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Abstract Converting solar energy into fuels is pursued as an attractive route to reduce dependence on fossil fuel. In this context, photothermal catalysis is a very promising approach through converting photons into heat to drive catalytic reactions. There are mainly three key factors that govern the photothermal catalysis performance: maximized solar absorption, minimized thermal emission and excellent catalytic property of catalyst. However, the previous research has focused on improving solar absorption and catalytic performance of catalyst, largely neglected the optimization of thermal emission. Here, we demonstrate an optically selective catalyst based Ti3C2Tx Janus design, that enables minimized thermal emission, maximized solar absorption and good catalytic activity simultaneously, thereby achieving excellent photothermal catalytic performance. When applied to Sabatier reaction and reverse water-gas shift (RWGS) as demonstrations, we obtain an approximately 300% increase in catalytic yield through reducing the thermal emission of catalyst by ~70% under the same irradiation intensity. It is worth noting that the CO2 methanation yield reaches 3317.2 mmol gRu −1 h−1 at light power of 2 W cm−2, setting a performance record among catalysts without active supports. We expect that this design opens up a new pathway for the development of high-performance photothermal catalysts.

Abstract Transportation systems primarily depend on vehicular flow on roads. Developed countries have shifted towards automated signal control, which manages and updates signal synchronisation automatically. In contrast, traffic in underdeveloped countries is mainly governed by manual traffic light systems. These existing manual systems lead to numerous issues, wasting substantial resources such as time, energy, and fuel, as they cannot make real‐time decisions. In this work, we propose an algorithm to determine traffic signal durations based on real‐time vehicle density, obtained from live closed circuit television camera feeds adjacent to traffic signals. The algorithm automates the traffic light system, making decisions based on vehicle density and employing Faster R‐CNN for vehicle detection. Additionally, we have created a local dataset from live streams of Punjab Safe City cameras in collaboration with the local police authority. The proposed algorithm achieves a class accuracy of 96.6% and a vehicle detection accuracy of 95.7%. Across both day and night modes, our proposed method maintains an average precision, recall, F1 score, and vehicle detection accuracy of 0.94, 0.98, 0.96 and 0.95, respectively. Our proposed work surpasses all evaluation metrics compared to state‐of‐the‐art methodologies.

Abstract Background Spatial molecular profiling depends on accurate cell segmentation. Identification and quantitation of individual cells in dense tissues, e.g. highly inflamed tissue caused by viral infection or immune reaction, remains a challenge. Methods We first assess the performance of 18 deep learning-based cell segmentation models, either pre-trained or trained by us using two public image sets, on a set of immunofluorescence images stained with immune cell surface markers in skin tissue obtained during human herpes simplex virus (HSV) infection. We then further train eight of these models using up to 10,000+ training instances from the current image set. Finally, we seek to improve performance by tuning parameters of the most successful method from the previous step. Results The best model before fine-tuning achieves a mean Average Precision (mAP) of 0.516. Prediction performance improves substantially after training. The best model is the cyto model from Cellpose. After training, it achieves an mAP of 0.694; with further parameter tuning, the mAP reaches 0.711. Conclusion Selecting the best model among the existing approaches and further training the model with images of interest produce the most gain in prediction performance. The performance of the resulting model compares favorably to human performance. The imperfection of the final model performance can be attributed to the moderate signal-to-noise ratio in the imageset.

Abstract This paper focuses on radio resource management (RRM) in multi-user dual-function radar communication (DFRC) systems using orthogonal frequency division multiplexing (OFDM) waveforms. We propose two RRM schemes, one from the perspective of sum rate maximization and the other from the perspective of user fairness maximization. These optimization problems are non-convex due to the presence of mixed integer terms, making them difficult to solve. To address these challenges, we have employed a decomposition approach to transform these two complex problems into separate, more readily solvable ones. In addressing the sum rate maximization problem, we initially introduce a heuristic greedy algorithm to obtain a resource allocation scheme that satisfies radar performance requirements. Subsequently, we utilize a cyclic iterative method along with KKT conditions to solve the sum rate maximization problem for communication users. Concerning the fairness maximization problem for communication users, we similarly employ a heuristic greedy algorithm to obtain a resource allocation scheme that meets radar performance constraints. Then utilize the Lagrangian dual method to solve the multi-user fairness maximization problem for communication users. Our experimental results confirm the effectiveness of the proposed algorithms.

BackgroundHigh-intensity chemotherapy can cause life-threatening complications in pediatric patients. Therefore, this study investigated safety and efficacy of long-acting pegylated recombinant human granulocyte colony-stimulating factor (PEG-rhG-CSF; Jinyouli®) in children undergoing high-intensity chemotherapy.MethodsTreatment-naive patients received post-chemotherapy PEG-rhG-CSF as primary prophylaxis for two cycles. The primary endpoints were drug-related adverse events (AEs) and bone pain scores. Secondary endpoints included grade 3–4 neutropenia, duration of neutropenia recovery, absolute neutrophil count changes, febrile neutropenia (FN), reduced chemotherapy intensity, antibiotic usage, and AE severity. The cost-effectiveness of PEG-rhG-CSF was compared with that of rhG-CSF (Ruibai®).ResultsHere, 307 and 288 patients underwent one and two PEG-rhG-CSF cycles, respectively. Ninety-one patients experienced drug-related AEs, primarily bone pain (12.7%). Moreover, Grade 3–4 neutropenia and FN were observed. Median FN durations were 3.0 days in both cycles. No drug-related delays were observed during chemotherapy. One patient experienced grade 4 neutropenia-induced reduction in chemotherapy intensity during cycle 2. In total, 138 patients received antibiotics. PEG-rhG-CSF exhibited superior cost-effectiveness compared to rhG-CSF.ConclusionOur findings indicate that PEG-rhG-CSF is safe, efficient, and cost-effective in pediatric patients undergoing high-intensity chemotherapy, providing preliminary evidence warranting further randomized controlled trials.

Summary: Background: The combined vincristine, pegylated liposomal doxorubicin (PLD), and cyclophosphamide (VPC) regimen has never been studied in pediatric patients. Methods: This open-label, single-center, single-arm phase I study utilizing a “3 + 3” design enrolled children with relapsed/refractory (R/R) solid tumors. Three dose levels of PLD (Duomeisu®) were studied (30, 40, or 50 mg/m2) in combination with cyclophosphamide (1500 mg/m2), mesna (1500 mg/m2), and vincristine (1.5 mg/m2, maximum 2 mg) once every 3 weeks. The primary endpoints included safety, the maximum tolerated dose (MTD) of PLD (Duomeisu®), and the recommended phase 2 dose (RP2D) of PLD (Duomeisu®) for further phase 2 investigation. The secondary endpoints were objective response rate (ORR) and disease control rate (DCR). This study is registered with ClinicalTrials.gov, NCT04213612. Findings: Between January 7, 2020, and November 18, 2021, 34 patients were eligible and evaluable for toxicity, while 26 patients were evaluable for response. The MTD of PLD (Duomeisu®) was 30 mg/m2. The most common adverse event (AE) was grade 3 or 4 neutropenia (61.8%). The most common grade 1 or 2 non-hematologic AE and cardiotoxicity effects were vomiting (35.3%) and abnormal electrocardiogram T waves (20.6%), respectively. ORR and DCR to VPC regimen after two cycles were 50.0% and 92.3%, respectively. Targeted gene panel sequencing revealed the activation of TP53 mutation may be an adverse prognostic factor. Interpretation: The VPC regimen showed a promising safety profile and had preliminary efficacy in children with R/R solid tumors. The RP2D for PLD (Duomeisu®) combined with cyclophosphamide and vincristine is 30 mg/m2 once every 3 weeks. Funding: CSPC Ouyi Pharmaceutical Co., Ltd., Shijiazhuang, the National Key Research and Development Program of China [No. 2022YFC2705005], the National Natural Science Foundation of China [No. 82203303], and the Basic and Applied Basic Research Foundation of Guangdong Province [No. 2021A1515110234].

Abstract Nowadays, silver nanoparticles (AgNPs) have attracted the attention of many researchers due to their special physical, chemical, and biological properties. There is strong evidence that biogenic AgNPs can act as potent anticancer agents through the production of reactive oxygen species (ROS) and initiate the mitochondrial pathway of apoptosis. That is why we decided to use Nepeta bracteata Benth flower extract for the first time to bio-synthesize AgNPs and study their cytotoxic and apoptotic effects on SK-BR-3 cells. AgNPs were biosynthesized at 70 °C after mixing silver nitrate and flower extract with a specific ratio and concentration, then were characterized using various analytical techniques, such as FESEM, FTIR, EDS, and zeta potential. Studies have shown that AgNPs have an irregular and circular shape, with about 99% by weight of silver, carbon, and oxygen. On the other hand, the appropriate size (below 57 nm) and surface charge (− 11.52 mV) make them stable in biological fluids. The better cytotoxic effect of AgNPs compared to flower extract on SK-BR-3 cells was investigated using the MTT method. The positive effect of AgNPs on inhibiting the growth of SK-BR-3 breast cancer cells was again confirmed by the sulforhodamine B staining method, so that AgNPs were able to decrease the density of cancer cells in a concentration-dependent manner. In addition, the flow cytometry test proved that biosynthesized AgNPs using Nepeta bracteata Benth flower extract can induce apoptosis in SK-BR-3 cancer cells. Real-time PCR then proved that the ratio of Bak1/Bclx, as well as caspase-3 expression, was increased due to active ROS-producing biomolecules present in the plant extract, and therefore, AgNPs can activate the mitochondria-dependent apoptosis pathway in breast cancer cells. Finally, their negligible oxidative stress on erythrocytes was confirmed by the lipid peroxidation method and showed that biosynthesized AgNPs can be used for breast cancer treatment without showing adverse effects on erythrocytes.

Abstract MYCN amplification is an independent poor prognostic factor in patients with high-risk neuroblastoma (NB). Further exploring the molecular regulatory mechanisms in MYCN-amplified NB will help to develop novel therapy targets. In this study, methylenetetrahydrofolate dehydrogenase 1 (MTHFD1) was identified as the differentially expressed gene (DEG) highly expressed in MYCN-amplified NB, and it showed a positive correlation with MYCN and was associated with a poor prognosis of NB patients. Knockdown of MTHFD1 inhibited proliferation and migration, and induced apoptosis of NB cells in vitro. Mouse model experiments validated the tumorigenic effect of MTHFD1 in NB in vivo. In terms of the mechanism, ChIP-qPCR and dual-luciferase reporter assays demonstrated that MTHFD1 was directly activated by MYCN at the transcriptional level. As an important enzyme in the folic acid metabolism pathway, MTHFD1 maintained the NADPH redox homeostasis in MYCN-amplified NB. Knockdown of MTHFD1 reduced cellular NADPH/NADP+ and GSH/GSSG ratios, increased cellular reactive oxygen species (ROS) and triggered the apoptosis of NB cells. Moreover, genetic knockdown of MTHFD1 or application of the anti-folic acid metabolism drug methotrexate (MTX) potentiated the anti-tumor effect of JQ1 both in vitro and in vivo. Taken together, MTHFD1 as an oncogene is a potential therapeutic target for MYCN-amplified NB. The combination of MTX with JQ1 is of important clinical translational significance for the treatment of patients with MYCN-amplified NB.

Abstract Radiative cooling is a zero-energy technology that enables subambient cooling by emitting heat into outer space (~3 K) through the atmospheric transparent windows. However, existing designs typically focus only on the main atmospheric transparent window (8–13 μm) and ignore another window (16–25 μm), under-exploiting their cooling potential. Here, we show a dual-selective radiative cooling design based on a scalable thermal emitter, which exhibits selective emission in both atmospheric transparent windows and reflection in the remaining mid-infrared and solar wavebands. As a result, the dual-selective thermal emitter exhibits an ultrahigh subambient cooling capacity (~9 °C) under strong sunlight, surpassing existing typical thermal emitters (≥3 °C cooler) and commercial counterparts (as building materials). Furthermore, the dual-selective sample also exhibits high weather resistance and color compatibility, indicating a high practicality. This work provides a scalable and practical radiative cooling design for sustainable thermal management.

Hypergraph neural networks have gained substantial popularity in capturing complex correlations between data items in multimodal datasets. In this study, we propose a novel approach called the self-supervised hypergraph learning (SHL) framework that focuses on extracting hypergraph features to improve multimodal representation. Our method utilizes a dual embedding strategy and leverages SHL to improve the accuracy and robustness of the model. To achieve this, we employ a hypergraph learning framework to extract global context effectively by capturing rich inter-modal dependencies. Additionally, we introduce a novel self-supervised learning (SSL) component that utilizes the interaction graph data, thereby strengthening the robustness of the model. By jointly optimizing hypergraph feature extraction and SSL, SHL significantly improves the performance of multimodal representation tasks. To validate the effectiveness of our approach, we construct two comprehensive multimodal micro-video recommendation datasets using publicly available data (TikTok and MovieLens-10M). Prior to dataset creation, we meticulously handle invalid entries and outliers and complete missing mode information using external auxiliary sources, such as YouTube. These datasets are made publicly available to the research community for evaluation purposes. Experimental results on the above recommendation datasets demonstrate that the proposed SHL approach outperforms state-of-the-art baselines, highlighting its superior performance in multimodal representation tasks.

Abstract Objective To early recognise and improve the prognosis of children systemic lupus erythematosus (cSLE)-associated pancreatitis by summarising and analysing clinical features and prognosis data from 12 cases. Methods Retrospective analysis of clinical data from 12 cases of cSLE-associated pancreatitis diagnosed and treated from January 2016 to December 2021 at hospitals such as Children’s Hospital of Capital Institute of Paediatrics. Results The median SLEDAI-2K score for disease activity was 18.00 (range 12.25–21.00) in the case group and 10.00 (range 7.00–18.00) in the control group, with a statistically significant difference (P < 0.05) between the two groups. The case group had a higher proportion of abdominal pain, vomiting, abdominal distension, pleural effusion, Raynaud’s phenomenon (RP), splenic infarction, and concurrent macrophage activation syndrome (MAS) than the control group, with a statistically significant difference (P < 0.05). Serum ferritin (SF), alanine transaminase (ALT), aspartate transaminase (AST), lactate dehydrogenase (LDH), amylase, and increased 24-h urine protein levels were statistically different between the two groups (P < 0.05); platelet counts (PLT) reduction was also statistically different (P < 0.05). The case group had a higher proportion of methylprednisolone pulse therapy, cyclophosphamide pulse therapy during remission induction, and therapeutic plasma exchange than the control group, with a statistically significant difference (P < 0.05) between the two. Conclusion CSLE-associated pancreatitis has a high fatality rate. The presence of RP, splenic infarction, pleural effusion, and MAS warrants attention from clinicians regarding the possibility of pancreatitis. Once pancreatitis is detected, the primary disease needs active treatment for better prognosis.

Abstract Polyimides externally deployed in spacecraft or satellites extensively have various aerospace hazards, including atomic oxygen (AO) erosion, irradiation degradation, and electrostatic charge/discharge (ESC/ESD). To cope with these challenges, we fabricate a ZnO/CuNi‐polyimide composite film with augmented permanence. Using spectroscopy and microscopy techniques, we have shown that the combination of chelation and cross‐linking in the interfacial architecture leads to enhanced interfacial compatibility and mechanical robustness. Besides, due to the positive AO diffusion barrier ability of the wurtzite ZnO, our composite film shows remarkable AO resistance and a very small Ey value of 6.88 × 10−26 cm3/atom, which is merely 2.29% of that of pristine polyimide. Moreover, the well‐defined nanocrystalline state with minimal lattice swelling (0.3%–0.7%) of the Fe+‐irradiated ZnO/CuNi‐polyimide at a damaging dose of 353.4 dpa demonstrates its excellent irradiation resistance. Finally, the ZnO/CuNi‐polyimide also shows sufficient electrostatic dissipation capacity to cope with the ESC/ESD events. Our fabrication approach for composite films based on multi‐technology integration shows potential for aerospace applications and deployment.

Abstract General movements (GMs) have been widely used for the early clinical evaluation of infant brain development, allowing immediate evaluation of potential development disorders and timely rehabilitation. The infants’ general movements can be captured digitally, but the lack of quantitative assessment and well‐trained clinical pediatricians presents an obstacle for many years to achieve wider deployment, especially in low‐resource settings. There is a high potential to explore wearable sensors for movement analysis due to outstanding privacy, low cost, and easy‐to‐use features. This work presents a sparse sensor network with soft wireless IMU devices (SWDs) for automatic early evaluation of general movements in infants. The sparse network consisting of only five sensor nodes (SWDs) with robust mechanical properties and excellent biocompatibility continuously and stably captures full‐body motion data. The proof‐of‐the‐concept clinical testing with 23 infants showcases outstanding performance in recognizing neonatal activities, confirming the reliability of the system. Taken together with a tiny machine learning algorithm, the system can automatically identify risky infants based on the GMs, with an accuracy of up to 100% (99.9%). The wearable sparse sensor network with an artificial intelligence‐based algorithm facilitates intelligent evaluation of infant brain development and early diagnosis of development disorders.

Power consumers can obtain authoritative green environmental value certification through green electricity trading, which plays an important role in improving the production competitiveness of enterprises, especially for international product trade affairs. However, the credibility of green electricity transactions faces serious challenges in the enterprise green authentication affairs, especially the user’s identity authentication, the traceability of green electricity transactions, and the standardization of green electricity transactions. Aiming to solve the certification and traceability problem of tradable green certificates, this paper proposes an integrated green certificate trading protocol, which solves its double-trading problem and helps to improve the credibility of renewable energy use. The main contribution is providing a solution based on the consortium blockchain technology to solve the main challenges mentioned above. The main solved scheme designs a series of protocols, which includes a purchase protocol, payment protocol, and non-transferable protocol. The whole process ensures the credibility, traceability, and non-transferability of green certificate trading. Multiple verification measures are adopted to address security and privacy challenges in green certificate management. Through security analysis, the protocol effectively defends against attacks such as double payments, transaction rollback, and transaction replays while ensuring users’ privacy.

Abstract Future communication networks are widely considered to be able to provide both high‐speed communication service and reliable sensing service. Integrated sensing and communication (ISAC) is considered to be the key technology to realize this vision. Appropriate resource allocation strategy is essential for ensuring the quality of both communication and sensing services in terms of ISAC. Existing ISAC‐based resource allocation schemes mainly focus on the coexistence of sensing and communication. However, in multi‐user ISAC networks, the services required by users are diverse, including sensing‐only, communication‐only and dual‐function cases. To this end, a resource allocation strategy is proposed based on user quality of service (QoS). Specifically, the sum rate of cellular network is maximized via optimizing spectrum resource selection and transmit power, while satisfying the sensing QoS. The optimal solution to the entire problem is achieved by first demonstrating that the communication rate in this paper is a monotonically increasing function of sensing power, which allows to obtain the optimal power allocation scheme. Subsequently, a matching method is employed to obtain the optimal spectrum sharing scheme. Numerical results validate the effectiveness of the proposed algorithm and also unveil a flexible trade‐off in ISAC systems over the benchmark scheme.

BackgroundCough is a common symptom during and after COVID-19 infection; however, few studies have described the cough profiles of COVID-19. ObjectiveThe aim of this study was to investigate the prevalence, severity, and associated risk factors of severe and persistent cough in individuals with COVID-19 during the latest wave of the Omicron variant in China. MethodsIn this nationwide cross-sectional study, we collected information of the characteristics of cough from individuals with infection of the SARS-CoV-2 Omicron variant using an online questionnaire sent between December 31, 2022, and January 11, 2023. ResultsThere were 11,718 (n=7978, 68.1% female) nonhospitalized responders, with a median age of 37 (IQR 30-47) years who responded at a median of 16 (IQR 12-20) days from infection onset to the time of the survey. Cough was the most common symptom, occurring in 91.7% of participants, followed by fever, fatigue, and nasal congestion (68.8%-87.4%). The median cough visual analog scale (VAS) score was 70 (IQR 50-80) mm. Being female (odds ratio [OR] 1.31, 95% CI 1.20-1.43), having a COVID-19 vaccination history (OR 1.71, 95% CI 1.37-2.12), current smoking (OR 0.48, 95% CI 0.41-0.58), chronic cough (OR 2.04, 95% CI 1.69-2.45), coronary heart disease (OR 1.71, 95% CI 1.17-2.52), asthma (OR 1.22, 95% CI 1.02-1.46), and gastroesophageal reflux disease (GERD) (OR 1.21, 95% CI 1.01-1.45) were independent factors for severe cough (VAS>70, 37.4%). Among all respondents, 35.0% indicated having a productive cough, which was associated with risk factors of being female (OR 1.44, 95% CI 1.31-1.57), having asthma (OR 1.84, 95% CI 1.52-2.22), chronic cough (OR 1.44, 95% CI 1.19-1.74), and GERD (OR 1.22, 95% CI 1.01-1.47). Persistent cough (>3 weeks) occurred in 13.0% of individuals, which was associated with the risk factors of having diabetes (OR 2.24, 95% CI 1.30-3.85), asthma (OR 1.70, 95% CI 1.11-2.62), and chronic cough (OR 1.97, 95% CI 1.32-2.94). ConclusionsCough is the most common symptom in nonhospitalized individuals with Omicron SARS-CoV-2 variant infection. Being female, having asthma, chronic cough, GERD, coronary heart disease, diabetes, and a COVID-19 vaccination history emerged as independent factors associated with severe cough, productive cough, and persistent cough.

Abstract Objective This study was designed to analyze the structural characteristics of the intestinal flora of elderly Uygur patients with sarcopenia, thereby providing new ideas for clinical treatment. Methods Firstly, fecal samples were collected from 40 elderly Uygur patients with sarcopenia (Sarcopenia group) and 40 healthy people (Control group). Next, significant differences in the intestinal flora between the two groups were analyzed based on 16S rDNA high‐throughput sequencing. The linear discriminant analysis effect size (LEfSe) was used to estimate the magnitude of the effect of each component (species) abundance on the differential effect. Additionally, an analysis was also performed on the relationship between the intestinal flora and the cytokines in the peripheral blood of patients with sarcopenia. Results The results of β diversity showed that there were differences in the structure of the intestinal flora between the two groups. Besides, the phylum level of intestinal flora between the two groups was not significantly different. However, the difference was significant in the intestinal flora at the order, family, and genus levels between the two groups. Among them, Lachnoclostridium, Photobacterium, Anaerobic Bacillus, Hydrogenophilus, and Eubacterium were enriched in the Sarcopenia group; Prevotella 9, Firmicutes FCS020 group, Streptobacillus, Aggregatibacter, Corynebacterium, Clostridium Difficile, and Haloanaerobium were enriched in the Control group. The LEfSe outcomes further showed that Lachnoclostridium was highly enriched in the Sarcopenia group; Prevotella 9 and Firmicutes FCS020 group were significantly enriched in the Control group. Furthermore, the relative abundance of Lachnoclostridium and Streptobacillus were significantly different in patients with high and low IL‐6 levels. Conclusion In conclusion, Lachnoclostridium is significantly enriched in the intestines of elderly Uygur patients with sarcopenia; the increase in Lachnoclostridium abundance and the decrease in Streptobacillus abundance are associated with high levels of IL‐6. Therefore, abnormal intestinal flora is related to inflammatory reflexes in patients with sarcopenia.

Objectives: This review aimed to harmoniously summarize and compare outlier rates for various cardiac troponin (cTn) assays, including high-sensitivity-cTn (hs-cTn) assays and contemporary cTn (generation of assays prior to hs-cTn ones) assays, from the published studies. Methods: The PRISMA guidelines were utilized to perform this systematic review. Five databases, including PubMed, Scopus, Embase, Cochrane Library, and Web of Science, were searched using specific keywords up to June 30th, 2023. Studies reporting specifically calculated outlier rates for cTn assays when conducting in-vitro diagnosis in human samples were included. Selected studies were then further assessed using the GRADE tool. Results: Thirteen studies were included. The data from the studies were summarized statistically in this review. The results showed substantial evidence of improved analytical robustness or reduced respective mean rates of outliers, critical outliers, and analytical outliers for hs-cTn assays (0.14 %, 0.18 %, and 0.18 %) compared to contemporary cTn assays (0.63 %, 0.71 %, and 0.50 %). Conclusion: The findings offer promisingly provide a comprehensive reference for laboratory scientists and clinical staff in choosing the most suitable cTn assay for patient care regrading outlier rates. Besides, this review reveals the advancements of hs-cTn assays with lower outlier rates than contemporary cTn assays. The emerging challenges for continuously improving analytical robustness of cTn assays are also elaborated.

From established latency, human herpes virus type 2 (HSV-2) frequently reactivates into the genital tract, resulting in symptomatic ulcers or subclinical shedding. Tissue-resident memory (TRM) CD8+ T cells that accumulate and persist in the genital skin at the local site of recrudescence are the “first responders” to viral reactivation, performing immunosurveillance and containment and aborting the ability of the virus to induce clinical lesions. This review describes the unique spatiotemporal characteristics, transcriptional signatures, and noncatalytic effector functions of TRM CD8+ T cells in the tissue context of human HSV-2 infection. We highlight recent insights into the intricate overlaps between intrinsic resistance, innate defense, and adaptive immunity in the tissue microenvironment and discuss how rapid virus–host dynamics at the skin and mucosal level influence clinical outcomes of genital herpes diseases.

Knowledge tracing (KT) for evaluating students' knowledge is an essential task in personalized education. More and more researchers have devoted themselves to solving KT tasks, e.g., deep knowledge tracing (DKT), which can capture more sophisticated representations of student knowledge. Nonetheless, these techniques ignore the reconstruction of the observed input information. Therefore, this leads to poor predictions of students' knowledge, even if the student performed well in the past knowledge state. In this article, we first employ causal inference for explanatory analysis of KT, then propose a learning algorithm for stable KT based on the analysis outcomes. The proposed approach aims to achieve stable KT by constructing global balanced weights that facilitate estimating feature influence and assessing causal relationships between individual variables and outcome variables. We have proved the approach has effective in accuracy and interpretability through extensive experimentation on real-world datasets. In conclusion, this article has methodological implications for the stable assessment of students' knowledge and provides a reference for personalization and use of intelligence in the educational teaching process.

TP53 mutation (TP53-mut) correlates with inferior survival in many cancers, whereas its prognostic role in diffuse large B-cell lymphoma (DLBCL) is still in controversy. Therefore, more precise risk stratification needs to be further explored for TP53-mut DLBCL patients. A set of 2637 DLBCL cases from multiple cohorts, was enrolled in our analysis. Among the 2637 DLBCL patients, 14.0% patients (370/2637) had TP53-mut. Since missense mutations account for the vast majority of TP53-mut DLBCL patients, and most non-missense mutations affect the function of the P53 protein, leading to worse survival rates, we distinguished patients with missense mutations. A TP53 missense mutation risk model was constructed based on a 150-combination machine learning computational framework, demonstrating excellent performance in predicting prognosis. Further analysis revealed that patients with high-risk missense mutations are significantly associated with early progression and exhibit dysregulation of multiple immune and metabolic pathways at the transcriptional level. Additionally, the high-risk group showed an absolutely suppressed immune microenvironment. To stratify the entire cohort of TP53-mut DLBCL, we combined clinical characteristics and ultimately constructed the TP53 Prognostic Index (TP53PI) model. In summary, we identified the truly high-risk TP53-mut DLBCL patients and explained this difference at the mutation and transcriptional levels. [ABSTRACT FROM AUTHOR]