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Abstract With the diminishing availability of oil and gas resources, the Rotary Steerable System has become increasingly important. However, the vibrations and shocks during the drilling process pose challenges to the Measurement While Drilling. In recent years, the application of machine learning in the field of petroleum exploration has gradually expanded, especially in the estimation of geological parameters and lithology discrimination. However, there is still limited research on drilling tool attitude measurement. To address the above-mentioned issues, this study proposes a method that combines drilling tool attitude sensor data with artificial neural networks to improve the accuracy of dynamic inclination measurement. This method utilizes machine learning techniques, combining real-time z-axis acceleration signals and magnetic induction signals, and employs a deep learning model to invert the x and y-axis acceleration signals, thereby achieving high-precision measurement of dynamic inclination angles. Experimental results show that Long Short-Term Memory model, under simulated measurement conditions with different rotational speeds, yields dynamic inclination curve errors ranging from 0.4° to 0.7°, significantly reducing the errors compared to the original measurements. This method not only improves the accuracy of inclination angle measurement but also demonstrates strong adaptability to different rotational speeds, providing more accurate data support for drilling operations.

Abstract Background Knee osteoarthritis (OA) is a common and serious joint disease and patients mainly suffer from knee pain and dysfunction, significantly impacting their quality of life and daily activities. Non-pharmacological treatments and total knee arthroplasty (TKA) are the two major treatments for knee OA. TKA is the primary treatment for severe knee OA patients, however, the postoperative rehabilitation results are not entirely satisfactory. To enhance postoperative rehabilitation outcomes, preoperative lower-limb muscle strengthening has aroused attention and demonstrated significant results on functional recovery after TKA. Specifically, isokinetic muscle strengthening (IMS) has been highly anticipated as an efficient training method. Nevertheless, its effects in perioperative and long-term periods of TKA require further investigation. This study aims to evaluate the impact of preoperative lower-limb IMS during the 4 weeks before TKA on the perioperative and postoperative outcomes, with particular attention on knee function, pain, and inflammatory response. Methods In this study, 104 patients awaiting TKA will be randomized to 2 groups: the IMS and health education groups. All patients will be evaluated at baseline, post-intervention, discharge day, and postoperative day 14, with follow-up assessments at 6 weeks, 3, 6, and 12 months after discharge. The primary outcome will be the Knee Injury and Osteoarthritis Outcome Score 3 months after TKA and the medial-to-lateral knee contact force ratio. The secondary outcomes will be changes in lower-limb muscle strength, knee pain, inflammation response, active range of motion, performance-based function (5-times Sit-to-Stand Test, Time Up-and-Go Test, 3 m walking speed), Berg Balance Scale, and surgical need. Discussion Lower-limb IMS can improve muscle strength, pain, and function for early knee OA patients. However, the effects of different doses of IMS on perioperative and postoperative knee pain, inflammation response, and function after TKA are inconclusive. This study will provide high-quality evidence to assess the efficacy of IMS in end-stage patients who will undergo TKA. Understanding IMS in end-stage knee OA patients will help guide future surgical management for this population. Trial Registration Chinese Clinical Trial Registry ChiCTR2400088136.

Abstract There are increasing concerns regarding the rapid expansion of polystyrene nanoplastics (PS-NPs), which could impact human health. Previous studies have shown that nanoplastics can be transferred from mothers to offspring through the placenta and breast milk, resulting in cognitive deficits in offspring. However, the neurotoxic effects of maternal exposure on offspring and its mechanisms remain unclear. In this study, PS-NPs (50 nm) were gavaged to female rats throughout gestation and lactation to establish an offspring exposure model to study the neurotoxicity and behavioral changes caused by PS-NPs on offspring. Neonatal rat hippocampal neuronal cells were used to investigate the pathways through which NPs induce neurodevelopmental toxicity in offspring rats, using iron inhibitors, autophagy inhibitors, reactive oxygen species (ROS) scroungers, P53 inhibitors, and NCOA4 inhibitors. We found that low PS-NPs dosages can cause ferroptosis in the hippocampus of the offspring, resulting in a decline in the cognitive, learning, and memory abilities of the offspring. PS-NPs induced NOCA4-mediated ferritinophagy and promoted ferroptosis by inciting ROS production to activate P53-mediated ferritinophagy. Furthermore, the levels of the antioxidant factors glutathione peroxidase 4 (GPX4) and glutathione (GSH), responsible for ferroptosis, were reduced. In summary, this study revealed that consumption of PS-NPs during gestation and lactation can cause ferroptosis and damage the hippocampus of offspring. Our results can serve as a basis for further research into the neurodevelopmental effects of nanoplastics in offspring. Graphical Abstract

Abstract With the development of laser scanners and machine learning, point cloud semantic segmentation plays a significant role in autonomous driving, scene reconstruction, human-computer interaction, and other fields. In recent years, point cloud semantic segmentation based on deep learning has become one of the key research directions in point cloud processing. Due to the limited ability to exploit geometric details and contextual information in point clouds, most methods that adopt encoder-decoder architecture lose local structural information easily, especially detailed features, and extract features insufficiently. To address this issue, the edge-preserving inception DenseGCN U-Net (entitled as EIDU-Net) is proposed. EIDU-Net makes full use of the complementation between geometric details in the original point cloud and high-level features. The edge-preserved graph pooling (EGP) layer, the key module of the EIDU-Net, is designed to retain additional edge feature information from the original point cloud during pooling operations. Accordingly, the edge-preserved graph unpooling (EGU) layer can restore the feature graph more efficiently based on the additionally retained edge features. Extensive experiments demonstrate that our proposed EIDU-Net has remarkable improvements on semantic segmentation tasks under whatever S3DIS or Terracotta Warrior fragments. Our code is publicly available at https://github.com/caoxin918/EIDU-Net .

Abstract Porous organic cages (POCs) with permanent porosity and excellent host–guest property hold great potentials in regulating ion transport behavior, yet their feasibility as solid-state electrolytes has never been testified in a practical battery. Herein, we design and fabricate a quasi-solid-state electrolyte (QSSE) based on a POC to enable the stable operation of Li-metal batteries (LMBs). Benefiting from the ordered channels and cavity-induced anion-trapping effect of POC, the resulting POC-based QSSE exhibits a high Li+ transference number of 0.67 and a high ionic conductivity of 1.25 × 10−4 S cm−1 with a low activation energy of 0.17 eV. These allow for homogeneous Li deposition and highly reversible Li plating/stripping for over 2000 h. As a proof of concept, the LMB assembled with POC-based QSSE demonstrates extremely stable cycling performance with 85% capacity retention after 1000 cycles. Therefore, our work demonstrates the practical applicability of POC as SSEs for LMBs and could be extended to other energy-storage systems, such as Na and K batteries.

Abstract Inflammatory bowel diseases (IBD) are a group of chronic, non-specific intestinal diseases that could comorbid with varieties of negative emotional constructs, including pain-related negative emotions and trait negative emotions; however, the link between brain functions and different dimensions of negative emotions remains largely unknown. Ninety-eight patients with IBD and forty-six healthy subjects were scanned using a 3.0-T functional magnetic resonance imaging scanner. The amplitudes of low-frequency fluctuation (ALFF), regional homogeneity (ReHo), and degree centrality (DC) were used to assess resting-state brain activity. Partial least squares (PLS) correlation was employed to assess the relationship among abnormal brain activities, pain-related and trait negative emotions. Compared to controls, patients with IBD exhibited higher values of ALFF in the right anterior cingulate cortex (ACC), lower values of ALFF in the left postcentral gyrus, and higher values of DC in the bilateral ACC. Multivariate PLS correlation analysis revealed the brain scores of the ACC were correlated with pain-related negative emotions, the brain salience in the left postcentral gyrus was associated with the higher-order trait depression. These findings can enhance our comprehension of how pain-related negative emotion and trait negative emotion affect the brains of patients with IBD in distinct ways.

Objective The anterior controllable antedisplacement and fusion (ACAF) technique is a new procedure for the treatment of cervical ossification of the posterior longitudinal ligament (OPLL) that requires management of the disc adjacent to the ossification. This study describes a novel technique to reduce the number of fixed segments, namely, the “Klotski technique.” The efficacy of ACAF using the Klotski technique was compared with that of anterior cervical corpectomy and fusion (ACCF) in the treatment of OPLL with en bloc type dural ossification (DO). Methods The clinical data of 25 patients with severe OPLL and en bloc type DO who were treated by the ACAF Klotski technique or ACCF at our hospital from January 2020 to January 2022 were retrospectively analyzed. In the Klotski technique, the number of segments fused within the OPLL is limited. The antedisplacement space was designed according to the shape of the vertebrae-OPLL-DO complex (VODC). Then, the entire VODC was antedisplaced as in Klotski. Neurological function and image examination were assessed preoperatively and postoperatively. Complications associated with surgery were recorded. Results Patients were followed up for 24–36 months. There were 11 patients who were treated with ACAF and 14 patients who were treated with ACCF. At 2 weeks after surgery, the incidence of neurological deterioration was 21.4% (3 of 14) in the ACCF group and 9.1% (1 of 11) in the ACAF group. The incidence of intraoperative cerebrospinal fluid leakage (CFL) was 35.7% (5 of 14) in the ACCF group and 9.1% (1 of 11) in the ACAF group. The postoperative follow-up JOA scores of the patients in both groups were significantly better than their preoperative JOA scores (p

Ovarian cancer ranks among the deadliest gynecological malignancies, with high-grade serous adenocarcinoma (HGSA) constituting 75 % of ovarian cancer cases and accounting for 80 %–90 % of ovarian cancer-related fatalities. Accurate prognosis prediction for ovarian HGSA is of critical clinical significance. However, existing prognostic analysis methods exhibit suboptimal performance in identifying prognostically relevant pathological markers, making it challenging, even for experienced pathologists, to forecast the prognosis of ovarian HGSA patients accurately. Deep learning holds promise in enhancing prognostic prediction accuracy. However, a significant challenge in this field arises from the impracticality of directly inputting histopathology whole-slide images with millions to billions of pixels into existing deep learning networks for training and inference. To address this issue, we propose a prognostic analysis network for ovarian cancer based on hierarchical sampling and fine-grained recognition. This network comprises a two-stage hierarchical sampling sub-network, a fine-grained image recognition sub-network, and a prognostic analysis sub-network. We assess the system's performance using a pathological dataset of 450 cases of ovarian HGSA diagnosed and treated at the Pathology Department of the West China Second University Hospital of Sichuan University. Results indicate that: (1) The proposed prognostic analysis network based on two-stage hierarchical sampling sub-network can effectively analyze the histopathology whole-slide image; (2) the use of fine-grained image recognition and the introduction of clinical information can improve the performance of HGSA prognosis analysis method, with an improvement range of 4.77–10.66%; and (3) the proposed method can be used for the model analysis of pathological datasets of HGSA. Moreover, this method can be used to explore effective characteristics from the multi-modal dataset through automatic learning with prediction recall, accuracy, and precision rates of 80.0%, 81.1%, and 81.8%, respectively, underscoring its clinical potential. This study reveals the reliability and effectiveness of the proposed prognosis evaluation method of HGCA. Conclusions can help clinicians precisely evaluate the recurrence risk of patients, take the initiative to master the diagnosis and treatment, and increase the long-term survival rate of patients.

Identifying novel compound–protein interactions (CPIs) plays a pivotal role in target identification and drug discovery. Although the recent multimodal methods have achieved outstanding advances in CPI prediction, they fail to effectively learn both intra-modality and inter-modality dynamics, which limits their prediction performance. To address the limitation, we propose a novel multimodal tensor fusion CPI prediction framework, named MMTF-CPI, which contains three unimodal learning modules for structure, heterogeneous network and transcriptional profiling modalities, a tensor fusion module and a prediction module. MMTF-CPI is capable of focusing on both intra-modality and inter-modality dynamics with the tensor fusion module. We demonstrated that MMTF-CPI is superior to multiple state-of-the-art multimodal methods across seven datasets. The prediction performance of MMTF-CPI is significantly improved with the tensor fusion module compared to other fusion methods. Moreover, our case studies confirmed the practical value of MMTF-CPI in target identification. Via MMTF-CPI, we also discovered several candidate compounds for the therapy of breast cancer and non-small cell lung cancer.

Abstract Utilizing Generative Adversarial Networks (GANs) to generate 3D representations of the Terracotta Warriors offers a novel approach for the preservation and restoration of cultural heritage. Through GAN technology, we can produce complete 3D models of the Terracotta Warriors’ faces, aiding in the repair of damaged or partially destroyed figures. This paper proposes a distillation model, DIGAN, for generating 3D Terracotta Warrior faces. By extracting knowledge from StyleGAN2, we train an innovative 3D generative network. G2D, the primary component of the generative network, produces detailed and realistic 2D images. The 3D generator modularly decomposes the generation process, covering texture, shape, lighting, and pose, ultimately rendering 2D images of the Terracotta Warriors’ faces. The model enhances the learning of 3D shapes through symmetry constraints and multi-view data, resulting in high-quality 2D images that closely resemble real faces. Experimental results demonstrate that our method outperforms existing GAN-based generation methods.

Abstract Background Unicompartmental knee arthroplasty (UKA) has been proved to be a successful treatment for osteoarthritis patients. However, the stress shielding caused by mismatch in mechanical properties between human bones and artificial implants remains as a challenging issue. This study aimed to properly design a bionic porous tibial implant and evaluate its biomechanical effect in reconstructing stress transfer pathway after UKA surgery. Methods Voronoi structures with different strut sizes and porosities were designed and manufactured with Ti6Al4V through additive manufacturing and subjected to quasi-static compression tests. The Gibson-Ashby model was used to relate mechanical properties with design parameters. Subsequently, finite element models were developed for porous UKA, conventional UKA, and native knee to evaluate the biomechanical effect of tibial implant with designed structures during the stance phase. Results The internal stress distribution on the tibia plateau in the medial compartment of the porous UKA knee was found to closely resemble that of the native knee. Furthermore, the mean stress values in the medial regions of the tibial plateau of the porous UKA knee were at least 44.7% higher than that of the conventional UKA knee for all subjects during the most loading conditions. The strain shielding reduction effect of the porous UKA knee model was significant under the implant and near the load contact sites. For subject 1 to 3, the average percentages of nodes in bone preserving and building region (strain values range from 400 to 3000 μm/m) of the porous UKA knee model, ranging from 68.7 to 80.5%, were higher than that of the conventional UKA knee model, ranging from 61.6 to 68.6%. Conclusions The comparison results indicated that the tibial implant with designed Voronoi structure offered better biomechanical functionality on the tibial plateau after UKA. Additionally, the model and associated analysis provide a well-defined design process and dependable selection criteria for design parameters of UKA implants with Voronoi structures.

Abstract Enoblituzumab, an immunotherapeutic agent targeting CD276, shows both safety and efficacy in activating T cells and oligodendrocyte-like cells against various cancers. Preclinical studies and mouse models suggest that therapies targeting CD276 may outperform PD1/PD-L1 blockade. However, data from mouse models indicate a significant non-responsive population to anti-CD276 treatment, with the mechanisms of resistance still unclear. In this study, we evaluate the activity of anti-CD276 antibodies in a chemically-induced murine model of head and neck squamous cell carcinoma. Using models of induced and orthotopic carcinogenesis, we identify ITGB6 as a key gene mediating differential responses to anti-CD276 treatment. Through single-cell RNA sequencing and gene-knockout mouse models, we find that ITGB6 regulates the expression of the tumor-associated chemokine CX3CL1, which recruits and activates PF4+ macrophages that express high levels of CX3CR1. Inhibition of the CX3CL1-CX3CR1 axis suppresses the infiltration and secretion of CXCL16 by PF4+ macrophages, thereby reinvigorating cytotoxic CXCR6+ CD8+ T cells and enhancing sensitivity to anti-CD276 treatment. Further investigations demonstrate that inhibiting ITGB6 restores sensitivity to PD1 antibodies in mice resistant to anti-PD1 treatment. In summary, our research reveals a resistance mechanism associated with immune checkpoint inhibitor therapy and identifies potential targets to overcome resistance in cancer treatment.

Abstract Modern human society is burdened with the pandemic of cardiovascular and metabolic diseases. Metrnl is a widely distributed secreted protein in the body, involved in regulating glucose and lipid metabolism and maintaining cardiovascular system homeostasis. In this review, we present the predictive and therapeutic roles of Metrnl in various cardiovascular and metabolic diseases, including atherosclerosis, ischemic heart disease, cardiac remodeling, heart failure, hypertension, chemotherapy-induced myocardial injury, diabetes mellitus, and obesity.

Ground penetrating radar (GPR) is a vital non-destructive testing (NDT) technology that can be employed for detecting the backfill grouting of shield tunnels. To achieve intelligent analysis of GPR data and overcome the subjectivity of traditional data processing methods, the CatBoost & BO-TPE model was constructed for regressing the grouting thickness based on GPR waveforms. A full-scale model test and corresponding numerical simulations were carried out to collect GPR data at 400 and 900 MHz, with known backfill grouting thickness. The model test helps address the limitation of not knowing the grout body condition in actual field detection. The data were then used to create machine learning datasets. The method of feature selection was proposed based on the analysis of feature importance and the electromagnetic (EM) propagation law in mediums. The research shows that: (1) the CatBoost & BO-TPE model exhibited outstanding performance in both experimental and numerical data, achieving R2 values of 0.9760, 0.8971, 0.8808, and 0.5437 for numerical data and test data at 400 and 900 MHz. It outperformed extreme gradient boosting (XGBoost) and random forest (RF) in terms of performance in the backfill grouting thickness regression; (2) compared with the full-waveform GPR data, the feature selection method proposed in this paper can promote the performance of the model. The selected features within the 5–30 ns of the A-scan can yield the best performance for the model; (3) compared to GPR data at 900 MHz, GPR data at 400 MHz exhibited better performance in the CatBoost & BO-TPE model. This indicates that the results of the machine learning model can provide feedback for the selection of GPR parameters; (4) the application results of the trained CatBoost & BO-TPE model in engineering are in line with the patterns observed through traditional processing methods, yet they demonstrate a more quantitative and objective nature compared to the traditional method.

Abstract Swin Transformer is an important work among all the attempts to reduce the computational complexity of Transformers while maintaining its excellent performance in computer vision. Window-based patch self-attention can use the local connectivity of the image features, and the shifted window-based patch self-attention enables the communication of information between different patches in the entire image scope. Through in-depth research on the effects of different sizes of shifted windows on the patch information communication efficiency, this article proposes a Dual-Scale Transformer with double-sized shifted window attention method. The proposed method surpasses CNN-based methods such as U-Net, AttenU-Net, ResU-Net, CE-Net by a considerable margin (Approximately 3% $$\sim$$ ∼ 6% increase), and outperforms the Transformer based models single-scale Swin Transformer(SwinT)(Approximately 1% increase), on the datasets of the Kvasir-SEG, ISIC2017, MICCAI EndoVisSub-Instrument and CadVesSet. The experimental results verify that the proposed dual scale shifted window attention benefits the communication of patch information and can enhance the segmentation results to state of the art. We also implement an ablation study on the effect of the shifted window size on the information flow efficiency and verify that the dual-scale shifted window attention is the optimized network design. Our study highlights the significant impact of network structure design on visual performance, providing valuable insights for the design of networks based on Transformer architectures.

Abstract Continuous hemodynamic monitoring in a wearable means can play a crucial role in managing hypertension and preventing catastrophic cardiovascular events. In this study, we have described the fully wearable tonometric device, referred to as flexible adaptive sensing tonometry (FAST), which is capable of continuous and accurate monitoring of hemodynamic parameters within the medical-grade precision. In particular, the FAST system integrates a 1 × 8 unit array of highly sensitive and highly flexible iontronic sensing (FITS) with 1 mm spatial resolution and a closed-loop motion system. The flexible tonometric architecture has been used to determine the radial arterial position with high sensitivity and high conformability, which simplifies the biaxial searching process of the traditional applanation tonometry into a highly efficient uniaxial applanation while keeping the medical-precision assessments. Importantly, a self-calibration algorithm can be automatically implemented during the applanation process, from which the intra-arterial blood pressure wave can be continuously predicted within the medical-grade precision, and subsequently, multi-parametric hemodynamic analysis can be performed in real-time. Experimental validations on health volunteers have demonstrated that the FAST measurements are all within the required accuracy of the clinical standards for continuous pulse wave assessments, blood pressure monitoring as well as other key hemodynamic parameter evaluations. Therefore, the FAST system, by integrating the flexible iontronic sensing array, provides a real-time, medical-grade hemodynamic monitoring solution in a continuously wearable manner, from which remote patient-centered monitoring can be delivered with both medical precision and convenience.

Objective To investigate the developmental defects caused by knockdown of best1 gene in zebrafish as a model for a subtype of craniovertebral junction (CVJ) malformation. Methods Two antisense morpholinos (MOs) were designed targeting zebrafish best1 to block translation (ATG-MO) or to disrupt splicing (I3E4-MO). MOs were microinjected into fertilized one-cell embryos. Efficacy of splicing MO was confirmed by reverse transcription-polymerase chain reaction. Phenotypes were analyzed and quantified by microscopy at multiple developmental stages. Neuronal outgrowth was assessed in transgenic zebrafish expressing green fluorescent protein in neurons. Skeletal ossification was visualized by Calcein staining. Results Knockdown of best1 resulted in zebrafish embryos with shorter body length, curved axis, low survival rate, microcephaly, reduced eye size, smaller head and brain, impaired neuronal outgrowth, and reduced ossification of craniofacial and vertebral bone. Conclusion Best1 gene plays critical roles in ophthalmologic, neurological and skeletal development in zebrafish. A patient with a premature stop codon in BEST1 gene exhibited similar phenotypes, implying a subtype of CVJ malformation.

Abstract Endocrine resistance poses a significant clinical challenge for patients with hormone receptor-positive and human epithelial growth factor receptor 2-negative (HR + HER2−) breast cancer. Dysregulation of estrogen receptor (ER) and ERBB signaling pathways is implicated in resistance development; however, the integration of these pathways remains unclear. While SMAD4 is known to play diverse roles in tumorigenesis, its involvement in endocrine resistance is poorly understood. Here, we investigate the role of SMAD4 in acquired endocrine resistance in HR + HER2− breast cancer. Genome-wide CRISPR screening identifies SMAD4 as a regulator of 4-hydroxytamoxifen (OHT) sensitivity in T47D cells. Clinical data analysis reveals downregulated SMAD4 expression in breast cancer tissues, correlating with poor prognosis. Following endocrine therapy, SMAD4 expression is further suppressed. Functional studies demonstrate that SMAD4 depletion induces endocrine resistance in vitro and in vivo by enhancing ER and ERBB signaling. Concomitant inhibition of ER and ERBB signaling leads to aberrant autophagy activation. Simultaneous inhibition of ER, ERBB, and autophagy pathways synergistically impacts SMAD4-depleted cells. Our findings unveil a mechanism whereby endocrine therapy-induced SMAD4 downregulation drives acquired resistance by integrating ER and ERBB signaling and suggest a rational treatment strategy for endocrine-resistant HR + HER2− breast cancer patients.

Abstract Background Stress hyperglycemia occurs frequently in patients following acute myocardial infarction (AMI) and may aggravate myocardial stiffness, but relevant evidence is still lacking. Accordingly, this study aimed to examine the impact of admission stress hyperglycemia on left ventricular (LV) myocardial deformation in patients following AMI. Methods A total of 171 patients with first AMI (96 with normoglycemia and 75 with hyperglycemia) underwent cardiac magnetic resonance (CMR) examination were included. AMI patients were classified according to admission blood glucose level (aBGL): < 7.8 mmol/L (n = 96), 7.8–11.1 mmol/L (n = 41) and ≥ 11.1 mmol/L (n = 34). LV strains, including global radial/circumferential/longitudinal peak strain (PS)/peak systolic strain rate (PSSR)/peak diastolic strain rate (PDSR), were measured and compared between groups. Further, subgroup analyses were separately conducted for AMI patients with and without diabetes. Multivariate analysis was employed to assess the independent association between aBGL and LV global PS in AMI patients. Results LV global PS, PSSR and PDSR were decreased in radial, circumferential and longitudinal directions in hyperglycemic AMI patients compared with normoglycemic AMI patients (all P

Abstract Cryptophytes are ancestral photosynthetic organisms evolved from red algae through secondary endosymbiosis. They have developed alloxanthin-chlorophyll a/c2-binding proteins (ACPs) as light-harvesting complexes (LHCs). The distinctive properties of cryptophytes contribute to efficient oxygenic photosynthesis and underscore the evolutionary relationships of red-lineage plastids. Here we present the cryo-electron microscopy structure of the Photosystem II (PSII)–ACPII supercomplex from the cryptophyte Chroomonas placoidea. The structure includes a PSII dimer and twelve ACPII monomers forming four linear trimers. These trimers structurally resemble red algae LHCs and cryptophyte ACPI trimers that associate with Photosystem I (PSI), suggesting their close evolutionary links. We also determine a Chl a-binding subunit, Psb-γ, essential for stabilizing PSII–ACPII association. Furthermore, computational calculation provides insights into the excitation energy transfer pathways. Our study lays a solid structural foundation for understanding the light-energy capture and transfer in cryptophyte PSII–ACPII, evolutionary variations in PSII–LHCII, and the origin of red-lineage LHCIIs.

Abstract Background Several immune checkpoint inhibitors (ICIs) have been linked to the occurrence of Vogt-Koyanagi-Harada disease (VKHD)-like uveitis. Among the ICIs, there has been no report of immune-related adverse events (irAEs) caused by a new programmed death protein-1(PD-1) monoclonal antibody (Toripalimab). Case presentation This paper presents a case of VKHD-like uveitis that arose following Toripalimab therapy for urothelial cancer of the bladder, and the patient experienced symptoms 10 days after the final dosage of 20 months of medication treatment. This patient with bladder uroepithelial carcinoma had severe binocular acute panuveitis with exudative retinal detachment after receiving Toripalimab therapy. Binocular VKHD-like uveitis was suggested as a diagnosis. Both eyes recovered after discontinuing immune checkpoint inhibitors and local and systemic corticosteroid treatment. Conclusions This report suggests that VKHD-like uveitis can also occur in patients receiving novel PD-1 antibodies and the importance of paying attention to eye complications in patients receiving treatment over a long period.

Abstract Background Depression and sleep disturbances are associated with increased risks of various diseases and mortality, but their impacts on mortality in cancer survivors remain unclear. The objective of this study was to characterize the independent and joint associations of depressive symptoms and sleep disturbances with mortality outcomes in cancer survivors. Methods This population-based prospective cohort study included cancer survivors aged ≥ 20 years (n = 2947; weighted population, 21,003,811) from the National Health and Nutrition Examination Survey (NHANES) 2007–2018 cycles. Depressive symptoms and sleep disturbances were self-reported. Depressive symptoms were assessed using the Patient Health Questionnaire 9 (PHQ-9). Death outcomes were determined by correlation with National Death Index records through December 31, 2019. Primary outcomes included all-cause, cancer-specific, and noncancer mortality. Results During the median follow-up of 69 months (interquartile range, 37–109 months), 686 deaths occurred: 240 participants died from cancer, 146 from heart disease, and 300 from other causes. Separate analyses revealed that compared with a PHQ-9 score (0–4), a PHQ-9 score (5–9) was associated with a greater risk of all-cause mortality (hazard ratio [HR], 1.28; 95% CI, 1.03–1.59), and a PHQ-9 score (≥ 10) was associated with greater risk of all-cause mortality (HR, 1.37; 95% CI, 1.04–1.80) and noncancer mortality (HR, 1.45; 95% CI, 1.01–2.10). Single sleep disturbances were not associated with mortality risk. In joint analyses, the combination of a PHQ-9 score ≥ 5 and no sleep disturbances, but not sleep disturbances, was associated with increased risks of all-cause mortality, cancer-specific mortality, and noncancer mortality. Specifically, compared with individuals with a PHQ-9 score of 0–4 and no sleep disturbances, HRs for all-cause mortality and noncancer mortality in individuals with a PHQ-9 score of 5–9 and no sleep disturbances were 1.72 (1.21–2.44) and 1.69 (1.10–2.61), respectively, and 2.61 (1.43–4.78) and 2.77 (1.27–6.07), respectively, in individuals with a PHQ-9 score ≥ 10 and no sleep disturbances; HRs for cancer-specific mortality in individuals with a PHQ-9 score ≥ 5 and no sleep disturbances were 1.95 (1.16–3.27). Conclusions Depressive symptoms were linked to a high risk of mortality in cancer survivors. The combination of a PHQ-9 score (≥ 5) and an absence of self-perceived sleep disturbances was associated with greater all-cause mortality, cancer-specific mortality, and noncancer mortality risks, particularly in individuals with a PHQ-9 score (≥ 10).

Abstract Brain metastases (BM) are common in patients with epidermal growth factor receptor (EGFR)‐mutant non‐small cell lung cancer (NSCLC) and confer poor prognoses. Zorifertinib (AZD3759), an EGFR‐tyrosine kinase inhibitor (TKI) with high blood‐brain barrier penetration, has previously demonstrated promising systemic and intracranial antitumor activity in phase 1–3 studies. This is the first report of a patient with EGFR‐mutant (exon 21 L858R) NSCLC and symptomatic untreated multiple BM who achieved a long overall survival (OS) of more than 65 months after sequential treatment with zorifertinib and a third‐generation EGFR‐TKI. This new treatment paradigm offers a new treatment option and deserves further clinical exploration to prolong OS of patients with EGFR‐mutant NSCLC and untreated multiple BM.

PurposeThis research aimed to create a machine learning model for clinical-radiomics that utilizes unenhanced computed tomography images to assess the likelihood of malignant cerebral edema (MCE) in individuals suffering from acute ischemic stroke (AIS).MethodsThe research included 179 consecutive patients with AIS from two different hospitals. These patients were randomly assigned to training (n = 143) and validation (n = 36) sets with an 8:2 ratio. Using 3DSlicer software, the radiomics features of regions impacted by infarction were derived from unenhanced CT scans. The radiomics features linked to MCE were pinpointed through a consistency test, Student’s t test and the least absolute shrinkage and selection operator (LASSO) method for selecting features. Clinical parameters associated with MCE were also identified. Subsequently, machine learning models were constructed based on clinical, radiomics, and clinical-radiomics. Ultimately, the efficacy of these models was evaluated by measuring the operating characteristics of the subjects through their area under the curve (AUCs).ResultsLogistic regression (LR) was found to be the most effective machine learning algorithm, for forecasting the MCE. In the training and validation cohorts, the AUCs of clinical model were 0.836 and 0.773, respectively, for differentiating MCE patients; the AUCs of radiomics model were 0.849 and 0.818, respectively; the AUCs of clinical and radiomics model were 0.912 and 0.916, respectively.ConclusionThis model can assist in predicting MCE after acute ischemic stroke and can provide guidance for clinical treatment and prognostic assessment.

PurposeMachine learning (ML) models were constructed according to non-contrast computed tomography (NCCT) images as well as clinical and laboratory information to assess risk stratification for the occurrence of hemorrhagic transformation (HT) in acute ischemic stroke (AIS) patients.MethodsA retrospective cohort was constructed with 180 AIS patients who were diagnosed at two centers between January 2019 and October 2023 and were followed for HT outcomes. Patients were analyzed for clinical risk factors for developing HT, infarct texture features were extracted from NCCT images, and the radiomics score (Rad-score) was calculated. Then, five ML models were established and evaluated, and the optimal ML algorithm was used to construct the clinical, radiomics, and clinical-radiomics models. Receiver operating characteristic (ROC) curves were used to compare the performance of the three models in predicting HT.ResultsBased on the outcomes of the AIS patients, 104 developed HT, and the remaining 76 had no HT. The HT group consisted of 27 hemorrhagic infarction (HI) and 77 parenchymal-hemorrhage (PH). Patients with HT had a greater neutrophil-to-lymphocyte ratio (NLR), baseline National Institutes of Health Stroke Scale (NIHSS) score, infarct volume, and Rad-score and lower Alberta stroke program early CT score (ASPECTS) (all p 0.05), with an overall accuracy of 79.5%. Moreover, this model performed reliably in predicting the PH and HI subcategories, with accuracies of 82.9 and 92.9%, respectively.ConclusionML models based on clinical and NCCT radiomics characteristics can be used for early risk evaluation of HT development in AIS patients and show great potential for clinical precision in treatment and prognostic assessment.

Abstract Objectives The simplified endoscopic score of Crohn’s disease (SES-CD) is the gold standard for quantitatively evaluating Crohn’s disease (CD) activity but is invasive. This study aimed to develop and validate a machine learning (ML) model based on dual-energy CT enterography (DECTE) to noninvasively evaluate CD activity. Methods We evaluated the activity in 202 bowel segments of 46 CD patients according to the SES-CD score and divided the segments randomly into training set and testing set at a ratio of 7:3. Least absolute shrinkage and selection operator (LASSO) was used for feature selection, and three models based on significant parameters were established based on logistic regression. Model performance was evaluated using receiver operating characteristic (ROC), calibration, and clinical decision curves. Results There were 110 active and 92 inactive bowel segments. In univariate analysis, the slope of spectral curve in the venous phases (λHU-V) has the best diagnostic performance, with an area under the ROC curve (AUC) of 0.81 and an optimal threshold of 1.975. In the testing set, the AUC of the three models established by the 7 variables to differentiate CD activity was 0.81–0.87 (DeLong test p value was 0.071–0.766, p > 0.05), and the combined model had the highest AUC of 0.87 (95% confidence interval (CI): 0.779–0.959). Conclusions The ML model based the DECTE can feasibly evaluate CD activity, and DECTE parameters provide a quantitative analysis basis for evaluating specific bowel activities in CD patients. Critical relevance statement The machine learning model based on dual-energy computed tomography enterography can be used for evaluating Crohn’s disease activity noninvasively and quantitatively. Key Points Dual-energy CT parameters are related to Crohn’s disease activity. Three machine learning models effectively evaluated Crohn’s disease activity. Combined models based on conventional and dual-energy CT have the best performance. Graphical Abstract

Abstract Background In the aftermath of the COVID-19 pandemic, there has been a surge in human metapneumovirus (HMPV) transmission, surpassing pre-epidemic levels. We aim to elucidate the clinical and epidemiological characteristics of HMPV infections in the post-COVID-19 pandemic era. Methods In this retrospective single-center study, participants diagnosed with laboratory confirmed HMPV infection through Targeted Next Generation Sequencing were included. The study encompassed individuals admitted to Henan Children's Hospital between April 29 and June 5, 2023. Demographic information, clinical records, and laboratory indicators were analyzed. Results Between April 29 and June 5, 2023, 96 pediatric patients were identified as infected with HMPV with a median age of 33.5 months (interquartile range, 12 ~ 48 months). The majority (87.5%) of infected children were under 5 years old. Notably, severe cases were statistically younger. Predominant symptoms included fever (81.3%) and cough (92.7%), with wheezing more prevalent in the severe group (56% vs 21.1%). Coinfection with other viruses was observed in 43 patients, with Epstein–Barr virus (EBV) (15.6%) or human rhinovirus A (HRV type A) (12.5%) being the most common. Human respiratory syncytial virus (HRSV) coinfection rate was significantly higher in the severe group (20% vs 1.4%). Bacterial coinfection occurred in 74 patients, with Haemophilus influenzae (Hin) and Streptococcus pneumoniae (SNP) being the most prevalent (52.1% and 41.7%, respectively). Severe patients demonstrated evidence of multi-organ damage. Noteworthy alterations included lower concentration of IL-12p70, decreased lymphocytes percentages, and elevated B lymphocyte percentages in severe cases, with statistical significance. Moreover, most laboratory indicators exhibited significant changes approximately 4 to 5 days after onset. Conclusions Our data systemically elucidated the clinical and epidemiological characteristics of pediatric patients with HMPV infection, which might be instructive to policy development for the prevention and control of HMPV infection and might provide important clues for future HMPV research endeavors.

Abstract Osteophytes are frequently observed in elderly people and most commonly appear at the anterior edge of the cervical and lumbar vertebrae body. The anterior osteophytes keep developing and will lead to neck/back pain over time. In clinical practice, the accurate measurement of the anterior osteophyte length and the understanding of the temporal progression of anterior osteophyte growth are of vital importance to clinicians for effective treatment planning. This study proposes a new measuring method using the osteophyte ratio index to quantify anterior osteophyte length based on lateral radiographs. Moreover, we develop a continuous stochastic degradation model with time-related functions to characterize the anterior osteophyte formation and growth process on cervical and lumbar vertebrae over time. Follow-up data of anterior osteophytes up to 9 years are obtained for measurement and model validation. The agreement test indicates excellent reproducibility for our measuring method. The proposed model accurately fits the osteophyte growth paths. The model predicts the mean time to onset of pain and obtained survival function of the degenerative vertebrae. This research opens the door to future quantification and mathematical modeling of the anterior osteophyte growth on human cervical and lumbar vertebrae. The measured follow-up data is shared for future studies.

Abstract Head and neck squamous cell carcinoma (HNSCC) is characterized by high recurrence or distant metastases rate and the prognosis is challenging. There is mounting evidence that tumor-infiltrating B cells (TIL-Bs) have a crucial, synergistic role in tumor control. However, little is known about the role TIL-Bs play in immune microenvironment and the way TIL-Bs affect the outcome of immune checkpoint blockade. Using single-cell RNA sequencing (scRNA-seq) data from the Gene Expression Omnibus (GEO) database, the study identified distinct gene expression patterns in TIL-Bs. HNSCC samples were categorized into TIL-Bs inhibition and TIL-Bs activation groups using unsupervised clustering. This classification was further validated with TCGA HNSCC data, correlating with patient prognosis, immune cell infiltration, and response to immunotherapy. We found that the B cells activation group exhibited a better prognosis, higher immune cell infiltration, and distinct immune checkpoint levels, including elevated PD-L1. A prognostic model was also developed and validated, highlighting four genes as potential biomarkers for predicting survival outcomes in HNSCC patients. Overall, this study provides a foundational approach for B cells-based tumor classification in HNSCC, offering insights into targeted treatment and immunotherapy strategies.

Abstract Due to the antiquity and difficulty of excavation, the Terracotta Warriors have suffered varying degrees of damage. To restore the cultural relics to their original appearance, utilizing point clouds to repair damaged Terracotta Warriors has always been a hot topic in cultural relic protection. The output results of existing methods in point cloud completion often lack diversity. Probability-based models represented by Denoising Diffusion Probabilistic Models have recently achieved great success in the field of images and point clouds and can output a variety of results. However, one drawback of diffusion models is that too many samples result in slow generation speed. Toward this issue, we propose a new neural network for Terracotta Warriors fragments completion. During the reverse diffusion stage, we initially decrease the number of sampling steps to generate a coarse result. This preliminary outcome undergoes further refinement through a multi-scale refine network. Additionally, we introduce a novel approach called Partition Attention Sampling to enhance the representation capabilities of features. The effectiveness of the proposed model is validated in the experiments on the real Terracotta Warriors dataset and public dataset. The experimental results conclusively demonstrate that our model exhibits competitive performance in comparison to other existing models.

Abstract Background The regulatory role of N6-methyladenosine (m6A) modification in the onset and progression of cancer has garnered increasing attention in recent years. However, the specific role of m6A modification in pulmonary metastasis of colorectal cancer remains unclear. Methods This study identified differential m6A gene expression between primary colorectal cancer and its pulmonary metastases using transcriptome sequencing and immunohistochemistry. We investigated the biological function of METTL3 gene both in vitro and in vivo using assays such as CCK-8, colony formation, wound healing, EDU, transwell, and apoptosis, along with a BALB/c nude mouse model. The regulatory mechanisms of METTL3 in colorectal cancer pulmonary metastasis were studied using methods like methylated RNA immunoprecipitation quantitative reverse transcription PCR, RNA stability analysis, luciferase reporter gene assay, Enzyme-Linked Immunosorbent Assay, and quantitative reverse transcription PCR. Results The study revealed high expression of METTL3 and YTHDF1 in the tumors of patients with pulmonary metastasis of colorectal cancer. METTL3 promotes epithelial-mesenchymal transition in colorectal cancer by m6A modification of SNAIL mRNA, where SNAIL enhances the secretion of CXCL2 through the NF-κB pathway. Additionally, colorectal cancer cells expressing METTL3 recruit M2-type macrophages by secreting CXCL2. Conclusion METTL3 facilitates pulmonary metastasis of colorectal cancer by targeting the m6A-Snail-CXCL2 axis to recruit M2-type immunosuppressive macrophages. This finding offers new research directions and potential therapeutic targets for colorectal cancer treatment.

Abstract Utrophin (UTRN), known as a tumor suppressor, potentially regulates tumor development and the immune microenvironment. However, its impact on breast cancer’s development and treatment remains unstudied. We conducted a thorough examination of UTRN using both bioinformatic and in vitro experiments in this study. We discovered UTRN expression decreased in breast cancer compared to standard samples. High UTRN expression correlated with better prognosis. Drug sensitivity tests and RT-qPCR assays revealed UTRN’s pivotal role in tamoxifen resistance. Furthermore, the Kruskal–Wallis rank test indicated UTRN’s potential as a valuable diagnostic biomarker for breast cancer and its utility in detecting T stage of breast cancer. Additionally, our results demonstrated UTRN’s close association with immune cells, inhibitors, stimulators, receptors, and chemokines in breast cancer (BRCA). This research provides a novel perspective on UTRN’s role in breast cancer’s prognostic and therapeutic value. Low UTRN expression may contribute to tamoxifen resistance and a poor prognosis. Specifically, UTRN can improve clinical decision-making and raise the diagnosis accuracy of breast cancer.

Abstract Interplay between innate and adaptive immune cells is important for the antitumor immune response. However, the tumor microenvironment may turn immune suppressive, and tumor associated macrophages are playing a role in this transition. Here, we show that CD276, expressed on tumor-associated macrophages (TAM), play a role in diminishing the immune response against tumors. Using a model of tumors induced by N-butyl-N-(4-hydroxybutyl) nitrosamine in BLCA male mice we show that genetic ablation of CD276 in TAMs blocks efferocytosis and enhances the expression of the major histocompatibility complex class II (MHCII) of TAMs. This in turn increases CD4 + and cytotoxic CD8 + T cell infiltration of the tumor. Combined single cell RNA sequencing and functional experiments reveal that CD276 activates the lysosomal signaling pathway and the transcription factor JUN to regulate the expression of AXL and MerTK, resulting in enhanced efferocytosis in TAMs. Proving the principle, we show that simultaneous blockade of CD276 and PD-1 restrain tumor growth better than any of the components as a single intervention. Taken together, our study supports a role for CD276 in efferocytosis by TAMs, which is potentially targetable for combination immune therapy.

Abstract Creating microenvironments that mimic an enzyme’s active site is a critical aspect of supramolecular confined catalysis. In this study, we employ the commonly used chiral 1,1’-bi-2-naphthol (BINOL) phosphates as subcomponents to construct supramolecular hollow nanotube in an aqueous medium through non-covalent intermolecular recognition and arrangement. The hexagonal nanotubular structure is characterized by various techniques, including X-ray, NMR, ESI-MS, AFM, and TEM, and is confirmed to exist in a homogeneous aqueous solution stably. The nanotube’s length in solution depends on the concentration of chiral BINOL-phosphate as a monomer. Additionally, the assembled nanotube can accelerate the rate of the 3-aza-Cope rearrangement reaction by up to 85-fold due to the interior confinement effect. Based on the detailed kinetic and thermodynamic analyses, we propose that the chain-like substrates are constrained and pre-organized into a reactive chair-like conformation, which stabilizes the transition state of the reaction in the confined nanospace of the nanotube. Notably, due to the restricted conformer with less degrees of freedom, the entropic barrier is significantly reduced compared to the enthalpic barrier, resulting in a more pronounced acceleration effect.

Abstract It is very challenging to accurately segment smoke images because smoke has some adverse vision characteristics, such as anomalous shapes, blurred edges, and translucency. Existing methods cannot fully focus on the texture details of anomalous shapes and blurred edges simultaneously. To solve these problems, a Dense Multi‐scale context and Asymmetric pooling Embedding Network (DMAENet) is proposed to model the smoke edge details and anomalous shapes for smoke segmentation. To capture the feature information from different scales, a Dense Multi‐scale Context Module (DMCM) is proposed to further enhance the feature representation capability of our network under the help of asymmetric convolutions. To efficiently extract features for long‐shaped objects, the authors use asymmetric pooling to propose an Asymmetric Pooling Enhancement Module (APEM). The vertical and horizontal pooling methods are responsible for enhancing features of irregular objects. Finally, a Feature Fusion Module (FFM) is designed, which accepts three inputs for improving performance. Low and high‐level features are fused by pixel‐wise summing, and then the summed feature maps are further enhanced in an attention manner. Experimental results on synthetic and real smoke datasets validate that all these modules can improve performance, and the proposed DMAENet obviously outperforms existing state‐of‐the‐art methods.

Abstract Symbiodinium are the photosynthetic endosymbionts for corals and play a vital role in supplying their coral hosts with photosynthetic products, forming the nutritional foundation for high-yield coral reef ecosystems. Here, we determine the cryo-electron microscopy structure of Symbiodinium photosystem I (PSI) supercomplex with a PSI core composed of 13 subunits including 2 previously unidentified subunits, PsaT and PsaU, as well as 13 peridinin-Chl a/c-binding light-harvesting antenna proteins (AcpPCIs). The PSI–AcpPCI supercomplex exhibits distinctive structural features compared to their red lineage counterparts, including extended termini of PsaD/E/I/J/L/M/R and AcpPCI-1/3/5/7/8/11 subunits, conformational changes in the surface loops of PsaA and PsaB subunits, facilitating the association between the PSI core and peripheral antennae. Structural analysis and computational calculation of excitation energy transfer rates unravel specific pigment networks in Symbiodinium PSI–AcpPCI for efficient excitation energy transfer. Overall, this study provides a structural basis for deciphering the mechanisms governing light harvesting and energy transfer in Symbiodinium PSI–AcpPCI supercomplexes adapted to their symbiotic ecosystem, as well as insights into the evolutionary diversity of PSI–LHCI among various photosynthetic organisms.

Abstract Background Currently, noninvasive imaging techniques and circulating biomarkers are still insufficient to accurately assess carotid plaque stability, and an in-depth understanding of the molecular mechanisms that contribute to plaque instability is still lacking. Methods We established a clinical study cohort containing 182 patients with carotid artery stenosis. After screening, 39 stable and 49 unstable plaques were included in the discovery group, and quantitative proteomics analysis based on data independent acquisition was performed for these plaque samples. Additionally, 35 plaques were included in the validation group to validate the proteomics results by immunohistochemistry analysis. Results A total of 397 differentially expressed proteins were identified in stable and unstable plaques. These proteins are primarily involved in ferroptosis and lipid metabolism-related functions and pathways. Plaque validation results showed that ferroptosis- and lipid metabolism-related proteins had different expression trends in stable plaques versus unstable fibrous cap regions and lipid core regions. Ferroptosis- and lipid metabolism-related mechanisms in plaque stability were discussed. Conclusions Our results may provide a valuable strategy for revealing the mechanisms affecting plaque stability and will facilitate the discovery of specific biomarkers to broaden the therapeutic scope.

Electromagnetic metamaterial absorbers (MMAs), with pivotal roles in applications such as stealth and communication, have revolutionised the field of absorbing materials by offering unique characteristics unattainable by natural materials through artificially designed unit cell structures. Although many MMAs have been successfully prepared through traditional micro/nano fabrication processes, inherent limitations persist. 3D printing, or additive manufacturing, offers unprecedented design freedom and streamlined production processes for MMAs. This review provides a comprehensive examination of 3D-printed MMAs. It first introduces the fundamental concepts and history of MMAs with a brief description of traditional techniques for MMA fabrication. Subsequently, it elucidates mechanisms governing the loss and absorption of electromagnetic MMAs and traditional absorbing materials, shedding light on the design principles. Next, this review centres on 3D printing modalities employed to fabricate MMAs by categorising the processes into one-step and two-step manufacturing that leverage the unique advantages of 3D printing. Moreover, it also delves into innovative 4D-printed MMAs, showcasing the potential of dynamically adjustable MMAs. In the end, potential future research directions are discussed by taking into consideration of current concerns. This review underscores the transformative impact of 3D-printed MMAs, paving a new avenue for future innovations in electromagnetic engineering.

ObjectiveTo compare the effectiveness of radiomic features based on 18F-FDG PET/CT images within (intranodular) and around (perinodular) lung nodules/masses in distinguishing between lung adenocarcinoma and pulmonary granulomas.MethodsFor this retrospective study, 18F-FDG PET/CT images were collected for 228 patients. Patients diagnosed with lung adenocarcinoma (n = 156) or granulomas (n = 72) were randomly assigned to a training (n = 159) and validation (n = 69) groups. The volume of interest (VOI) of intranodular, perinodular (1–5 voxels, termed Lesion_margin1 to Lesion_margin5) and total area (intra- plus perinodular region, termed Lesion_total1 to Lesion_total5) on PET/CT images were delineated using PETtumor and Marge tool of segmentation editor. A total of 1,037 radiomic features were extracted separately from PET and CT images, and the optimal features were selected to develop radiomic models. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC).ResultsGood and acceptable performance was, respectively, observed in the training (AUC = 0.868, p < 0.001) and validation (AUC = 0.715, p = 0.004) sets for the intranodular radiomic model. Among the perinodular models, the Lesion_margin2 model demonstrated the highest AUC in both sets (0.883 and 0.616, p < 0.001 and p = 0.122). Similarly, in terms of total models, Lesion_total2 model was found to outperform others in the training (AUC = 0.879, p < 0.001) and validation (AUC = 0.742, p = 0.001) sets, slightly surpassing the intranodular model.ConclusionWhen intra- and perinodular radiomic features extracted from the immediate vicinity of the nodule/mass up to 2 voxels distance on 18F-FDG PET/CT imaging are combined, improved differential diagnostic performance in distinguishing between lung adenocarcinomas and granulomas is achieved compared to the intra- and perinodular radiomic features alone.

Rice is the leading global staple crop. Low temperatures pose negative impacts on rice’s optimal growth and development. Rice cultivars acclimating to low temperatures exhibited improved seedling emergence under direct-seeded sowing conditions, yet little is known about the genes that regulate germination at low temperatures (LTG). In this research investigation, we’ve performed whole genome sequencing for the 273 rice plant materials. Using the best linear unbiased prediction (BLUP) values for each rice material, we identified 7 LTG-related traits and performed the efficient genetic analysis and genome-wide association study (GWAS). As a result of this, 95 quantitative trait loci (QTLs) and 1001 candidate genes associated with LTG in rice were identified. Haplotype analysis and functional annotation of the candidate genes resulted in the identification of three promising candidate genes (LOC_Os08g30520 for regulating LTG4 and LTG5, LOC_Os10g02625 for regulating LTG6, LTg7 and LTG8, and LOC_Os12g31460 for regulating LTG7, LTg8 and LTG9) involving in the regulation of LTG in rice. This research provides a solid foundation for addressing the LTG issue in rice and will be valuable in future direct-seeded rice breeding programs.

Non-alcoholic fatty liver disease (NAFLD) is increasingly recognized as a global public health concern characterized by excessive lipid accumulation in the liver. Ferulic acid (FA), a common foodborne phenolic acid, has been shown to reduce lipid accumulation by activating adenosine monophosphate-activated protein kinase (AMPK) and suppressing the expression of sterol regulatory element-binding protein-1 (SREBP-1). In this study, we synthesized a new class of (E)-3-(3-methoxy-4-substituted phenyl)-acrylic acid derivatives as AMPK activators. Among these derivatives, compound S17 demonstrated a more potent inhibitory effect on lipid accumulation compared to its counterparts. Specifically, S17 notably diminished intracellular triglyceride (TG) levels and the activities of alanine aminotransferase (ALT) and aspartate aminotransferase (AST), which are indicators of liver function. In addition, S17 curtailed intracellular lipid accumulation by activating the AMPK signaling pathway and down-regulating the protein expression of SREBP-1 in free fatty acid (FFA)-induced HepG2 cells. Therefore, findings from this study strongly suggest that compound S17 may serve as a promising therapeutic candidate for the treatment of NAFLD.

The starter diet for Japanese eel (Anguilla japonica) has always been a difficult problem for the realization of total artificial reproduction. Therefore, this research analyzed the nutritional composition of artificially fertilized eggs, and transcriptome of samples from early hatchlings of fry to better understand nutrients requirements. The composition of crude lipid and crude protein in fertilized eggs was 7.24% ± 0.32% and 10.56% ± 0.41%, respectively. Seven kinds of essential amino acids (EAA) were detected but took a comparable lower content (3.19%) than other marine fish eggs. We randomly assembled 265.74 million clean reads and identified 1751 differentially expressed genes (DEGs) (P < 0.01) from pre-leptocephalus larvae. A total of 23 KEGG pathways related to the digestive and metabolic system were detected. Genes related to the secretion pathway of saliva, pancreatic juice and other digestive juices were significantly changed. Transcriptome analysis showed that as larvae aged, glycolytic metabolism and the transcription level of hexokinase (HK) increased significantly (day 0 to 12). This study will facilitate future studies on the nutrition of A. japonica larvae and other biological traits to reproductive research.

BackgroundThe efficacy of large language models (LLMs) in domain-specific medicine, particularly for managing complex diseases such as osteoarthritis (OA), remains largely unexplored. ObjectiveThis study focused on evaluating and enhancing the clinical capabilities and explainability of LLMs in specific domains, using OA management as a case study. MethodsA domain-specific benchmark framework was developed to evaluate LLMs across a spectrum from domain-specific knowledge to clinical applications in real-world clinical scenarios. DocOA, a specialized LLM designed for OA management integrating retrieval-augmented generation and instructional prompts, was developed. It can identify the clinical evidence upon which its answers are based through retrieval-augmented generation, thereby demonstrating the explainability of those answers. The study compared the performance of GPT-3.5, GPT-4, and a specialized assistant, DocOA, using objective and human evaluations. ResultsResults showed that general LLMs such as GPT-3.5 and GPT-4 were less effective in the specialized domain of OA management, particularly in providing personalized treatment recommendations. However, DocOA showed significant improvements. ConclusionsThis study introduces a novel benchmark framework that assesses the domain-specific abilities of LLMs in multiple aspects, highlights the limitations of generalized LLMs in clinical contexts, and demonstrates the potential of tailored approaches for developing domain-specific medical LLMs.

Abstract BackgroundLarge language models (LLMs) have achieved great progress in natural language processing tasks and demonstrated the potential for use in clinical applications. Despite their capabilities, LLMs in the medical domain are prone to generating hallucinations (not fully reliable responses). Hallucinations in LLMs’ responses create substantial risks, potentially threatening patients’ physical safety. Thus, to perceive and prevent this safety risk, it is essential to evaluate LLMs in the medical domain and build a systematic evaluation. ObjectiveWe developed a comprehensive evaluation system, MedGPTEval, composed of criteria, medical data sets in Chinese, and publicly available benchmarks. MethodsFirst, a set of evaluation criteria was designed based on a comprehensive literature review. Second, existing candidate criteria were optimized by using a Delphi method with 5 experts in medicine and engineering. Third, 3 clinical experts designed medical data sets to interact with LLMs. Finally, benchmarking experiments were conducted on the data sets. The responses generated by chatbots based on LLMs were recorded for blind evaluations by 5 licensed medical experts. The evaluation criteria that were obtained covered medical professional capabilities, social comprehensive capabilities, contextual capabilities, and computational robustness, with 16 detailed indicators. The medical data sets include 27 medical dialogues and 7 case reports in Chinese. Three chatbots were evaluated: ChatGPT by OpenAI; ERNIE Bot by Baidu, Inc; and Doctor PuJiang (Dr PJ) by Shanghai Artificial Intelligence Laboratory. ResultsDr PJ outperformed ChatGPT and ERNIE Bot in the multiple-turn medical dialogues and case report scenarios. Dr PJ also outperformed ChatGPT in the semantic consistency rate and complete error rate category, indicating better robustness. However, Dr PJ had slightly lower scores in medical professional capabilities compared with ChatGPT in the multiple-turn dialogue scenario. ConclusionsMedGPTEval provides comprehensive criteria to evaluate chatbots by LLMs in the medical domain, open-source data sets, and benchmarks assessing 3 LLMs. Experimental results demonstrate that Dr PJ outperforms ChatGPT and ERNIE Bot in social and professional contexts. Therefore, such an assessment system can be easily adopted by researchers in this community to augment an open-source data set.

Micro(nano)plastic, as a new type of environmental pollutant, have become a potential threat to the life and health of various stages of biology. However, it is not yet clear whether they will affect brain development in the fetal stage. Therefore, this study aims to explore the potential effects of nanoplastics on the development of fetal rat brains. To assess the allocation of NPs (25 nm and 50 nm) in various regions of the fetal brain, pregnant rats were exposed to concentrations (50, 10, 2.5, and 0.5 mg/kg) of PS-NPs. Our results provided evidence of the transplacental transfer of PS-NPs to the fetal brain, with a prominent presence observed in several cerebral regions, notably the cerebellum, hippocampus, striatum, and prefrontal cortex. This distribution bias might be linked to the developmental sequence of each brain region. Additionally, we explored the influence of prenatal exposure on the myelin development of the cerebellum, given its the highest PS-NP accumulation in offspring. Compared with control rats, PS-NPs exposure caused a significant reduction in myelin basic protein (MBP) and myelin oligodendrocyte glycoprotein (MOG) expression, a decrease in myelin thickness, an increase in cell apoptosis, and a decline in the oligodendrocyte population. These effects gave rise to motor deficits. In conclusion, our results identified the specific distribution of NPs in the fetal brain following prenatal exposure and revealed that prenatal exposure to PS-NPs can suppress myelin formation in the cerebellum of the fetus.

The concept of passenger-centric design has become increasingly significant in the intelligent automotive industry. Enhancing in-cabin thermal comfort while maintaining energy efficiency is crucial, and to this end, we leverage real-time passenger thermal sensation as a basis for HVAC control strategies. This study introduces a non-invasive, dual-phase prediction model that evaluates passenger thermal comfort by utilizing facial thermal imaging and environmental factors. The proposed model employs a deep learning neural network to extract pertinent features from the facial thermal images for an initial prediction. Then this prediction is refined by integrating it with environmental data through a machine learning algorithm, facilitating real-time personal thermal comfort assessment. Data collection for the model was performed across two distinct seasons, with 22 participants in summer and 18 in winter, gathering facial thermal images, environmental parameters, and thermal sensation votes to construct a comprehensive dataset. Analysis revealed a set of critical environmental factors that correlate strongly with thermal sensation votes, which were used as auxiliary features in the model. The performance of the proposed dual-phase approach, combining deep learning and machine learning techniques, demonstrated robust effectiveness on both summer and winter datasets. This substantiates the method's practicality for real-world application within vehicle environments.

Abstract Background Pneumocystis pneumonia (PCP) is a life-threatening opportunistic fungal infection with a high mortality rate in immunocompromised patients, ranging from 20 to 80%. However, current understanding of the variation in host immune response against Pneumocystis across different timepoints is limited. Methods In this study, we conducted a time-resolved single-cell RNA sequencing analysis of CD45+ cells sorted from lung tissues of mice infected with Pneumocystis. The dynamically changes of the number, transcriptome and interaction of multiply immune cell subsets in the process of Pneumocystis pneumonia were identified according to bioinformatic analysis. Then, the accumulation of Trem2hi interstitial macrophages after Pneumocystis infection was verified by flow cytometry and immunofluorescence. We also investigate the role of Trem2 in resolving the Pneumocystis infection by depletion of Trem2 in mouse models. Results Our results characterized the CD45+ cell composition of lung in mice infected with Pneumocystis from 0 to 5 weeks, which revealed a dramatic reconstitution of myeloid compartments and an emergence of PCP-associated macrophage (PAM) following Pneumocystis infection. PAM was marked by the high expression of Trem2. We also predicted that PAMs were differentiated from Ly6C+ monocytes and interacted with effector CD4+ T cell subsets via multiple ligand and receptor pairs. Furthermore, we determine the surface markers of PAMs and validated the presence and expansion of Trem2hi interstitial macrophages in PCP by flow cytometry. PAMs secreted abundant pro-inflammation cytokines, including IL-6, TNF-α, GM-CSF, and IP-10. Moreover, PAMs inhibited the proliferation of T cells, and depletion of Trem2 in mouse lead to reduced fungal burden and decreased lung injury in PCP. Conclusion Our study delineated the dynamic transcriptional changes in immune cells and suggests a role for PAMs in PCP, providing a framework for further investigation into PCP’s cellular and molecular basis, which could provide a resource for further discovery of novel therapeutic targets.

Frequency scanning interferometry (FSI) has become a popular method for long-range, targetbased, distance measurements. However, the cost of developing such systems, particularly the electronic components required for high-speed data acquisition (DAQ), remains a significant concern. In this article, we present a cost-effective, FPGA-based real-time DAQ system specifically designed for FSI, with a focus on long absolute distance measurements. Our design minimizes the use of third-party intellectual property (IP) and is fully compatible with the Xilinx FPGA 7 series families. The hardware employs a 160-MS/s, 16-bit dual-channel ADC interfaced to the FPGA via a low-voltage differential signaling (LVDS). The proposed system incorporates an external sampling clock, referred to as the K-clock, which linearizes the laser’s tuning rate, enabling optical measurements to be sampled at equal optical frequency intervals rather than equal time intervals. Additionally, we present the design of a high-speed, 160-MS/s ADC module for the front-end analog signal interface and the LVDS connection to the chosen FPGA. We demonstrate that the digitized data samples can be efficiently transmitted to a polarization controller (PC) application via a USB interface for further processing.

Inrush current is high-magnitude current drawn by power transformers upon energization. The severity of inrush current depends on factors such as the transformer’s residual flux and the voltage phase angle at the energization instant. This paper proposes a flux matching method for the energization of V/V traction transformers to mitigate inrush current. This is achieved by adjusting the residual flux of the core to an appropriate reference value and then obtaining the proper energization instant. To this end, the method only requires knowledge of nominal voltage and excitation current, eliminating the need to acquire transformer’s parameters/design information. The railway power conditioner, typically present at the low voltage side of the V/V transformer, is used as a current source to inject sinusoidal current into the transformer windings before its energization. The reference residual flux is calculated based on the circuit breaker operating characteristics. The energization instant is determined such that the adjusted flux density matches the steady-state flux expected with respect to the applied voltage. The proposed method is validated by conducting over 14,000 simulations under different conditions using PSCAD/EMTDC. The method is also implemented and successfully tested on a laboratory-scale test rig, which verifies its effectiveness in more realistic conditions.