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Abstract Social phobia (SP) refers to excessive anxiety about social interactions. College students, with their exposure to academic, familial, and job-related pressures, are an ideal population for early screening and intervention of social phobia. Additionally, COVID-19 prevention measures including keeping social distance may further impact social phobia. This study aims to investigate the influencing factors of social phobia among Chinese college students and to tentatively explore the impact of COVID-19 prevention measures on social phobia. Respondents were recruited through Chinese Internet social platforms for an online survey. College students’ social phobia scores in pre- and early-COVID-19 periods were measured using Peters' short form of the Social Interaction Anxiety Scale and Social Phobia Scale (SIAS-6/SPS-6). Demographic information, family information, social relations, self-evaluation, and subjective feelings regarding the impact of COVID-19 preventive measures on social phobia were collected. A multivariable logistic regression model was used to analyze the influencing factors. A total of 1859 valid questionnaires were collected, revealing that the social phobia scores increased from 12.3 ± 11.9 to 13.4 ± 11.9 between pre- and early-COVID-19 periods, with an increase of 1.0 ± 6.4 (p

Abstract Deep learning models have been widely used in electroencephalogram (EEG) analysis and obtained excellent performance. But the adversarial attack and defense for them should be thoroughly studied before putting them into safety-sensitive use. This work exposes an important safety issue in deep-learning-based brain disease diagnostic systems by examining the vulnerability of deep learning models for diagnosing epilepsy with brain electrical activity mappings (BEAMs) to white-box attacks. It proposes two methods, Gradient Perturbations of BEAMs (GPBEAM), and Gradient Perturbations of BEAMs with Differential Evolution (GPBEAM-DE), which generate EEG adversarial samples, for the first time by perturbing BEAMs densely and sparsely respectively, and find that these BEAMs-based adversarial samples can easily mislead deep learning models. The experiments use the EEG data from CHB-MIT dataset and two types of victim models each of which has four different deep neural network (DNN) architectures. It is shown that: (1) these BEAM-based adversarial samples produced by the proposed methods in this paper are aggressive to BEAM-related victim models which use BEAMs as the input to internal DNN architectures, but unaggressive to EEG-related victim models which have raw EEG as the input to internal DNN architectures, with the top success rate of attacking BEAM-related models up to 0.8 while the top success rate of attacking EEG-related models only 0.01; (2) GPBEAM-DE outperforms GPBEAM when they are attacking the same victim model under a same distortion constraint, with the top attack success rate 0.8 for the former and 0.59 for the latter; (3) a simple modification to the GPBEAM/GPBEAM-DE will make it have aggressiveness to both BEAMs-related and EEG-related models (with top attack success rate 0.8 and 0.64), and this capacity enhancement is done without any cost of distortion increment. The goal of this study is not to attack any of EEG medical diagnostic systems, but to raise concerns about the safety of deep learning models and hope to lead to a safer design.

The global outbreak of the 2022 monkeypox virus infection of humans and the 2023 documentation of a more virulent monkeypox in the Democratic Republic of the Congo raised public health concerns about the threat of human-to-human transmission of zoonotic diseases. Currently available vaccines may not be sufficient to contain outbreaks of a more transmissible and pathogenic orthopoxvirus. Development of a safe, effective, and scalable vaccine against orthopoxviruses to stockpile for future emergencies is imminent. In this study, we have developed an mRNA vaccine candidate, ALAB-LNP, expressing four vaccinia viral antigens A27, L1, A33, and B5 in tandem in one molecule, and evaluated the vaccine immunogenicity in rodent models. Immunization of animals with the candidate mRNA vaccine induced a potent cellular immune response and long-lasting antigen-specific binding antibody and neutralizing antibody responses against vaccinia virus. Strikingly, the sera from the vaccine-immunized mice cross-reacted with all four homologous antigens of multiple orthopoxviruses and neutralized monkeypox virus in vitro, holding promise for this mRNA vaccine candidate to be used for protection of humans from the infection of monkeypox and other orthopoxvirus.

Abstract Respiratory syncytial virus (RSV) infection in children is the most common viral respiratory infection and can cause severe lung damage or death. There is no licensed vaccine for preventing RSV infection. Previously we demonstrated that an RSV vaccine, BARS13, consisting of recombinant G protein from E. coli plus cyclosporine A (CsA) as an immune-modulator, can protect animals from RSV challenge without inducing vaccine-enhanced disease (VED). To maximize the efficacy of such a vaccine, we introduced RSV pre-fusion F protein (pre-F) to form a new vaccine comprised of the pre-F and G proteins with the CsA. Two intramuscular immunizations with the vaccine induced a higher level of neutralizing antibodies against RSV and protected mice from RSV challenge without incurring VED. Interestingly, the addition of the pre-F to the vaccine facilitated anti-G antibody production and protection from RSV infection mainly via induction of antibodies against the central conserved domain (CCD) of the G protein which correlated with blocking the CX3C-CX3CR1 interaction. A 15 amino acid sequence (FP4) within the F2 region of pre-F served as a CD4+ Th epitope to facilitate the anti-G antibody response. Collectively, such a combination of the FP4 peptide with the G protein and CsA provides a novel strategy for developing a safe and maximally effective recombinant G protein-containing RSV vaccine.

ObjectivesThe risk factors for extraurothelial recurrence (EUR) after radical nephroureterectomy (RNU) in patients with upper urinary tract urothelial carcinoma (UTUC) are currently inconsistent and unclear. In this study, we aimed to identify these risk factors and develop a grading system for EUR.MethodsWe retrospectively analyzed 220 patients who underwent RNU for UTUC in our center from January 2009 to December 2020. Overall survival (OS) and extraurothelial recurrence-free survival (EURFS) were compared using the Kaplan–Meier curve with a log-rank test. Univariate and multivariate Cox regression models were applied to identify the independent risk factors related to EUR.ResultsThe median follow-up period was 42 (range: 2–143) months. Of the 220 patients, 61 patients developed EUR in our cohort, which had worse survival outcome. Multivariate Cox regression analysis showed pathologic stage, lymph node (LN) status, lymphovascular invasion (LVI), Ki-67, neutrophil-to-lymphocyte ratio (NLR), and platelet-to-lymphocyte ratio (PLR) were independent risk factors for EUR. The Kaplan–Meier curves revealed a significant difference in EUR among the three risk groups.ConclusionOur study suggests that pathologic stage, LN status, LVI, Ki-67, NLR, and PLR are independent risk factors for EUR in UTUC patients after RNU. The development of a grading system for EUR risk stratification may assist urologists in making clinical decisions regarding the management of UTUC.

Privacy protection, high labeling cost, and varying characteristics of seizures among patients and at different times are the main obstacles to building seizure detection models. Considering these issues, we propose a novel Mentor-Student architecture for Patient-Specific seizure detection (MS4PS). It contains a new method of knowledge transferring called mentor-select-for-student, which exploits the knowledge of a mentor model by using this model to select data for training a student model, making it possible to avoid transferring patient data and the negative influence of transferring parameters/structures of pre-trained models. It also contains a new method of active learning, which uses both an experienced mentor model and a quick-learning student model to select high-quality samples for doctors to label. Each of the two models is coupled with a particular sample selection strategy that combines uncertainty/certainty and the distance between the unlabeled samples and labeled seizure samples. The proposed method can quickly train a suitable detector for a patient at his/her first epilepsy diagnosis with the help of: (1) an experienced mentor model that chooses the most category-certain electroencephalography (EEG) data segments; (2) a student model (detector itself) that chooses the most category-uncertain EEG data segments; (3) doctors who label these data segments selected by both the mentor model and student model. By replacing or improving the mentor model and refining the historical models of patients when they come next time, the MS4PS system can be sustainably promoted. The proposed method is tested on the CHB-MIT and NEO datasets, and the results demonstrate its effectiveness and efficiency.

ObjectiveWe aimed to evaluate the effect of the timing of diagnostic ureteroscopy (URS) on intravesical recurrence (IVR) following radical nephroureterectomy (RNU).Patients and methodsThe clinical data of 220 patients with upper tract urothelial carcinoma (UTUC) treated with RNU at our center from June 2010 to December 2020 were retrospectively analyzed. According to the timing of the URS, all patients were divided into three groups: the no URS group, the 1-session group (diagnostic URS immediately followed by RNU), and the 2-session group (RNU after diagnostic URS). Additionally, we analyzed IVR-free survival (IVRFS) using the Kaplan-Meier and Cox proportional regression methods.ResultsThe median follow-up period of these 220 patents was 41 (range: 2-143) months. Among them, 58 patients developed IVR following RNU. Kaplan-Meier curve displayed a significantly higher IVR rate in both treatment groups than in the no-URS group (p=0.025). In the subgroup of patients with renal pelvis cancer, the incidence of IVR was significantly higher in both treatment groups than in the group without URS (p=0.006). In univariate Cox proportional regression analysis, the two treatment groups were risk factors for IVR compared to the no-URS group [p=0.027, hazard ratio (HR): 1.93, 95% confidence interval (CI): 1.08-3.46]. The two-stage group (p=0.032, HR: 1.98, 95% CI: 1.08-3.65), positive urine pathology (p

In order to select a reasonable controlling mode of rocky desertification and restrain the development of rocky desertification with local conditions, the soil moisture in Karst area was studied by using full-polarization Synthetic Aperture Radar (SAR) data in this paper. Based on the decisive role of rocky desertification degree, vegetation, slope and characteristic of the soil, different models were employed to invert the soil moisture in different rocky desertification degree areas according to their land use type and vegetation as follows: (1) The advanced integral equation model (AIEM) was employed in the intensive rocky desertification and serious rocky desertification areas. (2) The water-cloud model (WCM) coupled with AIEM was employed in the slight rocky desertification and moderate rocky desertification areas. (3) The Michigan microwave canopy scattering (MIMICS) model coupled with AIEM was employed in the non-rocky desertification and potential rocky desertification areas. As a result, the soil moisture was inverted successfully in the whole study area with the total inversion accuracy of 84.37% and RMSE of 2.10. This result demonstrates that the proposed method can invert the soil moisture accurately and provide a powerful technical support for inverting the soil moisture in the Karst area with large span, fluctuating landform and diverse vegetation.

Data augmentation, generating new data that are similar but not same with the original data by making a series of transformations to the original data, is one of the mainstream methods to alleviate the problem of insufficient data. Instead of augmenting input data, this paper proposes a method for augmenting features in the middle layers of deep models, called MEML (Mean Extrapolation in Middle Layers). It gets the features outputted by any middle layer of deep models and then generates new features by doing mean extrapolation with some randomly selected features. After that, it replaces these selected features with their corresponding new ones and keeps the labels unchanged, and then lets the new composed output continue to propagate forward. Experiments on two classic deep neural network models and three image datasets show that our MEML method can significantly improve the model classification accuracy and outperform in most experiments the state-of-the-art feature space augmentation methods that factor dropout and K Nearest Neighbors extrapolation. Interestingly, when coupled with some input space augmentation methods, e.g., rotation and horizontal flip, our MEML method further improves the performance of two deep models on three datasets, implying that the input space augmentation methods and MEML could complement each other.

Myeloid-derived suppressor cells (MDSCs) are a population of myeloid progenitor cells with immunoregulatory functions and their role in myasthenia gravis (MG) was unknown. In this study, we investigated the phenotypic and functional alterations of MDSCs in MG before and after immunotherapy. The frequency of MDSCs significantly increased and negatively correlated to that of Th1 or Th17 cells after immunotherapy. MDSCs from untreated patients with MG showed an impaired suppression of IFN-γ production in T-cells and improved immunosuppressive function was identified after immunotherapy. The MFI of Arg-1 in MDSCs also increased after immunotherapy. These findings suggested the functional difference in MDSCs before and after immunotherapy, and MDSCs might play a role in disease remission.

Although respiratory syncytial virus (RSV) infection in infants and young children is a global public health issue, development of a safe RSV vaccine has been impeded by formalin-inactivated RSV-enhanced respiratory disease (ERD). In developing a safer yet effective RSV vaccine for children, a strategy to decrease over-reactive T cells and increase neutralizing anti-RSV antibodies should be considered. We previously demonstrated that adult mice immunized with RSV recombinant G protein plus low-dose Cyclosporine A (G+ CsA) could, upon subsequent RSV challenge, produce increased levels of antigen-specific T regulatory cells in lungs that overcame the ERD. Neutralizing anti-RSV antibodies that prevented viral infection were also elicited. In this study, we investigated if such a G+ CsA vaccine could provide infant mice with the same protection from RSV infection without ERD. The results showed that the G+ CsA vaccine could prevent RSV infection with only a mild loss of body weight. Importantly, there was nearly normal morphology and no mucus appearance in lung tissues after RSV challenge. These results demonstrate that the G+ CsA vaccine strategy achieved similar benefits in the neonatal prime and infancy boost model as in the adult mouse model. The G+ CsA immunization strategy is potentially safe and effective in neonates and infants because it suppresses the devastating ERD.

Exercise therapy is seen as one of the major treatments for the rehabilitation for patients, particularly using modern technologies, such as virtual reality or augmented reality. Computer-assisted physical rehabilitation training involves measuring performance by analyzing the movement data collected with a sensory system during prescribed rehabilitation exercises. Human activity recognition is a challenging topic for machine learning in the present area of research. Since the sensor-based activity recognition seeks deep knowledge from various low-level sensor readings concerning human activities. In this paper, the Smart Sensor-based Rehabilitation Exercise Recognition (SSRER) system has been proposed using a deep learning framework. For the recognition of rehabilitation exercise with sensor information, a convolutional neural network (CNN) has been used on dynamic platform(D-CNN) where it has sensory data for physical rehabilitation exercise body movement by Gaussian mixture models (GMM). The input signals and GMMs are in various segments contains shapes for many CNN routes. To retrieve the state transition likelihood of hidden states, the Sensor (S-CNN) utilizes the algorithm of improved lossless information compression as discriminant features of various movements. Therefore, the hybridized CNN of the Sensor (S-CNN) and D-CNN are combined with a deep learning classifier to assess every rehabilitation class exercise at different levels. The categorized deep learning methods show improved performance with best-learned features for any rehabilitation exercise. The difference between the best attribute and the test score analyzed mathematically with our collected data and a variety of activity recognition datasets has been illustrated in this article with test results.

Channel capacity reflects the ability of the system to transmit information without errors.The path loss and impulse response of the system were calculated based on traversing tiny unit method,and then the frequency response of the system was obtained by the discrete Fourier transform of the impulse response sampling sequence,and the 3 dB bandwidth of the system was calculated.Considering the shot noise caused by signal light,the signal-to-noise ratio of the system was obtained according to the quantum limit method,and then the relationship between the channel capacity and the geometric parameters of transmitter and receiver in non-line-of-sight noncoplanar ultraviolet communication system were simulated and analyzed by Shannon formula.The results show that the channel capacity decreases with the increase of the off axis angle and the communication distance.When the elevation angles of transmitter and receiver are less than 40°,the channel capacity decreases rapidly with the increase of the elevation angles of transmitter and receiver,and the transmitter elevation angle has a great influence on the channel capacity.The system channel capacity is almost constant with the increase of divergence angle,and the larger the receiver field of view angle,the greater the system channel capacity.

Although adjuvants are a common component of many vaccines, there are few adjuvants licensed for use in humans due to concerns about their toxic effects. There is a need to develop new and safe adjuvants, because some existing vaccines have low immunogenicity among certain patient groups. In this study, SBP, a hepatitis B surface antigen binding protein that was discovered through screening a human liver cDNA expression library, was introduced into hepatitis B vaccine. A good laboratory practice, non-clinical safety evaluation was performed to identify the side effects of both SBP and SBP-adjuvanted hepatitis B vaccine. The results indicate that SBP could enhance the HBsAg-specific immune response, thus increasing the protection provided by the hepatitis B vaccine. The safety data obtained here warrant further investigation of SBP as a vaccine adjuvant.

Abstract Background and Objective Tinengotinib, a novel multi-target small molecule kinase inhibitor, is currently undergoing phase II clinical trial in the USA and China. The purpose of this open-label study was to investigate the absorption, metabolism, and excretion of [14C]tinengotinib following a single oral dose in healthy subjects. Methods Six healthy male subjects received a single oral dose of [14C]tinengotinib capsules at 10 mg/100 µCi, and blood, urine, and feces samples were collected. Phenotyping experiments were further conducted to confirm the enzymes involved in its metabolism. Results Tinengotinib was rapidly absorbed in plasma with a time to peak drug concentration (T max) of 1.0–4.0 h post-dose and a long terminal half-life (t ½) of 23.7 h. Blood-to-plasma radioactivity concentration ratios across timepoints ranged from 0.780 to 0.827, which indicated minimal association of radioactivity with blood cells. The mean cumulative excreted radioactivity was 99.57% of the dose, including 92.46% (68.65% as unchanged) in feces and 7.11% (0.28% as unchanged) in urine. In addition to unchanged tinengotinib, a total of 11 radioactive metabolites were identified in plasma, urine, and feces. The most abundant circulating radioactivity was the parent drug in plasma, which comprised 88.23% of the total radioactivity area under the concentration–time curve (AUC). Metabolite M410-3 was a major circulating metabolite, accounting for 5.38% of the parent drug exposure and 4.75% of the total drug-related exposure, respectively. All excreted metabolites accounted for less than 5.10% and 1.82% of the dose in feces and urine, respectively. In addition, no unique metabolites were observed in humans. Tinengotinib was metabolized mainly via CYP3A4. Conclusions Overall, tinengotinib demonstrated a complete mass balance with limited renal excretion, no disproportionate blood metabolism, and slow elimination, primarily through the fecal route. The results of this study provide evidence to support the rational use of tinengotinib as a pharmacotherapeutic agent. Registration ChinadrugTrials.org.cn identifier: CTR20212852.

Abstract Citrullination plays an essential role in various physiological or pathological processes, however, whether citrullination is involved in regulating tumour progression and the potential therapeutic significance have not been well explored. Here, we find that peptidyl arginine deiminase 4 (PADI4) directly interacts with and citrullinates hypoxia-inducible factor 1α (HIF-1α) at R698, promoting HIF-1α stabilization. Mechanistically, PADI4-mediated HIF-1αR698 citrullination blocks von Hippel-Lindau (VHL) binding, thereby antagonizing HIF-1α ubiquitination and subsequent proteasome degradation. We also show that citrullinated HIF-1αR698, HIF-1α and PADI4 are highly expressed in hepatocellular carcinoma (HCC) tumour tissues, suggesting a potential correlation between PADI4-mediated HIF-1αR698 citrullination and cancer development. Furthermore, we identify that dihydroergotamine mesylate (DHE) acts as an antagonist of PADI4, which ultimately suppresses tumour progression. Collectively, our results reveal citrullination as a posttranslational modification related to HIF-1α stability, and suggest that targeting PADI4-mediated HIF-1α citrullination is a promising therapeutic strategy for cancers with aberrant HIF-1α expression.

Abstract A 28 days pesticide degradation experiment was conducted for broccoli (Brassica oleracea L. var. italica Planch) and pakchoi (Brassica chinensis L.) with three pesticides (chlorantraniliprole (CAP), haloxyfop-etotyl (HPM), and indoxacarb (IXB)) to explore the effects of biochar on pesticide environmental fate and rhizosphere soil diversity. Rice straw biochar (RB) was applied to soil at a 25.00 t ha−1 dosage under greenhouse conditions, and its effects on the degradation of three pesticides in vegetables and in soil were investigated individually. Overall, RB application effectively facilitated CAP and HPM degradation in broccoli by 13.51–39.42% and in broccoli soil by 23.80–74.10%, respectively. RB application slowed the degradation of CAP, HPM and IXB in pakchoi by 0.00–57.17% and slowed the degradation of CAP in pakchoi by 37.32–43.40%. The results showed that the effect of RB application on pesticide degradation in crops and soil was related to biochar properties, pesticide solubility, plant growth status, and soil characteristics. Rhizosphere soil microorganisms were also investigated, and the results showed that biochar application may be valuable for altering bacterial richness and diversity. The effect of biochar application on pesticide residues in crops and soil was influenced by the vegetable variety first, and the second was pesticide characteristics. RB applied to soil at a 25.00 t ha−1 dosage under greenhouse conditions is recommended for broccoli production to ensure food safety. Our results suggested that biochar application in soil could reduce pesticide non-point source pollution, especially for highly soluble pesticides, and could affect soil microorganisms.

Straw return is commonly used to improve soil fertility and quality, thereby contributing to improved crop production; however, the influence of different straw management on soil moisture variation, its response to precipitation, and soil pore properties remains largely inadequate, let alone the relationships between these factors. To fill this knowledge gap, the temporal dynamics of soil moisture were monitored during the maize growing season from 2022 to 2023 in a fixed-site field experiment on straw return, including straw removal (CK), straw direct incorporation (SD) and straw-derived biochar incorporation (BC), in Northeast China. In contrast to CK, BC was observed to have a significant impact on soil moisture, increasing the monthly average of September by 30.3 % and the annual average by 5.90 % in 2022 (P < 0.05). In addition, SD significantly increased the average for June by 28.5 % (P < 0.05) and exhibited a more pronounced linear correlation between soil moisture and rainfall amount with the greatest slope (y = 0.2061x + 0.0026, R² = 0.3098). The results indicated that the soil water storage capacity was enhanced when straw was returned, with a more effective impact in BC in 2022 but in SD in 2023, partially attributing to the discrepancies in precipitation quantity, intensity, and frequency between the two years. Furthermore, BC significantly reduced the dissipated precipitation fraction and enhanced the soil water memory in both 2022 (P < 0.05) and 2023 (P < 0.01), based on the evaluation of the capacity of soil to retain water. The results of X-ray computed tomography (CT) scanning showed that straw return resulted in the reconstruction of a relatively intricate pore network, which was characterized by an increase in porosity and macropore number in SD, an increase in pore circularity, a reduction in pore diameter and maccropore number in BC, which was associated with soil layer depth. It is concluded that the improvement of soil water and pore properties, as evidenced by an enhanced soil moisture memory and an increased porosity, contributed to the enhancement of soil nutrients under straw return conditions. This study provides valuable insights for selecting a suitable mode of straw return, thereby facilitating the effective utilization of straw and water resources in agricultural systems, particularly in arid regions.

Biochar derived from biomass pyrolysis has proven to be an excellent material for pesticide adsorption and can be used as soil amendment for pesticide non-point pollution. However, the adsorption and desorption mechanisms for certain biochar and pesticide are still unclear. In this study, we investigated the properties of biochar derived from walnut (Juglans regia L.) shell (WSB), and used batch equilibrium method to investigate the adsorption and desorption behavior for chlorantraniliprole (CAP). The physical-chemical analysis showed that there were mainly lignin charcoal of alkyl carbon, methoxyl carbon, aromatic carbon, and carboayl carbon as the primary carbon compounds of WSB. The π - π electron donor acceptor interaction, electrostatic interaction, and hydrogen bond were the primary adsorption mechanisms of the WSB adsorption. Batch equilibrium study under 298 K showed that WSB application in the soil significantly improved the adsorption ability for CAP, and the adsorption behavior was a mono-layer adsorption process as Langmuir model fitted the adsorption isotherm data better than the Freundlich model. While Freundlich model analysis showed that WSB addition to the soil changed the isothermal adsorption line from the S style to the L style. The spontaneous degree reaction of sorbents from strong to weak was in the following order: 5%-WSB >7%-WSB >10%-WSB >1%-WSB >3%-WSB > soil > WSB, and the maximum application effect was achieved at 5 % (m/m) WSB dosage mixed with the soil. Therefore, we considered that WSB addition in soil increased its CAP adsorption capacity, and 5 % (m/m) WSB application was the best choice for CAP pollution control. These data will contribute to the adsorption mechanism and the optimal use dosage of WSB for CAP pollution control.

Abstract Background The rarity of primary central nervous system lymphoma (PCNSL) and treatment heterogeneity contributes to a lack of prognostic models for evaluating posttreatment remission. This study aimed to develop and validate radiomic‐based models to predict the durable response (DR) to high‐dose methotrexate (HD‐MTX)‐based chemotherapy in PCNSL patients. Methods A total of 159 patients pathologically diagnosed with PCNSL between 2011 and 2021 across two institutions were enrolled. According to the NCCN guidelines, the DR was defined as the remission lasting ≥1 year after receiving HD‐MTX‐based chemotherapy. For each patient, a total of 1218 radiomic features were extracted from prebiopsy T1 contrast‐enhanced MR images. Multiple machine‐learning algorithms were utilized for feature selection and classification to build a radiomic signature. The radiomic‐clinical integrated models were developed using the random forest method. Model performance was externally validated to verify its clinical utility. Results A total of 105 PCNSL patients were enrolled after excluding 54 cases with ineligibility. The training and validation cohorts comprised 76 and 29 individuals, respectively. Among them, 65 patients achieved DR. The radiomic signature, consisting of 8 selected features, demonstrated strong predictive performance, with area under the curves of 0.994 in training cohort and 0.913 in validation cohort. This signature was independently associated with the DR in both cohorts. Both the radiomic signature and integrated models significantly outperformed the clinical models in two cohorts. Decision curve analysis underscored the clinical utility of the established models. Conclusions This radiomic signature and integrated models have the potential to accurately predict the DR to HD‐MTX‐based chemotherapy in PCNSL patients, providing valuable therapeutic insights.

Encouraging and astonishing developments have recently been achieved in image-based diagnostic technology. Modern medical care and imaging technology are becoming increasingly inseparable. However, the current diagnosis pattern of signal to image to knowledge inevitably leads to information distortion and noise introduction in the procedure of image reconstruction (from signal to image). Artificial intelligence (AI) technologies that can mine knowledge from vast amounts of data offer opportunities to disrupt established workflows. In this prospective study, for the first time, we develop an AI-based signal-to-knowledge diagnostic scheme for lung nodule classification directly from the computed tomography (CT) raw data (the signal). We find that the raw data achieves almost comparable performance with CT, indicating that it is possible to diagnose diseases without reconstructing images. Moreover, the incorporation of raw data through three common convolutional network structures greatly improves the performance of the CT models in all cohorts (with a gain ranging from 0.01 to 0.12), demonstrating that raw data contains diagnostic information that CT does not possess. Our results break new ground and demonstrate the potential for direct signal-to-knowledge domain analysis.

Introduction: TT-01025-CL is an oral, irreversible small molecule that potently inhibits vascular adhesion protein-1 (VAP-1) for the treatment of inflammation associated with non-alcoholic steatohepatitis (NASH). The objectives of this study were to evaluate the safety/tolerability, pharmacokinetics, and pharmacodynamics of TT-01025-CL, a VAP-1 inhibitor, in healthy Chinese volunteers.Methods: Double-blind, placebo-controlled, dose-escalation studies were conducted in subjects randomized to receive oral once-daily TT-01025-CL (ranges: 10–300 mg [single dose]; 20–100 mg for 7 days [multiple doses]) or placebo under fasting conditions. Safety and tolerability were monitored throughout the study. Pharmacokinetic (PK) parameters were determined using non-compartment analysis. The activity of semicarbazide-sensitive amine oxidase (SSAO)-specific amine oxidase and the accumulation of methylamine in plasma were evaluated as pharmacodynamic (PD) biomarkers.Results: A total of 36 (single-dose group) and 24 (multiple-dose group) subjects were enrolled in the study. No serious adverse events (AEs) were reported, and no subject discontinued due to an AE. All treatment-emergent adverse events (TEAEs) were mild and moderate in intensity. No dose-dependent increase in the intensity or frequency of events was observed. TT-01025-CL was rapidly absorbed after administration. In the single-ascending dose (SAD) study, median Tmax ranged from 0.5 to 2 h and mean t1/2z ranged from 2.09 to 4.39 h. PK was linear in the range of 100–300 mg. The mean Emax of methylamine ranged from 19.167 to 124.970 ng/mL, with mean TEmax ranging from 13.5 to 28.0 h. The complete inhibition (>90%) of SSAO activity was observed at 0.25–0.5 h post-dose and was maintained 48–168 h post-dose. In the multiple-ascending dose (MAD) study, a steady state was reached by day 5 in the 40 mg and 100 mg dose groups. Negligible accumulation was observed after repeated dosing. PK was linear in the range of 20–100 mg. Plasma methylamine appeared to plateau at doses of 20 mg and above, with mean Emax ranging from 124.142 to 156.070 ng/mL and mean TEmax ranging from 14.2 to 22.0 h on day 7. SSAO activity in plasma was persistently inhibited throughout the treatment period. No evident change in methylamine and SSAO activity was observed in the placebo groups.Conclusion: TT-01025-CL was safe and well-tolerated at a single dose of up to 300 mg and multiple doses of up to 100 mg once daily for 7 consecutive days. Absorption and elimination occurred rapidly in healthy volunteers. Linearity in plasma exposure was observed. TT-01025-CL inhibited SSAO activity rapidly and persistently in humans. The profile of TT-01025-CL demonstrates its suitability for further clinical development.

Abstract Itaconate is a well-known immunomodulatory metabolite; however, its role in hepatocellular carcinoma (HCC) remains unclear. Here, we find that macrophage-derived itaconate promotes HCC by epigenetic induction of Eomesodermin (EOMES)-mediated CD8+ T-cell exhaustion. Our results show that the knockout of immune-responsive gene 1 (IRG1), responsible for itaconate production, suppresses HCC progression. Irg1 knockout leads to a decreased proportion of PD-1+ and TIM-3+ CD8+ T cells. Deletion or adoptive transfer of CD8+ T cells shows that IRG1-promoted tumorigenesis depends on CD8+ T-cell exhaustion. Mechanistically, itaconate upregulates PD-1 and TIM-3 expression levels by promoting succinate-dependent H3K4me3 of the Eomes promoter. Finally, ibuprofen is found to inhibit HCC progression by targeting IRG1/itaconate-dependent tumor immunoevasion, and high IRG1 expression in macrophages predicts poor prognosis in HCC patients. Taken together, our results uncover an epigenetic link between itaconate and HCC and suggest that targeting IRG1 or itaconate might be a promising strategy for HCC treatment.

Abstract Straw returning has been demonstrated as a beneficial approach for the utilization of renewable biomass source, which contributes to reducing environmental pollution and strengthening the sustainability of agriculture. However, information on how microorganisms respond to different straw return modes (SRMs) at varying nitrogen fertilizer levels (NFLs) in the black soil is still limited. The community composition, network pattern, and modular function of bacteria and fungi are investigated under three SRMs, including straw removal (CK), crushed straw incorporation (SD), and biochar incorporation (BC) at three NFLs (0, 144, and 240 kg N ha−1, respectively) mainly using Illumina MiSeq technique based on a long‐term maize field experiment. Results showed that bacterial richness, diversity, and fungal richness decreased with NFL reduction. However, these decreases can be compensated by SD and BC, demonstrating superiority for BC at reduced NFLs. SD and BC differed in their effects on the bacterial and fungal abundances (showing increments only in SD) and fungal Shannon diversity (remaining stable only in BC irrespective of NFLs). Microbial communities were substantially affected by SRMs and interacted with NFLs, which were driven by soil NH4+‐N, available potassium, total nitrogen, and pH. In addition, SD induced a network characterized by its highly complex (average degree 10.259 vs. 3.364) and stable structure (average clustering coefficient 0.503 vs. 0.239), Ascomycota as predominating keystone taxa, and abundant N‐cycling related bacteria, while BC formed a network comprising a superior modular structure (modularity 2.599 vs. 0.912), dominant symbiotic fungi, and soil bulk density as specific shaping factor, indicating that network pattern, keystone taxa, modular function, and determining factors shifted between SD and BC co‐occurrence networks. These results deepen insights into the response divergence of bacteria and fungi to SRMs and NFLs, providing a scientific basis for selecting the suitable strategy for sustainable straw utilization in the black soil area.

Energy storage efficiency (η) and large recoverable energy density (Wre) are necessary for antiferroelectric materials in order to develop antiferroelectric-based dielectric capacitors with exceptional energy storage capacity. In the present paper, the effect of doping La3+ on the energy storage capacity of Pb1−xLax(Hf0.45Sn0.55)0.995O3 antiferroelectric ceramics was studied. Adjusting the content of La and changing the phase structure of PLHS from antiferroelectric to relaxor ferroelectric gradually, which narrowed its hysteresis loop, yielded a high energy storage efficiency of 81.9% and the maximum breakdown field strength of 200 kV/cm when x = 2 mol%. In addition, the recoverable energy density and energy storage efficiency both showed excellent temperature stability and frequency stability in the temperature range of 10–110 °C and the frequency range of 10–100 Hz, suggesting that Pb0.98La0.02(Hf0.45Sn0.55)0.995O3 are favorable materials candidates for the preparation of pulsed-power capacitors that can be used in a wide range of conditions.

Abstract Background Dexmedetomidine could provide some advantages to prevent postoperative complications in elderly patients undergoing under general anaesthesia. However, dexmedetomidine inhibits haemodynamics to some extent due to its sympathetic inhibition. Objective To evaluate the effects of different doses of dexmedetomidine on haemodynamics during surgery and recovery after general anaesthesia in elderly patients undergoing hip replacement. Methods This was a prospective randomized double-blind controlled clinical trial. Eligible patients were randomly allocated into comparative groups (normal saline (NS) and midazolam (MD), n = 30) and dexmedetomidine groups at different doses (D0.25/D0.5/D0.75, n = 30). In the D0.25/D0.5/D0.75 groups, dexmedetomidine was administered at different initial loading doses (0.25/0.5/0.75 μg/kg for 15 min) following 0.5 μg/kg/h continuous infusion until the end of the operation. In the MD group, patients were administered 0.03 mg/kg midazolam at the beginning of anaesthesia induction. Results Compared to the MD and NS groups, there were significant decreases in MAP in the D0.5 and D0.75 groups at many time points, such as skin incision, end of operation, and from extubation until 30 min after extubation (P 0.05). Moreover, the percentage of patients whose MAP and HR decreased > 20% of baseline was higher in the D0.75 and D0.5 groups than that in all other groups. Compared to the NS group, from the beginning to the end of the operation, the 95% confidence interval (CI) of RR for MAP below > 20% of baseline in the D0.5 and D0.75 groups was greater than 1. In particular, the CI of the RR in the D0.75 group was greater than 1 until the patient awoke from general anaesthesia (P 20% of baseline in the D0.5 group was greater than 1 compared to the NS group at the time of induction and extubation (P 0.05). The recovery quality of patients during the post-anaesthesia period was also observed. No differences were observed among all the groups in the time to awakening or extubation after general anaesthesia (P > 0.05). According to the Riker Sedation-agitated Scale, dexmedetomidine significantly alleviated emergency agitation or delirium compared to NS (P

Antimicrobial resistance has brought great burden to global public health. Alternative strategies are needed to reduce the development of drug resistance. Herein, we have developed an effective synergistic antibacterial strategy combining low–temperature photothermal therapy (LT–PTT) with antibiotic therapy, improving the bactericidal efficiency to avoid antimicrobial resistance. Copper sulfide templated with bovine serum albumin (CuS–BSA) nanoparticles were selected as the photothermal agent, and co–loaded into the hydrogel (Gel) with mupirocin. The Gel could slow down the release rate of CuS–BSA and mupirocin, thereby prolonging the effective drug reaction time. More importantly, when applying near–infrared laser irradiation, the antibacterial activity of the platform could be enhanced greatly by LT–PTT effect of CuS–BSA nanoparticles. In vitro and in vivo results both confirmed that the antibacterial efficacy of the synergistic therapeutic strategy was improved greatly with complete bacterial removal. Overall, this platform has posed a potential strategy to reduce the development of drug resistance and improve patient compliance.

Abstract Purpose To develop and validate a deep learning‐based pathological risk score (RS) with an aim of predicting patients' prognosis to investigate the potential association between the information within the whole slide image (WSI) and cervical cancer prognosis. Methods A total of 251 patients with the International Federation of Gynecology and Obstetrics (FIGO) Stage IA1–IIA2 cervical cancer who underwent surgery without any preoperative treatment were enrolled in this study. Both the clinical characteristics and WSI of each patient were collected. To construct a prognosis‐associate RS, high‐dimensional pathological features were extracted using a convolutional neural network with an autoencoder. With the score threshold selected by X‐tile, Kaplan–Meier survival analysis was applied to verify the prediction performance of RS in overall survival (OS) and disease‐free survival (DFS) in both the training and testing datasets, as well as different clinical subgroups. Results For the OS and DFS prediction in the testing cohort, RS showed a Harrell's concordance index of higher than 0.700, while the areas under the curve (AUC) achieved up to 0.800 in the same cohort. Furthermore, Kaplan–Meier survival analysis demonstrated that RS was a potential prognostic factor, even in different datasets or subgroups. It could further distinguish the survival differences after clinicopathological risk stratification. Conclusion In the present study, we developed an effective signature in cervical cancer for prognosis prediction and patients' stratification in OS and DFS.