Longitudinal changes in cognitive function in early psychosis: a meta-analysis with the MATRICS consensus cognitive battery (MCCB).

INTRODUCTION: A previous meta-analysis indicated stable progress in cognitive functions in early psychosis, assessed through various tools. To avoid assessment-related heterogeneity, this study aims to examine the longitudinal cognitive function changes in early psychosis utilizing the MATRICS Consensus Cognitive Battery (MCCB). METHODS: Embase, PubMed, and Scopus were systematically searched from their inception to September 26th 2023. The inclusion criteria were longitudinal studies that presented follow-up MCCB data for individuals experiencing first-episode psychosis (FEP) and those with ultra-high risk for psychosis (UHR). RESULTS: Twelve studies with 791 participants (566 FEP patients and 225 healthy controls) were subjected to analysis. Suitable UHR studies were absent. Over time, both FEP patients and healthy controls showed significant improvements in MCCB total scores. Furthermore, FEP patients demonstrated improvements across all MCCB domains, while healthy controls only showed augmentations in specific domains such as speed of processing, attention, working memory, and reasoning and problem-solving. Visuospatial learning improvements were significantly greater in FEP patients compared to healthy controls. Subgroup analyses suggested that neither diagnostic type nor follow-up duration influenced the magnitude of cognitive improvement in FEP patients. CONCLUSION: The magnitude of cognitive improvement for MCCB domains was not significantly different between FEP and healthy controls other than visuospatial learning. This underscores visuospatial learning as a potentially sensitive cognitive marker for early pathologic state changes in psychotic disorders.

Characteristics of pain empathic networks in healthy and primary dysmenorrhea women: an fMRI study.

Pain empathy enables us to understand and share how others feel pain. Few studies have investigated pain empathy-related functional interactions at the whole-brain level across all networks. Additionally, women with primary dysmenorrhea (PDM) have abnormal pain empathy, and the association among the whole-brain functional network, pain, and pain empathy remain unclear. Using resting-state functional magnetic resonance imaging (fMRI) and machine learning analysis, we identified the brain functional network connectivity (FNC)-based features that are associated with pain empathy in two studies. Specifically, Study 1 examined 41 healthy controls (HCs), while Study 2 investigated 45 women with PDM. Additionally, in Study 3, a classification analysis was performed to examine the differences in FNC between HCs and women with PDM. Pain empathy was evaluated using a visual stimuli experiment, and trait and state of menstrual pain were recorded. In Study 1, the results showed that pain empathy in HCs relied on dynamic interactions across whole-brain networks and was not concentrated in a single or two brain networks, suggesting the dynamic cooperation of networks for pain empathy in HCs. In Study 2, PDM exhibited a distinctive network for pain empathy. The features associated with pain empathy were concentrated in the sensorimotor network (SMN). In Study 3, the SMN-related dynamic FNC could accurately distinguish women with PDM from HCs and exhibited a significant association with trait menstrual pain. This study may deepen our understanding of the neural mechanisms underpinning pain empathy and suggest that menstrual pain may affect pain empathy through maladaptive dynamic interaction between brain networks.

Preoperative Contrast-Enhanced CT-Based Deep Learning Radiomics Model for Distinguishing Retroperitoneal Lipomas and Well­Differentiated Liposarcomas.

RATIONALE AND OBJECTIVES: To assess the efficacy of a preoperative contrast-enhanced CT (CECT)-based deep learning radiomics nomogram (DLRN) for predicting murine double minute 2 (MDM2) gene amplification as a means of distinguishing between retroperitoneal well-differentiated liposarcomas (WDLPS) and lipomas. METHODS: This retrospective multi-center study included 167 patients (training/external test cohort, 104/63) with MDM2-positive WDLPS or MDM2-negative lipomas. Clinical data and CECT features were independently measured and analyzed by two radiologists. A clinico-radiological model, radiomics signature (RS), deep learning and radiomics signature (DLRS), and a DLRN incorporating radiomics and deep learning features were developed to differentiate between WDLPS and lipoma. The model utility was evaluated based on the area under the receiver operating characteristic curve (AUC), accuracy, calibration curve, and decision curve analysis (DCA). RESULTS: The DLRN showed good performance for distinguishing retroperitoneal lipomas and WDLPS in the training (AUC, 0.981; accuracy, 0.933) and external validation group (AUC, 0.861; accuracy, 0.810). The DeLong test revealed the DLRN was noticeably better than clinico-radiological and RS models (training: 0.981 vs. 0.890 vs. 0.751; validation: 0.861 vs. 0.724 vs. 0.700; both P < 0.05); however, no discernible difference in performance was seen between the DLRN and DLRS (training: 0.981 vs. 0.969; validation: 0.861 vs. 0.837; both P > 0.05). The calibration curve analysis and DCA demonstrated that the nomogram exhibited good calibration and offered substantial clinical advantages. CONCLUSION: The DLRN exhibited strong predictive capability in predicting WDLPS and retroperitoneal lipomas preoperatively, making it a promising imaging biomarker that can facilitate personalized management and precision medicine.

How far has the globe gone in achieving One Health? Current evidence and policy implications based on global One Health index.

Background: In the 21st century, as globalization accelerates and global public health crises occur, the One Health approach, guided by the holistic thinking of human-animal-environment and emphasizing interdisciplinary collaboration to address global health issues, has been strongly advocated by the international community. An immediate requirement exists for the creation of an assessment tool to foster One Health initiatives on both global and national scales. Methods: Built upon extensive expert consultations and dialogues, this follow-up study enhances the 2022 global One Health index (GOHI) indicator system. The GOHI framework is enriched by covering three indices, e.g. external drivers index (EDI), intrinsic drivers index (IDI), and core drivers index (CDI). The comprehensive indicator system incorporates 13 key indicators, 50 indicators, and 170 sub I-indicators, utilizing a fuzzy analytic hierarchy process to ascertain the weight for each indicator. Weighted and summed, the EDI, IDI, and CDI scores contribute to the computation of the overall GOHI 2022 score. By comparing the ranking and the overall scores among the seven regions and across 160 countries/territories, we have not only derived an overall profile of the GOHI 2022 scores, but also assessed the GOHI framework. We also compared rankings of indicators and sub I-indicators to provide greater clarity on the strengths and weaknesses of each region within the One Health domains. Results: The GOHI 2022 performance reveals significant disparities between countries/territories ranged from 39.03 to 70.61. The global average score of the GOHI 2022 is 54.82. The average score for EDI, IDI, and CDI are 46.57, 58.01, and 57.25, respectively. In terms of global rankings, countries from North America, Europe and Central Asia, East Asia and Pacific present higher scores. In terms of One Health domains of CDI, the lowest scores are observed in antimicrobial resistance (median: 43.09), followed by food security (median: 53.78), governance (median: 54.77), climate change (median: 64.12) and zoonotic diseases (median: 69.23). Globally, the scores of GOHI vary spatially, with the highest score in North America while lowest in sub-Saharan Africa. In addition, evidence shows associations between the socio-demographic profile of countries/territories and their GOHI performance in certain One Health scenarios. Conclusion: The objective of GOHI is to guide impactful strategies for enhancing capacity building in One Health. With advanced technology and an annually updated database, intensifying efforts to refine GOHI's data-mining methodologies become imperative. The goal is to offer profound insights into disparities and progressions in practical One Health implementation, particularly in anticipation of future pandemics.

Improved XLNet modeling for Chinese named entity recognition of edible fungus.

Introduction: The diversity of edible fungus species and the extent of mycological knowledge pose significant challenges to the research, cultivation, and popularization of edible fungus. To tackle this challenge, there is an urgent need for a rapid and accurate method of acquiring relevant information. The emergence of question and answer (Q&A) systems has the potential to solve this problem. Named entity recognition (NER) provides the basis for building an intelligent Q&A system for edible fungus. In the field of edible fungus, there is a lack of a publicly available Chinese corpus suitable for use in NER, and conventional methods struggle to capture long-distance dependencies in the NER process. Methods: This paper describes the establishment of a Chinese corpus in the field of edible fungus and introduces an NER method for edible fungus information based on XLNet and conditional random fields (CRFs). Our approach combines an iterated dilated convolutional neural network (IDCNN) with a CRF. First, leveraging the XLNet model as the foundation, an IDCNN layer is introduced. This layer addresses the limited capacity to capture features across utterances by extending the receptive field of the convolutional kernel. The output of the IDCNN layer is input to the CRF layer, which mitigates any labeling logic errors, resulting in the globally optimal labels for the NER task relating to edible fungus. Results: Experimental results show that the precision achieved by the proposed model reaches 0.971, with a recall of 0.986 and an F1-score of 0.979. Discussion: The proposed model outperforms existing approaches in terms of these evaluation metrics, effectively recognizing entities related to edible fungus information and offering methodological support for the construction of knowledge graphs.

[EMP3 inhibits the formation of heterotypic cell-in-cell structures in hepatocellular carcinoma].

Heterotypic cell-in-cell (heCIC) structures represent a unique intercellular interaction where tumor cells internalize immune cells to enhance the killing efficiency of immune cells. However, the mechanism of heCIC structure formation remains to be fully elucidated. In this study, we explored the role of epithelial membrane protein 3 (EMP3), a PMP-22/EMP/MP20 protein family member highly expressed in the patients with hepatocellular carcinoma and poor prognosis, in the formation of the heCIC structure formed by natural killer cells and hepatocellular carcinoma cells. The analysis of monoclonal hepatocellular carcinoma cell lines revealed that EMP3 presented low expression in the cells with high capability to form heCIC structure and high expression in those with low capability. Knocking down the expression of EMP3 by gene editing promoted the formation of heCIC structures, while overexpression of EMP3 significantly inhibited this process. Additionally, the expression of factors involved in the heCIC structure formation suggested that EMP3 inhibited the formation of heCIC structures by modulating the adhesion ability and cytoskeleton of tumor cells. The findings lay a foundation for enhancing the heCIC-mediated tumor immunotherapy by targeting EMP3.

Autonomous Crack Detection for Mountainous Roads Using UAV Inspection System.

Road cracks significantly affect the serviceability and safety of roadways, especially in mountainous terrain. Traditional inspection methods, such as manual detection, are excessively time-consuming, labor-intensive, and inefficient. Additionally, multi-function detection vehicles equipped with diverse sensors are costly and unsuitable for mountainous roads, primarily because of the challenging terrain conditions characterized by frequent bends in the road. To address these challenges, this study proposes a customized Unmanned Aerial Vehicle (UAV) inspection system designed for automatic crack detection. This system focuses on enhancing autonomous capabilities in mountainous terrains by incorporating embedded algorithms for route planning, autonomous navigation, and automatic crack detection. The slide window method (SWM) is proposed to enhance the autonomous navigation of UAV flights by generating path planning on mountainous roads. This method compensates for GPS/IMU positioning errors, particularly in GPS-denied or GPS-drift scenarios. Moreover, the improved MRC-YOLOv8 algorithm is presented to conduct autonomous crack detection from UAV imagery in an on/offboard module. To validate the performance of our UAV inspection system, we conducted multiple experiments to evaluate its accuracy, robustness, and efficiency. The results of the experiments on automatic navigation demonstrate that our fusion method, in conjunction with SWM, effectively enables real-time route planning in GPS-denied mountainous terrains. The proposed system displays an average localization drift of 2.75% and a per-point local scanning error of 0.33 m over a distance of 1.5 km. Moreover, the experimental results on the road crack detection reveal that the MRC-YOLOv8 algorithm achieves an F1-Score of 87.4% and a mAP of 92.3%, thus surpassing other state-of-the-art models like YOLOv5s, YOLOv8n, and YOLOv9 by 1.2%, 1.3%, and 3.0% in terms of mAP, respectively. Furthermore, the parameters of the MRC-YOLOv8 algorithm indicate a volume reduction of 0.19(×106) compared to the original YOLOv8 model, thus enhancing its lightweight nature. The UAV inspection system proposed in this study serves as a valuable tool and technological guidance for the routine inspection of mountainous roads.

Immune checkpoint inhibitors-induced diabetes mellitus (review).

Immune checkpoint inhibitors (ICIs) have become extensively utilized in the early-stage treatment of various cancers, offering additional therapeutic possibilities for patients with advanced cancer. However, certain patient populations are susceptible to experiencing toxic adverse effects from ICIs, such as thyrotoxicosis, rashes, among others. Specifically, ICIDM, induced by immune checkpoint inhibitors, exhibits characteristics similar to insulin-dependent diabetes mellitus (Type 1 Diabetes Mellitus, T1DM). ICIDM is characterized by a rapid onset and may coincide with severe ketoacidosis. Despite a favorable response to insulin therapy, patients typically require lifelong insulin dependence. After discussing the autoimmune adverse effects and the specifics of ICIs-induced diabetes mellitus (ICIDM), it is important to note that certain patient populations are particularly susceptible to experiencing toxic adverse effects from ICIs. Specifically, ICIDM, which is triggered by immune checkpoint inhibitors, mirrors the characteristics of insulin-dependent diabetes mellitus (Type 1 Diabetes Mellitus, T1DM). This article conducts an in-depth analysis of the literature to explore the pathogenesis, disease progression, and treatment strategies applicable to diabetes induced by immune checkpoint inhibitors (ICIDM).

Construction of dual driving force in carbon nitride for highly efficient hydrogen evolution: Simultaneously manipulating carriers transport in intra- and interlayer.

Photocatalytic hydrogen evolution is widely recognized as an environmentally friendly approach to address future energy crises and environmental issues. However, rapid recombination of photo-induced charges over carbon nitride in lateral and vertical direction hinder this process. Herein, we proposed an effective strategy involving the embedding of benzene rings and the intercalation of platinum atoms on carbon nitride for a controlled intralayer and interlayer charges flow. Modified carbon nitride exhibits a significant higher hydrogen evolution rate (6288.5 µmol/g/h), which is 42 times greater than that of pristine carbon nitride. Both experiments and simulations collectively indicate that the improved photocatalytic activities can be attributed to the adjustment of the highly symmetric structure of carbon nitride, achieved by embedding benzene rings to induce the formation of an intralayer build-in electric field and intercalating Pt atoms to enhance interlayer polarization, which simultaneously accelerate lateral and vertical charges migration. This dual-direction charges separation mechanism in carbon nitride provides valuable insights for the development of highly active photocatalysis.

A colorimetric/electrochemical microfluidic biosensor using target-triggered DNA hydrogels for organophosphorus detection.

Organophosphorus compounds are widely distributed and highly toxic to the environment and living organisms. The current detection of organophosphorus compounds is based on a single-mode method, which makes it challenging to achieve good portability, accuracy, and sensitivity simultaneously. This study designed a multifunctional microfluidic chip to develop a dual-mode biosensor employing a DNA hydrogel as a carrier and aptamers as recognition probes for the colorimetric/electrochemical detection of malathion, an organophosphorus compound. The biosensor balanced portability and stability by combining a microfluidic chip and target-triggered DNA hydrogel-sensing technologies. Moreover, the biosensor based on target-triggered DNA hydrogel modified microfluidic developed in this study exhibited a dual-mode response to malathion, providing both colorimetric and electrochemical signals. The colorimetric mode enables rapid visualization and qualitative detection and, when combined with a smartphone, allows on-site quantitative analysis with a detection limit of 56 nM. The electrochemical mode offers a broad linear range (0.01-3000 µM) and high sensitivity (a limit of detection of 5 nM). The two modes could validate each other and improve the accuracy of detection. The colorimetric/electrochemical dual-mode biosensor based on target-triggered DNA hydrogel modified microfluidic chip offers a portable, simple, accurate, and sensitive strategy for detecting harmful environmental and food substances.

The role of sleep quality and anxiety symptoms in the association between childhood trauma and self-harm attempt: A chain-mediated analysis in the UK Biobank.

BACKGROUND: Childhood trauma is a risk factor for self-harm/suicidal behavior, but research on the potential association linking sleep quality and anxiety symptoms to childhood trauma and self-harm attempt is limited. The aim of this study was to describe the mediating role of sleep quality and anxiety symptoms between childhood trauma and self-harm attempt, and to provide a scientific basis for the prevention of self-harm behaviors. METHODS: This study ultimately included 11,063 study participants who participated in the baseline survey of this large prospective cohort study of the UK Biobank. We used structural equation modeling (SEM) to analyze the chain mediating role of sleep quality and anxiety symptoms in childhood trauma and self-harm attempt while controlling for covariates. RESULTS: A total of 19.58 % of study participants self-reported self-harm attempt. Sleep quality was negatively correlated with childhood trauma, anxiety symptoms, and self-harm attempt (p < 0.01). Childhood trauma, anxiety symptoms, and self-harm attempt were positively correlated (p < 0.01). In addition, after adjusting for confounders, anxiety symptoms were able to partially mediate the association between childhood trauma and self-harm attempt (effect value: 0.042, p < 0.01), and sleep quality and anxiety symptoms can chain mediate the association between childhood trauma and self-harm attempt (effect value:0.002, p < 0.01), with a total mediating effect of 65.67 % of the total effect. Subgroup analyses further showed that the mediating effects of sleep quality and anxiety symptoms on childhood trauma and self-harm attempt differed across age, gender, ethnicity, and smoking and drinking subgroups. CONCLUSIONS: This study found a complex relationship between childhood trauma, sleep quality, anxiety symptoms, and self-harm attempt, with sleep quality and anxiety symptoms mediating the relationship between childhood trauma and self-harm attempt. Multiple avenues of intervention, such as the provision of professional psychological interventions and timely monitoring, should be used to improve the sleep quality and mental health of individuals with traumatic childhood experiences and to prevent the occurrence of emotionally harmful behaviors such as self-harm/suicide.

Angularly offset multiline dispersive optical phased array enabling large field of view and plateau envelope.

We propose and demonstrate an angularly offset multiline (AOML) dispersive silicon nitride optical phased array (OPA) that enables efficient line beam scanning with an expanded field of view (FOV) and plateau envelope. The suggested AOML OPA incorporates multiline OPA units, which were seamlessly integrated with a 45° angular offset through a thermo-optic switch based on a multimode interference coupler, resulting in a wide FOV that combines three consecutive scanning ranges. Simultaneously, a periodic diffraction envelope rendered by the multiline OPA units contributes to reduced peak intensity fluctuation of the main lobe across the large FOV. An expedient polishing enabling the angled facet was diligently accomplished through the implementation of oblique polishing techniques applied to the 90° angle of the chip. For each dispersive OPA unit, we engineered an array of delay lines with progressively adjustable delay lengths, enabling a passive wavelength-tunable beam scanning. Experimental validation of the proposed OPA revealed efficient beam scanning, achieved by wavelength tuning from 1530 to 1600 nm and seamless switching between multiline OPAs, yielding an FOV of 152° with a main lobe intensity fluctuation of 2.8 dB. The measured efficiency of dispersive scanning was estimated at 0.97°/nm, as intended.

Optical characterization of a fiber Fabry-Perot cavity: precision measurement of intra-cavity loss, transmittance, and reflectance.

We propose and demonstrate a method for characterizing the individual mirror parameters of a fiber Fabry-Perot cavity (FFPC). By measuring the reflection and transmission spectra of the FFPC with an incident laser propagating from the two mirrors of the FFPC and considering several normal or unique losses, the transmittance, reflectance, and intra-cavity loss of the individual mirrors can be determined. Due to the intrinsic limitation of cavity length, traditional powerful methods, such as the cavity ring-down technique, are not applicable to FFPCs for characterizing the parameters of individual mirrors. This scheme provides a dependable method for assessing FFPC mirrors and provides a significant capability for the implementation of strong-coupling cavity quantum electrodynamics based on FFPCs.

CUNet3+: A Multiscale Connected UNet for the Segmentation of Lung Cancer Cells in Pathology Sections Stained Using Rapid On-Site Cytopathological Evaluation.

Lung cancer is an increasingly serious health problem worldwide, and early detection and diagnosis are crucial for successful treatment. With the development of artificial intelligence and the growth of data volume, machine learning techniques can play a significant role in improving the accuracy of early detection in lung cancer. This study proposes a deep learning-based segmentation algorithm for rapid on-site cytopathological evaluation (ROSE) to enhance the diagnostic efficiency of endobronchial ultrasound-guided transbronchial needle aspiration biopsy (EBUS-TBNA) during surgery. By utilizing the CUNet3+ network model, cell clusters, including cancer cell clusters, can be accurately segmented in ROSE-stained pathological sections. The model demonstrated high accuracy, with an F1-score of 0.9604, recall of 0.9609, precision of 0.9654, and accuracy of 0.9834 on the internal testing data set. It also achieved an area under the receiver-operating characteristic curve of 0.9972 for cancer identification. The proposed algorithm provides time savings for on-site diagnosis, improves EBUS-TBNA efficiency, and outperforms classical segmentation algorithms in accurately identifying lung cancer cell clusters in ROSE-stained images. It effectively reduces over-segmentation, decreases network parameters, and enhances computational efficiency, making it suitable for real-time patient evaluation during surgical procedures.

Nirmatrelvir and ritonavir for inpatients with severe or critical COVID-19 beyond five days of symptom onset: a propensity score-matched, multicenter, retrospective cohort study.

BACKGROUND: There is an urgent need for therapeutic strategies for inpatients with severe or critical COVID-19. The evaluation of the clinical benefits of nirmatrelvir and ritonavir (Nmr/r) for these patients beyond five days of symptom onset is insufficient. METHODS: A new propensity score-matched cohort was constructed by using multicenter data from 6695 adult inpatients with COVID-19 from December 2022 to February 2023 in China after the epidemic control measures were lifted across the country. The severity of disease of the inpatients was based on the tenth trial edition of the Guidelines on the Diagnosis and Treatment of COVID-19 in China. The symptom onset of 1870 enrolled severe or critical inpatients was beyond five days, and they received either Nmr/r plus standard treatment or only standard care. The ratio of patients whose SOFA score improved more than 2 points, crucial respiratory endpoints, changes in inflammatory markers, safety on the seventh day following the initiation of Nmr/r treatment, and length of hospital stay were evaluated. RESULTS: In the Nmr/r group, on Day 7, the number of patients with an improvement in SOFA score ≥ 2 was much greater than that in the standard treatment group (P = 0.024) without a significant decrease in glomerular filtration rate (P = 0.815). Additionally, the rate of new intubation was lower (P = 0.004) and the no intubation days were higher (P = 0.003) in the first 7 days in the Nmr/r group. Other clinical benefits were limited. CONCLUSIONS: Our study may provide new insight that inpatients with severe or critical COVID-19 beyond five days of symptom onset benefit from Nmr/r. Future studies, particularly randomized controlled trials, are necessary to verify the above findings.

Association of Body Mass Index and Central Obesity with Spinopelvic Alignment Parameters in a Chinese Population: A Prospective Study.

OBJECTIVE: The purpose of this study was to explore the impact of central obesity on spinal sagittal balance in adults aged 18 and older by examining correlations between waist circumference (WC) and abdominal circumference (AC) and spinopelvic alignment parameters. METHODS: This prospective cohort study included 350 adults aged 18 and older. Participants underwent whole-body biplanar radiography using the EOS imaging system. Spinal and pelvic parameters were measured and correlated with body mass index, WC, and AC. Statistical analyses included one-way analysis of variance, Wilcoxon rank-sum tests for data with nonhomogeneous variances, and chi-squared tests for categorical data. Intra-rater and inter-rater reliability were assessed using intraclass correlation coefficients, with subsequent analyses to explore correlations between body measurements and spinal parameters. RESULTS: The study found significant correlations between increased WC and AC and changes in spinopelvic parameters. However, obesity did not uniformly influence all sagittal alignment parameters. Significant variations in spinal measurements indicate that central obesity plays a role in altering spinal stability and alignment. CONCLUSIONS: The findings highlight the impact of central obesity on spinal alignment and emphasize the importance of considering central obesity in clinical assessments of spinal pathologies. Further research is essential to better understand the relationship between obesity, spinal sagittal balance, and related health conditions.

Aluminum/Graphene Thermal Interface Materials with Positive Temperature Dependence.

Graphene is widely used in excellent thermal interface materials (TIMs), thanks to its remarkably high in-plane thermal conductivity (k∥). However, the poor through-plane thermal conductivity (k⊥) limits its further application. Here, we developed a simple in situ growth method to prepare graphene-based thermal interface composites with positively temperature-dependent thermal conductivity, which loaded aluminum (Al) nanoparticles onto graphene nanoplatelets (GNPs). To evaluate the variations in thermal performance, we determined the thermal diffusivity and specific heat capacity of the composites using a laser-flash analyzer and a differential scanning calorimeter, respectively. The Al nanoparticles act as bridges between the nanoplatelets, enhancing the k⊥ of the 1.3-Al/GNPs composite to 11.70 W·m-1·K-1 at 25 °C. Even more remarkably, those nanoparticles led to a unique increase in k⊥ with temperature, reaching 20.93 W·m-1·K-1 at 100 °C. Additionally, we conducted an in-depth investigation of the thermal conductivity mechanism of the Al/GNPs composites. The exceptional heat transport property enabled the composites to exhibit a superior heat dissipation performance in simulated practical applications. This work provides valuable insights into utilizing graphene in composites with Al nanoparticles, which have special thermal conductivity properties, and offers a promising pathway to enhance the k⊥ of graphene-based TIMs.

Association between dietary inflammatory index and NT-proBNP levels in US adults: A cross-sectional analysis.

BACKGROUND: With cardiovascular diseases standing as a leading cause of mortality worldwide, the interplay between diet-induced inflammation, as quantified by the Dietary Inflammatory Index (DII), and heart failure biomarker NT-proBNP has not been investigated in the general population. METHODS: This study analyzed data from the National Health and Nutrition Examination Survey (NHANES) 1999-2004, encompassing 10,766 individuals. The relationship between the DII and NT-proBNP levels was evaluated through multivariable-adjusted regression models. To pinpoint crucial dietary components influencing NT-proBNP levels, the LASSO regression model was utilized. Stratified analyses were then conducted to examine the associations within specific subgroups to identify differential effects of the DII on NT-proBNP levels across diverse populations. RESULTS: In individuals without heart failure, a unit increase in the DII was significantly associated with an increase in NT-proBNP levels. Specifically, NT-proBNP levels rose by 9.69 pg/mL (95% CI: 6.47, 12.91; p < 0.001) without adjustments, 8.57 pg/mL (95% CI: 4.97, 12.17; p < 0.001) after adjusting for demographic factors, and 5.54 pg/mL (95% CI: 1.75, 9.32; p = 0.001) with further adjustments for health variables. In participants with a history of heart failure, those in the second and third DII quartile showed a trend towards higher NT-proBNP levels compared to those in the lowest quartile, with increases of 717.06 pg/mL (95% CI: 76.49-1357.63, p = 0.030) and 855.49 pg/mL (95% CI: 156.57-1554.41, p = 0.018). Significant interactions were observed in subgroup analyses by age (<50: ß = 3.63, p = 0.141; 50-75: ß = 18.4, p<0.001; >75: ß = 56.09, p<0.001), gender (men: ß = 17.82, p<0.001; women: ß = 7.43, p = 0.061),hypertension (ß = 25.73, p<0.001) and diabetes (ß = 38.94, p<0.001). CONCLUSION: This study identified a positive correlation between the DII and NT-proBNP levels, suggesting a robust link between pro-inflammatory diets and increased heart failure biomarkers, with implications for dietary modifications in cardiovascular risk management.

Protease SfpB plays an important role in cell membrane stability and immune system evasion in Streptococcus agalactiae.

Bacteria possess the ability to develop diverse and ingenious strategies to outwit the host immune system, and proteases are one of the many weapons employed by bacteria. This study sought to identify S. agalactiae additional serine protease and determine its role in virulence. The S. agalactiae THN0901 genome features one S8 family serine peptidase B (SfpB), acting as a secreted and externally exposed entity. A S8 family serine peptidase mutant strain (ΔsfpB) and complement strain (CΔsfpB) were generated through homologous recombination. Compared to the wild-type strain THN0901, the absorption of EtBr dyes was significantly reduced (P < 0.01) in ΔsfpB, implying an altered cell membrane permeability. In addition, the ΔsfpB strain had a significantly lower survival rate in macrophages (P < 0.01) and a 61.85 % lower adhesion ability to the EPC cells (P < 0.01) compared to THN0901. In the in vivo colonization experiment using tilapia as a model, 210 fish were selected and injected with different bacterial strains at a concentration of 3 × 106 CFU/tail. At 6, 12, 24, 48, 72 and 96 h post-injection, three fish were randomly selected from each group and their brain, liver, spleen, and kidney tissues were isolated. Subsequently, it was demonstrated that the ΔsfpB strain exhibited a markedly diminished capacity for colonization in tilapia. Additionally, the cumulative mortality of ΔsfpB in fish after intraperitoneal injection was reduced by 19.92-23.85 %. In conclusion, the findings in this study have demonstrated that the SfpB plays a significant role in S. agalactiae cell membrane stability and immune evasion. The immune evasion is fundamental for the development and transmission of invasive diseases, the serine protease SfpB may be a promising candidate for the development of antimicrobial agents to reduce the transmission of S. agalactiae.

Photochemical Strategies toward Precision Targeting against Multidrug-Resistant Bacterial Infections.

Infectious diseases pose a serious threat and a substantial economic burden on global human and public health security, especially with the frequent emergence of multidrug-resistant (MDR) bacteria in clinical settings. In response to this urgent need, various photobased anti-infectious therapies have been reported lately. This Review explores and discusses several photochemical targeted antibacterial therapeutic strategies for addressing bacterial infections regardless of their antibiotic susceptibility. In contrast to conventional photobased therapies, these approaches facilitate precise targeting of pathogenic bacteria and/or infectious microenvironments, effectively minimizing toxicity to mammalian cells and surrounding healthy tissues. The highlighted therapies include photodynamic therapy, photocatalytic therapy, photothermal therapy, endogenous pigments-based photobleaching therapy, and polyphenols-based photo-oxidation therapy. This comprehensive exploration aims to offer updated information to facilitate the development of effective, convenient, safe, and alternative strategies to counter the growing threat of MDR bacteria in the future.