%, 10.8%, 62.5% and 25.9%, respectively. The incidence rate of diabetic ketoacidosis at disease onset was 41.1%, with the highest in children <10 years of age (50.6%). In patients not newly diagnosed, 60.7% were diagnosed with at least one chronic diabetic complication, with the highest proportion (45.3%) of diabetic peripheral neuropathy. Chronic complications were detected in 79.2% of people with T1D duration ≥10 years. CONCLUSIONS: In the most recent years, there were still unsatisfactory metabolic control and high incidence of diabetic ketoacidosis as well as chronic diabetic complications among inpatients with T1D in China. The ongoing CD1S prospective study aims to improve the quality of T1D management nationally.

M2 macrophage-derived exosomes induce angiogenesis and increase skin flap survival through HIF1AN/HIF-1α/VEGFA control.

BACKGROUND: Skin flap transplantation is a routine strategy in plastic and reconstructive surgery for skin-soft tissue defects. Recent research has shown that M2 macrophages have the potential for pro-angiogenesis during tissue healing. METHODS: In our research, we extracted the exosomes from M2 macrophages(M2-exo) and applied the exosomes in the model of skin flap transplantation. The flap survival area was measured, and the choke vessels were assessed by morphological observation. Hematoxylin and eosin (H&E) staining and Immunohistochemistry were applied to assess the neovascularization. The effect of M2-exo on the function of Human umbilical vein endothelial cells (HUVECs) was also investigated. We also administrated 2-methoxyestradiol (2-ME2, an inhibitor of HIF-1α) to explore the underlying mechanism. We tested the effects of M2-Exo on the proliferation of HUVECs through CCK8 assay and EdU staining assay. RESULTS: The survival area and number of micro-vessels in the skin flaps were increased in the M2-exo group. Besides, the dilation rate of choke vessels was also enhanced in the M2-exo group. Additionally, compared with the control group, M2-exo could accelerate the proliferation, migration and tube formation of HUVECs in vitro. Furthermore, the expression of the pro-angiogenesis factors, HIF-1α and VEGFA, were overexpressed with the treatment of the M2-exo. The expression of HIF1AN protein level was decreased in the M2-exo group. Finally, treatment with HIF-1α inhibitor reverses the pro-survival effect of M2-exo on skin flaps by interfering with the HIF1AN/HIF-1α/VEGFA signaling pathway. CONCLUSION: This study showed that M2-exosomes promote skin flap survival by enhancing angiogenesis, with HIF1AN/HIF-1α/VEGFA playing a crucial role in this process.

Exploring the relationship between metabolism and immune microenvironment in osteosarcoma based on metabolic pathways.

BACKGROUND: Metabolic remodeling and changes in tumor immune microenvironment (TIME) in osteosarcoma are important factors affecting prognosis and treatment. However, the relationship between metabolism and TIME needs to be further explored. METHODS: RNA-Seq data and clinical information of 84 patients with osteosarcoma from the TARGET database and an independent cohort from the GEO database were included in this study. The activity of seven metabolic super-pathways and immune infiltration levels were inferred in osteosarcoma patients. Metabolism-related genes (MRGs) were identified and different metabolic clusters and MRG-related gene clusters were identified using unsupervised clustering. Then the TIME differences between the different clusters were compared. In addition, an MRGs-based risk model was constructed and the role of a key risk gene, ST3GAL4, in osteosarcoma cells was explored using molecular biological experiments. RESULTS: This study revealed four key metabolic pathways in osteosarcoma, with vitamin and cofactor metabolism being the most relevant to prognosis and to TIME. Two metabolic pathway-related clusters (C1 and C2) were identified, with some differences in immune activating cell infiltration between the two clusters, and C2 was more likely to respond to two chemotherapeutic agents than C1. Three MRG-related gene clusters (GC1-3) were also identified, with significant differences in prognosis among the three clusters. GC2 and GC3 had higher immune cell infiltration than GC1. GC3 is most likely to respond to immune checkpoint blockade and to three commonly used clinical drugs. A metabolism-related risk model was developed and validated. The risk model has strong prognostic predictive power and the low-risk group has a higher level of immune infiltration than the high-risk group. Knockdown of ST3GAL4 significantly inhibited proliferation, migration, invasion and glycolysis of osteosarcoma cells and inhibited the M2 polarization of macrophages. CONCLUSION: The metabolism of vitamins and cofactors is an important prognostic regulator of TIME in osteosarcoma, MRG-related gene clusters can well reflect changes in osteosarcoma TIME and predict chemotherapy and immunotherapy response. The metabolism-related risk model may serve as a useful prognostic predictor. ST3GAL4 plays a critical role in the progression, glycolysis, and TIME of osteosarcoma cells.

CXCR4-Mediated Codelivery of FLT3 and BCL-2 Inhibitors for Enhanced Targeted Combination Therapy of FLT3-ITD Acute Myeloid Leukemia.

Acute myeloid leukemia (AML) is often associated with poor prognosis and survival. Small molecule inhibitors, though widening the treatment landscape, have limited monotherapy efficacy. The combination therapy, however, shows suboptimal clinical outcomes due to low bioavailability, overlapping systemic toxicity and drug resistance. Here, we report that CXCR4-mediated codelivery of the BCL-2 inhibitor venetoclax (VEN) and the FLT3 inhibitor sorafenib (SOR) via T22 peptide-tagged disulfide cross-linked polymeric micelles (TM) achieves synergistic treatment of FLT3-ITD AML. TM-VS with a VEN/SOR weight ratio of 1/4 and T22 peptide density of 20% exhibited an extraordinary inhibitory effect on CXCR4-overexpressing MV4-11 AML cells. TM-VS at a VEN/SOR dosage of 2.5/10 mg/kg remarkably reduced leukemia burden, prolonged mouse survival, and impeded bone loss in orthotopic MV4-11-bearing mice, outperforming the nontargeted M-VS and oral administration of free VEN/SOR. CXCR4-mediated codelivery of BCL-2 and FLT3 inhibitors has emerged as a prospective clinical treatment for FLT3-ITD AML.

Efficacy and safety of semaglutide 2.4 mg for weight loss in overweight or obese adults without diabetes: An updated systematic review and meta-analysis including the 2-year STEP 5 trial.

AIM: To explore the safety and efficacy of subcutaneous semaglutide 2.4 mg, administered once a week in non-diabetic overweight or obese individuals. METHODS: A thorough search was performed of various databases including PubMed, Embase, the Cochrane Library, Web of Science, clinicaltrials.gov, CNKI and Wanfang from their inception up to April 11, 2023. Our aim was to identify randomized controlled trials (RCTs) that compared the efficacy of semaglutide administered once weekly with placebo in overweight or obese adults. Through a review of the literature, data were extracted from relevant studies and assessed for quality, and a meta-analysis was conducted using RevMan 5.4.1 software. RESULTS: Six RCTs comprising 3962 overweight or obese individuals were identified. The findings indicated that, in comparison to the placebo group, semaglutide caused a significant and sustainable reduction in the percentage of body weight (BW; mean difference [MD]: -11.80% [95% confidence interval {CI} -12.93, -10.68]; P < 0.00001) as well as a decrease in absolute BW (MD: -12.2 kg [95% CI -13.3, -11.1]; P < 0.00001), body mass index (MD: -4.5 kg/m2 [95% CI -4.9, -4.1]; P < 0.00001) and waist circumference (MD:-9.4 cm [95% CI -10.1, -8.8]; P < 0.00001). Moreover, it achieved a higher proportion of patients who experienced weight loss exceeding 5%, 10%, 15% and 20%. Furthermore, semaglutide showed significant efficacy in controlling blood pressure, blood sugar levels, C-reactive protein levels, and lipid profiles. In terms of safety, the most common adverse effects following semaglutide treatment were gastrointestinal adverse reactions (risk ratio: 1.49 [95% CI 1.38, 1.60]; P < 0.00001), which were generally mild to moderate in severity and temporary. CONCLUSION: In overweight or obese non-diabetic individuals, semaglutide had a remarkable and sustained weight loss effect that was well tolerated and safe.

Evaluation of diagnostic potential of CD38 in rickets.

BACKGROUND: Rickets occurs in infants and children (aged 2 months to 3 years), compromising their skeletal development and damaging nervous, hematopoietic, immune, and other system functions. This study aimed to explore the significance of CD38 in rickets. METHODS: The microarray dataset GSE22523 was analyzed to obtain differentially expressed genes in rickets patients. A total of 36 rickets patients and healthy controls were recruited for the study, and their blood samples were collected, followed by detecting mRNA levels of CD38 using quantitative real-time polymerase chain reaction (qRT-PCR). Moreover, the significance of CD38 in rickets patients was analyzed by receiver operating characteristic (ROC) analysis, while the correlation between CD38 and 25-hydroxy-vitamin D (25OHD)/parathyroid hormone (PTH) was analyzed with Pearson's correlation. RESULTS: Results showed that CD38 mRNA levels and PTH contents were significantly increased in the rickets patients while 25OHD contents were decreased. Correlation analysis indicated that CD38 was positively correlated with PTH and negatively correlated with 25OHD in both serum and plasma samples of rickets patients. Moreover, ROC analysis showed that serum CD38 was 0.9005 (95 % CI: 0.8313-0.9696), and the AUCs of plasma CD38 was 0.7215 (95 % CI: 0.6031-0.8398) in differentiating rickets patients from healthy persons, advocating serum CD38 had better diagnostic value. CONCLUSION: CD38 mRNA levels were upregulated in rickets patients and closely correlated with PTH and 25OHD contents, indicating CD38 might be a diagnostic marker of rickets patients. Further research on the diagnostic utility of CD38 is necessary for the diagnosis and treatment of ricketsin rickets in the future.

Resveratrol modulates the inflammatory response in hPDLSCs via the NRF2/HO-1 and NF-κB pathways and promotes osteogenic differentiation.

OBJECTIVE: The purpose of this study was to investigate resveratrol's specific role as an anti-inflammatory and osteogenic differentiation of hPDLSCs in periodontitis and to reveal the mechanisms involved. BACKGROUND: Numerous studies have shown that inhibiting the inflammatory response of periodontal tissues and promoting the regeneration of alveolar bone are crucial treatments for periodontitis. Resveratrol has been found to have certain anti-inflammatory property. However, the anti-inflammatory mechanism and osteogenic effect of resveratrol in periodontitis are poorly understood. MATERIALS AND METHODS: We constructed an in vitro periodontitis model by LPS stimulation of hPDLSCs and performed WB, RT-qPCR, and immunofluorescence to analyze inflammatory factors and related pathways. In addition, we explored the osteogenic ability of resveratrol in in vitro models. RESULTS: In vitro, resveratrol ameliorated the inflammatory response associated with activation of the NF-κB pathway through activation of the NRF2/HO-1 pathway, characterized by inhibition of p65/p50 nuclear translocation and the proinflammatory cytokines interleukin-1ß levels. Resveratrol also has a positive effect on osteogenic differentiation. CONCLUSIONS: Observations suggest that resveratrol modulates the inflammatory response in hPDLSCs via the NRF2/HO-1 and NF-κB pathways and promotes osteogenic differentiation.

Applying generative AI with retrieval augmented generation to summarize and extract key clinical information from electronic health records.

BACKGROUND: Malnutrition is a prevalent issue in aged care facilities (RACFs), leading to adverse health outcomes. The ability to efficiently extract key clinical information from a large volume of data in electronic health records (EHR) can improve understanding about the extent of the problem and developing effective interventions. This research aimed to test the efficacy of zero-shot prompt engineering applied to generative artificial intelligence (AI) models on their own and in combination with retrieval augmented generation (RAG), for the automating tasks of summarizing both structured and unstructured data in EHR and extracting important malnutrition information. METHODOLOGY: We utilized Llama 2 13B model with zero-shot prompting. The dataset comprises unstructured and structured EHRs related to malnutrition management in 40 Australian RACFs. We employed zero-shot learning to the model alone first, then combined it with RAG to accomplish two tasks: generate structured summaries about the nutritional status of a client and extract key information about malnutrition risk factors. We utilized 25 notes in the first task and 1,399 in the second task. We evaluated the model's output of each task manually against a gold standard dataset. RESULT: The evaluation outcomes indicated that zero-shot learning applied to generative AI model is highly effective in summarizing and extracting information about nutritional status of RACFs' clients. The generated summaries provided concise and accurate representation of the original data with an overall accuracy of 93.25%. The addition of RAG improved the summarization process, leading to a 6% increase and achieving an accuracy of 99.25%. The model also proved its capability in extracting risk factors with an accuracy of 90%. However, adding RAG did not further improve accuracy in this task. Overall, the model has shown a robust performance when information was explicitly stated in the notes; however, it could encounter hallucination limitations, particularly when details were not explicitly provided. CONCLUSION: This study demonstrates the high performance and limitations of applying zero-shot learning to generative AI models to automatic generation of structured summarization of EHRs data and extracting key clinical information. The inclusion of the RAG approach improved the model performance and mitigated the hallucination problem.

TissueNexus: a database of human tissue functional gene networks built with a large compendium of curated RNA-seq data.

Mapping gene interactions within tissues/cell types plays a crucial role in understanding the genetic basis of human physiology and disease. Tissue functional gene networks (FGNs) are essential models for mapping complex gene interactions. We present TissueNexus, a database of 49 human tissue/cell line FGNs constructed by integrating heterogeneous genomic data. We adopted an advanced machine learning approach for data integration because Bayesian classifiers, which is the main approach used for constructing existing tissue gene networks, cannot capture the interaction and nonlinearity of genomic features well. A total of 1,341 RNA-seq datasets containing 52,087 samples were integrated for all of these networks. Because the tissue label for RNA-seq data may be annotated with different names or be missing, we performed intensive hand-curation to improve quality. We further developed a user-friendly database for network search, visualization, and functional analysis. We illustrate the application of TissueNexus in prioritizing disease genes. The database is publicly available at https://www.diseaselinks.com/TissueNexus/.

The Potential Value of Paraspinal Nerve Block (PVB) in Percutaneous Nephrolithotomy (PCNL) Compared with General Anesthesia and Epidural Anesthesia.

Objective: To analyze the potential value of paraspinal nerve block (PVB) in percutaneous nephrolithotomy (PCNL) and to compare it with general anesthesia and epidural anesthesia. Methods: 120 patients undergoing PCNL surgery in Shanghai Jiao Tong University Affiliated Sixth People's Hospital from January 2021 to June 2022 were selected and divided into PVB anesthesia group, general anesthesia group, and epidural anesthesia group according to different anesthesia methods, with 40 cases in each group. The anesthesia index (anesthesia operation time, anesthetic effect time, anesthesia time), the vital signs (heart rate, mean arterial pressure), postoperative pain [visual analog scale (VAS)], stress response index (cortisol and noradrenaline), the incidence of adverse reactions (nausea and vomiting, lethargy, dizziness, skin itching, bradycardia) were compared among the three groups. Results: The operation time of the anesthesia in the PVB anesthesia group was 5.72±1.25, which was significantly lower than that in the the general (7.95±1.15) and epidural anesthesia groups(8.23±1.43), and the differences were statistically significant (P = .000). The time of onset of anesthesia in the PVB anesthesia group was 6.63±1.87, which was significantly lower than that in the the general (9.84±2.41) and epidural anesthesia groups(10.14±2.89), and the differences were statistically significant (P = .000).The heart rate during percutaneous puncture and intraoperative lithotripsy in the PVB anesthesia group was statistically lower than in the general and epidural anesthesia groups (P < .05). The mean arterial pressure 20 minutes after anesthesia and at the end of operation in the PVB anesthesia group was higher than that in the general anesthesia group, and the mean arterial pressure during percutaneous puncture and intraoperative lithotomy was lower than that in the general anesthesia group (P < .05). The VAS scores of the PVB anesthesia group at 2, 6, 12, 24, and 48 hours after the operation were lower than those of general and epidural anesthesia groups (P < .05). The incidence of adverse reactions was 5.00% (2/40) in the PVB anesthesia group and 35.00% (14/40) in the general anesthesia group, which was lower than that of 27.50% (11/40) in the epidural anesthesia group. (P < .05). Conclusion: The potential value of PVB in PCNL is high is better than that of general anesthesia and epidural anesthesia, anesthesia can shorten operation time and work time, extend the time of anesthesia to maintain, and be helpful to the intraoperative vital signs in patients with stable, mild postoperative pain and stress, low incidence of adverse reactions, efficacy and safety are good, can be introduced.

TU-LESS procedure for acute abdomen in late pregnancy: a retrospective study.

BACKGROUND: Acute abdominal conditions during pregnancy are significant risks to maternal and fetal health, necessitating timely diagnosis and intervention. The choice of surgical approach is a major concern for obstetricians. OBJECTIVE: To evaluate the safety and efficacy of the TU-LESS procedure for acute abdomen in late pregnancy. METHODS: We retrospectively analyzed 12 patients who underwent TU-LESS for acute abdominal conditions in the third trimester from 2020 to 2023. We reviewed medical records for clinical characteristics, surgical interventions, postoperative complications, and pregnancy outcomes. RESULTS: The study included patients with a median age of 27 (range 20-35) and a BMI of 24.33 kg/m2 (range 21.34-31.96). The median gestational age at surgery was 30 weeks (range, 28 + 3-32 + 4 weeks), with surgeries lasting an average of 60 min (range, 30-163 min). Blood loss was 2-20 mL, and the median hospital stay post-surgery was 6 days (range, 2-16 days). There were no significant complications. The median time to delivery after TU-LESS was 56 days (range, 26-66 days), resulting in 8 full-term deliveries, 2 preterm cesareans, and 2 preterm vaginal deliveries. All newborns were healthy, with no fetal losses or neonatal deaths. CONCLUSION: TU-LESS, performed by experienced obstetricians and gynecologists with proper preoperative preparation, is safe and effective for managing acute abdomen in late pregnancy, without the need to delay surgery due to gestational age.

Improved Double Deep Q-Network Algorithm Applied to Multi-Dimensional Environment Path Planning of Hexapod Robots.

Detecting transportation pipeline leakage points within chemical plants is difficult due to complex pathways, multi-dimensional survey points, and highly dynamic scenarios. However, hexapod robots' maneuverability and adaptability make it an ideal candidate for conducting surveys across different planes. The path-planning problem of hexapod robots in multi-dimensional environments is a significant challenge, especially when identifying suitable transition points and planning shorter paths to reach survey points while traversing multi-level environments. This study proposes a Particle Swarm Optimization (PSO)-guided Double Deep Q-Network (DDQN) approach, namely, the PSO-guided DDQN (PG-DDQN) algorithm, for solving this problem. The proposed algorithm incorporates the PSO algorithm to supplant the traditional random selection strategy, and the data obtained from this guided approach are subsequently employed to train the DDQN neural network. The multi-dimensional random environment is abstracted into localized maps comprising current and next level planes. Comparative experiments were performed with PG-DDQN, standard DQN, and standard DDQN to evaluate the algorithm's performance by using multiple randomly generated localized maps. After testing each iteration, each algorithm obtained the total reward values and completion times. The results demonstrate that PG-DDQN exhibited faster convergence under an equivalent iteration count. Compared with standard DQN and standard DDQN, reductions in path-planning time of at least 33.94% and 42.60%, respectively, were observed, significantly improving the robot's mobility. Finally, the PG-DDQN algorithm was integrated with sensors onto a hexapod robot, and validation was performed through Gazebo simulations and Experiment. The results show that controlling hexapod robots by applying PG-DDQN provides valuable insights for path planning to reach transportation pipeline leakage points within chemical plants.

Assessment of monthly runoff simulations based on a physics-informed machine learning framework: The effect of intermediate variables in its construction.

Hydrological forecasting is of great importance for water resources management and planning, especially given the increasing occurrence of extreme events such as floods and droughts. The physics-informed machine learning (PIML) models effectively integrate conceptual hydrologic models with machine learning (ML) models. In this process, the intermediate variables of PIML models serve as bridges between inputs and outputs, while the impact of intermediate variables on the performance of PIML models remains unclear. To fill this knowledge gap, this study aims to encompass the construction of PIML models based on various hydrologic models, conduct comparative analyses of different intermediate variables based on a case study of 205 CAMELS basins, and further explore the relationship between the performance of PIML models and catchment characteristics. The optimal ML model for constructing PIML is first selected among four ML models within the 205 basins. The PIML models are then developed based on five monthly water balance models, namely TM, XM, MEP, SLM, and TVGM. To quantify the potential impact of difference in intermediate variables, two sets of experiments are further designed and performed, namely S1 with actual evapotranspiration as the intermediate variable and S2 with soil moisture as the intermediate variable. Results show that five PIML models generally outperformed the optimal standalone ML models, i.e., the Lasso model. Specifically, regardless of the choice of intermediate variables, the PIML-XM model consistently outperformed the other models within the same basins. Almost all constructed PIML models are affected by the intermediate variables in monthly runoff simulations. Typically, S1 exhibited better performance compared to S2. A greater impact of aridity index, forest fraction, and catchment area on model performance is observed in S2. These findings improve our understanding of constructing PIML models in hydrology by emphasizing their excellent performance in runoff simulations and highlighting the importance of intermediate variables.

Mixed crushing and competitive leaching of all electrode material components and metal collector fluid in the spent lithium battery.

Hydrometallurgy is a primary method for recovering cathode electrode materials from spent lithium-ion batteries (LIBs). Most of the current research materials are pure cathode electrode materials obtained through manual disassembly. However, the spent LIBs are typically broken as a whole during the actual industrial recycling which makes the electrode materials combined with the collector fluid. Therefore, the competitive leaching between metal collector fluid and electrode material was examined. The pyrolysis characteristics of the electrode materials were analyzed to determine the pyrolysis temperature. The electrode sheet was pyrolyzed and then crushed for competitive leaching. The effect of pyrolysis was analyzed by XPS. The competitive leaching behavior was studied based on leaching agent concentration, leaching time and leaching temperature. The composition and morphology of the residue were determined to prove the competitive leaching results by XRD-SEM. TG results showed that 500 °C was the suitable pyrolysis temperature. XPS analysis demonstrated that pyrolysis can completely remove PVDF. Li and Co were preferentially leached during the competitive leaching while the leaching rates were 90.10% and 93.40% with 50 min leaching at 70 °C. The Al and Cu had weak competitive leachability and the leaching rate was 29.10% and 0.00%. XRD-SEM analysis showed that Li and Co can be fully leached with residual Al and Cu remaining. The results showed that the mixed leaching of electrode materials is feasible based on its excellent selective leaching properties.

Research Progress of Non-Noble Metal Catalysts for Carbon Dioxide Methanation.

The extensive utilization of fossil fuels has led to a rapid increase in atmospheric CO2 concentration, resulting in various environmental issues. To reduce reliance on fossil fuels and mitigate CO2 emissions, it is important to explore alternative methods of utilizing CO2 and H2 as raw materials to obtain high-value-added chemicals or fuels. One such method is CO2 methanation, which converts CO2 and H2 into methane (CH4), a valuable fuel and raw material for other chemicals. However, CO2 methanation faces challenges in terms of kinetics and thermodynamics. The reaction rate, CO2 conversion, and CH4 yield need to be improved to make the process more efficient. To overcome these challenges, the development of suitable catalysts is essential. Non-noble metal catalysts have gained significant attention due to their high catalytic activity and relatively low cost. In this paper, the thermodynamics and kinetics of the CO2 methanation reaction are discussed. The focus is primarily on reviewing Ni-based, Co-based, and other commonly used catalysts such as Fe-based. The effects of catalyst supports, preparation methods, and promoters on the catalytic performance of the methanation reaction are highlighted. Additionally, the paper summarizes the impact of reaction conditions such as temperature, pressure, space velocity, and H2/CO2 ratio on the catalyst performance. The mechanism of CO2 methanation is also summarized to provide a comprehensive understanding of the process. The objective of this paper is to deepen the understanding of non-noble metal catalysts in CO2 methanation reactions and provide insights for improving catalyst performance. By addressing the limitations of CO2 methanation and exploring the factors influencing catalyst effectiveness, researchers can develop more efficient and cost-effective catalysts for this reaction.