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Summary: Background: Previous conventional epidemiological studies found a J-shape relationship between alcohol consumption and dementia, but this result was subject to confounding biases and reverse causation. Therefore, we aimed to investigate the potential linear or non-linear causal association between alcohol consumption and the incident risk of dementia in current drinkers. Methods: This study used data from the UK Biobank to investigate the relationship between alcohol consumption and dementia risk. 313,958 White British current drinkers, who were free of dementia during 2006–2010, were followed up until 2021. Alcohol consumption was self-reported and calculated according to the National Health Service guideline. The primary outcome was all-cause dementia identified through hospital and mortality records. We used multivariable Cox models with restricted cubic splines for conventional analysis and both non-linear and linear Mendelian Randomization (MR) analyses to assess causal relationships, employing a genetic score based on 95 SNPs identified from a meta-genome-wide association study of 941,280 people from Europe. Findings: 313,958 current drinkers consumed an average of 13.6 [IQR: 7.1–25.2] units/week alcohol (men averaged 20.2 [11.1–33.9] units/week and women 9.5 [5.3–16.7] units/week). During a mean follow-up of 13.2 years, 5394 (1.7%) developed dementia. Multivariable Cox model with restricted cubic spline functions identified a J-shaped relationship between alcohol consumption and dementia risk, with the lowest risk at 12.2 units/week. The non-linear MR failed to identify a significant non-linear causal relationship (p = 0.45). Both individual-level (HR: 2.22 95%CI [1.06–4.66]) and summary-level (1.89 [1.53–2.32]) linear MR analyses indicated that higher genetically predicted alcohol consumption increased dementia risk. Interpretation: This study identified a positive linear causal relationship between alcohol consumption and dementia among current drinkers. The J-shaped association found in conventional epidemiological analysis was not supported by non-linear MR analyses. Our findings suggested that there was no safe level of alcohol consumption for dementia. Funding: The Shenzhen Science and Technology Program and the Strategic Priority Research Program of Chinese Academy of Sciences.

Summary: Background: How often hypertensive patients could achieve remission to normal blood pressure (BP) (i.e.,

Abstract Background Chronic inflammation and insulin resistance are highly prevalent in patients on maintenance haemodialysis (MHD) and are strongly associated with protein energy wasting. We conducted a pilot, randomized, placebo‐controlled trial of recombinant human interleukin‐1 receptor antagonist (IL‐1ra) and pioglitazone to explore the safety, feasibility and efficacy for insulin‐mediated protein metabolism in patients undergoing MHD. Methods Twenty‐four patients were randomized to receive IL‐1ra, pioglitazone or placebo for 12 weeks. Changes in serum inflammatory markers and insulin‐mediated protein synthesis, breakdown and net balance in the whole‐body and skeletal muscle compartments were assessed using hyperinsulinaemic–hyperaminoacidemic clamp technique at baseline and Week 12. Results Among 24 patients, median (interquartile range) age was 51 (40, 61), 79% were African American and 21% had diabetes mellitus. All patients initiated on intervention completed the study, and no serious adverse events were observed. There was a statistically significant decrease in serum high‐sensitivity C‐reactive protein in the pioglitazone group compared with placebo, but not in the IL‐1ra group. No significant differences in the changes of whole‐body or skeletal muscle protein synthesis, breakdown and net balance were found between the groups. Conclusions In this pilot study, there were no statistically significant effects of 12 weeks of IL‐1ra or pioglitazone on protein metabolism in patients on MHD. Clinicaltrials.gov registration: NCT02278562.

The classification of fetuses as Small for Gestational Age (SGA) and Large for Gestational Age (LGA) is a critical aspect of neonatal health assessment. SGA and LGA, terms used to describe fetal weights that fall below or above the expected weights for Appropriate for Gestational Age (AGA) fetuses, indicate intrauterine growth restriction and excessive fetal growth, respectively. Early prediction and assessment of latent risk factors associated with these classifications can facilitate timely medical interventions, thereby optimizing the health outcomes for both the infant and the mother. This study aims to leverage first-trimester data to achieve these objectives. This study analyzed data from 7943 pregnant women, including 424 SGA, 928 LGA, and 6591 AGA cases, collected from 2015 to 2021 at the Third Affiliated Hospital of Sun Yat-sen University in Guangzhou, China. We propose a novel algorithm, named the Weighted Inheritance Voting Ensemble Learning Algorithm (WIVELA), to predict the classification of fetuses into SGA, LGA, and AGA categories based on biochemical parameters, maternal factors, and morbidity during pregnancy. Additionally, we proposed algorithms for relevance determination based on the classifier to ascertain the importance of features associated with SGA and LGA. The proposed classification solution demonstrated a notable average accuracy rate of 92.12% on 10-fold cross-validation over 100 loops, outperforming five state-of-the-art machine learning algorithms. Furthermore, we identified significant latent maternal risk factors directly associated with SGA and LGA conditions, such as weight change during the first trimester, prepregnancy weight, height, age, and obstetric factors like fetal growth restriction and birthing LGA baby. This study also underscored the importance of biomarker features at the end of the first trimester, including HDL, TG, OGTT-1h, OGTT-0h, OGTT-2h, TC, FPG, and LDL, which reflect the status of SGA or LGA fetuses. This study presents innovative solutions for classifying and identifying relevant attributes, offering valuable tools for medical teams in the clinical monitoring of fetuses predisposed to SGA and LGA conditions during the initial stage of pregnancy. These proposed solutions facilitate early intervention in nutritional care and prenatal healthcare, thereby contributing to enhanced strategies for managing the health and well-being of both the fetus and the expectant mother.

Summary: Background: Constipation is generally considered a common physical symptom of depression or a side effect of antidepressant treatments. However, according to the gut-brain axis hypothesis, the association between depression and constipation might be bi-directional. This study investigated the association between premorbid constipation and depression. Methods: We conducted a retrospective cohort study using data from UK Biobank. Individuals free of depression between 2006 and 2010 were included. Constipation status was determined using diagnostic codes from electronic health records or a baseline questionnaire. Data on covariates, including socio-demographic characteristics, lifestyle factors, health conditions, and regular medication use, were also collected through a baseline questionnaire. The primary outcome is incident depression, which was extracted from hospital inpatient admissions, primary care, self-report, and death data from baseline to 2022. The secondary outcome is depressive symptoms, which was assessed by Patient Health Questionnaire–9 (PHQ-9) from an online survey in 2016. Cox proportional hazard regression models were employed to assess the prospective association between constipation and incident depression. Logistic regression models were used to assess its association with depressive symptoms. Findings: Among the 449,459 participants included in the study, 18,596 (4.1%) experienced constipation at baseline, and 18,576 (4.1%) developed depression over a median follow-up period of 12.3 years. Premorbid constipation is associated with a 2.28-fold higher risk of depression. After adjusting the covariates, we found those with constipation still had a 48% higher risk of developing depression (adjusted hazard ratio [aHR] 1.48; 95% CI, 1.41–1.56) than those without constipation. Self-reported and diagnosed constipation were both associated with a higher risk of depression, with the aHR being 1.42 (95% CI: 1.34–1.51) and 1.66 (95% CI: 1.51–1.82), respectively. Participants with constipation were more likely to report depressive symptoms than people without (adjusted odds ratio 2.18; 95% CI, 1.97–2.43). These findings remained consistent in sensitivity analyses. Interpretation: Diagnosed and self-reported constipation are both prospectively associated with an elevated risk of depression. These explorative findings suggest that constipation may be an independent risk factor or a prodromal symptom of depression. Gastroenterologists and primary care physicians should pay more attention to the depressive symptoms of their constipation patients. Funding: The Shenzhen Science and Technology Program and the Strategic Priority Research Program of Chinese Academy of Sciences.

The significant differences in data domains between SAR images and the expensive and time-consuming process of data labeling pose significant challenges to terrain classification. Current terrain classification methodologies face challenges in addressing domain disparities and detecting uncommon terrain effectively. Based on Style Transformation and Domain Metrics (STDMs), we propose an unsupervised domain adaptive framework named STDM-UDA for terrain classification in this paper, which consists of two steps: image style transfer and domain adaptive segmentation. As a first step, image style transfer is performed within the image space to mitigate the differences in low-level features between SAR image domains. Subsequently, leveraging this process, the segmentation network extracts image features, employing domain metrics and adversarial training to enhance alignment between domain gaps in the semantic feature space. Finally, experiments conducted on several pairs of SAR images, each exhibiting varying degrees of differences in key imaging parameters such as source, resolution, band, and polarization, demonstrate the robustness of the proposed method. It achieves remarkably competitive classification accuracy, particularly for unlabeled, high-resolution broad scenes, effectively overcoming the domain gaps introduced by the diverse imaging parameters under studies.

Effective features play an important role in synthetic aperture radar (SAR) image interpretation. However, since SAR images contain a variety of terrain types, it is not easy to extract effective features of different terrains from SAR images. Deep learning methods require a large amount of labeled data, but the difficulty of SAR image annotation limits the performance of deep learning models. SAR images have inevitable geometric distortion and coherence speckle noise, which makes it difficult to extract effective features from SAR images. If effective semantic context features cannot be learned for SAR images, the extracted features struggle to distinguish different terrain categories. Some existing terrain classification methods are very limited and can only be applied to some specified SAR images. To solve these problems, a jigsaw puzzle self-supervised learning (JPSSL) framework is proposed. The framework comprises a jigsaw puzzle pretext task and a terrain classification downstream task. In the pretext task, the information in the SAR image is learned by completing the SAR image jigsaw puzzle to extract effective features. The terrain classification downstream task is trained using only a small number of labeled data. Finally, fully connected conditional random field processing is performed to eliminate noise points and obtain a high-quality terrain classification result. Experimental results on three large-scene high-resolution SAR images confirm the effectiveness and generalization of our method. Compared with the supervised methods, the features learned in JPSSL are highly discriminative, and the JPSSL achieves good classification accuracy when using only a small amount of labeled data.

The theoretical innovation of adapting Marxism to the Chinese context has put forth a novel concept of green development, signifying a new phase of adapting Marxism to the Chinese contest and needs of the times. Currently, there is a pressing need for in-depth research and understanding of the inherent essence and connotations of green development. Capitalizing on a macroscopic and forward-thinking strategy is essential to explore the new trends, characteristics, and tendencies of green development in the new era. Identifying the objective existence of practical issues in the field of green development, conducting a thorough analysis of the root causes, and proposing corresponding measures are imperative for more effective implementation of the green development concept. This effort aims to contribute to realizing the dream of the great rejuvenation of the Chinese nation in Contemporary Times.

To meet the Biden-Harris administration's goal of deploying 30 GW of offshore wind power by 2030 and 110 GW by 2050, expansion of wind energy into U.S. territorial waters prone to tropical cyclones (TCs) and extratropical cyclones (ETCs) is essential. This requires a deeper understanding of cyclone-related risks and the development of robust, resilient offshore wind energy systems. This paper provides a comprehensive review of state-of-the-science measurement and modeling capabilities for studying TCs and ETCs, and their impacts across various spatial and temporal scales. We explore measurement capabilities for environments influenced by TCs and ETCs, including near-surface and vertical profiles of critical variables that characterize these cyclones. The capabilities and limitations of Earth system and mesoscale models are assessed for their effectiveness in capturing atmosphere–ocean–wave interactions that influence TC/ETC-induced risks under a changing climate. Additionally, we discuss microscale modeling capabilities designed to bridge scale gaps from the weather scale (a few kilometers) to the turbine scale (dozens to a few meters). We also review machine learning (ML)-based, data-driven models for simulating TC/ETC events at both weather and wind turbine scales. Special attention is given to extreme metocean conditions like extreme wind gusts, rapid wind direction changes, and high waves, which pose threats to offshore wind energy infrastructure. Finally, the paper outlines the research challenges and future directions needed to enhance the resilience and design of next-generation offshore wind turbines against extreme weather conditions. [ABSTRACT FROM AUTHOR]

Objective: The current study aimed to investigate the baseline immune and inflammatory features and in-hospital outcomes of patients infected with the Omicron variant (PIWO) who presented with different disease severities during the first wave of mass Omicron infections in the Chinese population has occurred. Method: A cross‐sectional study was conducted on 140 hospitalized PIWO between December 11, 2022, and February 16, 2023. The clinical features, antibodies against SARS-CoV-2, immune cells, and inflammatory cytokines among mildly, severely, and critically ill PIWO at baseline and during follow-up period were compared. Result: Patients with severe (n = 49) and critical (n = 35) disease were primarily male, needed invasive mechanical ventilation treatment, and exhibited higher mortality than those with mild disease (n = 56). During acute infection, SARS-CoV-2-specific antibody levels fluctuated with disease severity, serum antibodies increased and the incidence of severe cases decreased in critically ill PIWO over time. Antibody titers in severe or critical PIWO with no antibody responses at baseline did not increase significantly over time. Meanwhile, CD4+T cell, CD8+T cell, and natural killer cell counts were negatively correlated with disease severity, whereas interleukin (IL)-6 and IL-10 levels were positively correlated. In addition, combined diabetes, immunosuppressive therapy before infection, serum amyloid A, IL-10 and neutrophil counts were independently associated with severe and critical illness in PIWO. Among the 11 nonsurvivors, 8, 2, 1 died of respiratory failure, sudden cardiac death, and renal failure, respectively. Compared with survivors, nonsurvivors exhibited lower seropositivity of SARS-CoV-2-specific antibody, reduced CD3+T and CD4+T cell counts, and higher IL-2R, IL-6, IL-8, and IL-10 levels. Of note, lactate dehydrogenase was a significant risk factor of death in severe or critically ill PIWO. Conclusion: This present study assessed the dynamic changes of SARS-CoV-2-specific antibodies, immune cells and inflammatory indexes between severely and critically ill PIWO. Critical and dead PIWO featured compromised humoral immune response and excessive inflammation, which broadened the understanding of the pathophysiology of Omicron infection and provides warning markers for severe disease and poor prognosis. Highlights : 1. Specific anti-SARS-CoV-2 antibodies showed a trend of increasing first and then decreasing with the progression of disease severity. 2. The severity of infection decreased and the levels of the four antibodies against SARS-CoV-2 increased in critically ill PIWO over time. 3. Negative antibodies in severe/critical PIWO at baseline did not increase significantly with therapeutic intervention. 4. Dead PIWO exhibited disordered features of immunocompromised, hyperinflammatory, abnormal coagulation and myocardial injury. 5. Combined diabetes, immunosuppressive therapy before infection, high levels of SAA, IL-10 and neutrophil counts were the risk factors for severe/critical illness. 6. High LDH was related to death event in severely/critically ill PIWO. [ABSTRACT FROM AUTHOR]

Microsecretory adenocarcinoma (MSA) is a new type of salivary gland neoplasm identified in the 2022 World Health Organization Classification of Head and Neck Tumour (Skalova et al., Head Neck Pathol 16:40-53, 2022) and is characterized by a unique set of histomorphologic and immunohistochemical features and a recurrent MEF2C::SS18 fusion. MSA was initially misdiagnosed as another salivary gland tumour due to its similar morphology; until recently, only fewer than 50 cases were reported. We present a case of MSA of the hard palate with diverse architectural growth patterns, bland cytological features, abundant basophilic intraluminal secretions and fibromyxoid stroma. The tumour cells were positive for the SOX10, S100, and p63 protein and negative for the p40 protein according to immunohistochemistry. SS18 gene rearrangement was demonstrated via break-apart fluorescence in situ hybridization. We also provided a comprehensive literature review and integrated the clinicopathological features, immunophenotype, and molecular alterations of the disease. A comprehensive understanding of MSA enables us to accurately distinguish and categorize MSA from other salivary gland tumours with analogous morphologies. [ABSTRACT FROM AUTHOR]

Atmospheric CO2 flux inversions require as input an estimate of spatial and temporal correlations of errors in their estimate of the prior mean. Some previous studies have used the differences in CO2 daily average flux estimates produced by terrestrial carbon cycle models and eddy covariance measurements to constrain the flux error correlations. Since inversions are starting to resolve the daily cycle, we set out to examine the correlations at sub‐daily time scales, as well as the correlations across years. To this end, we examine the autocorrelations in the difference between net ecosystem‐atmosphere exchange measurements from 75 AmeriFlux towers and temporally downscaled high‐spatial‐resolution flux estimates from the Carnegie‐Ames‐Stanford Approach (CASA) terrestrial carbon cycle model. We find that the daily cycle is prominent in these hourly autocorrelations and that these autocorrelations persist across years. We propose a family of functions to model these temporal correlations in atmospheric inversions, and use cross validation to determine which of the correlation functions best fits autocorrelation data from towers not in the training set. Correlation functions with a component that attempts to model the daily cycle in the differences match correlations from other towers better than those without. Those models that reproduce the same correlation structures at 1‐year intervals while modulating the amplitudes of the correlations between those intervals improve the fit still further. Plain Language Summary: Atmospheric CO2 flux inversions rely heavily on assumptions about errors in the initial flux estimate. If these errors are related in time or in space, inversions need to represent both the shape and the size of the likely errors. Previous studies have looked at the shape of the errors in daily average fluxes, but fewer have looked at the relationship between errors in one part of a day and in another part of that day, or between 1 year and the next. The study examines relationships in differences between measured and computer‐predicted ecosystem‐atmosphere carbon dioxide fluxes. There are strong relationships between flux errors separated by multiple years. Current attempts to solve for true ecosystem‐atmosphere fluxes using atmospheric data do not take these multi‐year relationships into consideration. Accounting for the multi‐year relationships may improve our ability to determine the true ecosystem‐atmosphere CO2 fluxes. We look for descriptions of these relationships that researchers can use when using atmospheric data to improve the computer‐predicted CO2 fluxes. Key Points: CO2 flux model‐data differences have autocorrelations that recur at intervals of a year with little decay over multiple yearsThese autocorrelations are largely caused by errors in the representation of the daily cycleWe can treat these persistent errors in the daily cycle either as prior flux error correlations or by corrections to the prior flux model [ABSTRACT FROM AUTHOR]

Abstract Infrared and visible image fusion involves merging the advantages of infrared and visible images to generate a composite image that encompasses thermal radiation as well as intricate texture details. Infrared and visible image fusion has garnered increasing attention, with numerous fusion methods proposed. However, how to fairly perceive the performance of fused image remains a contentious topic. This paper is dedicated to solving this problem from two perspectives (e.g., subjective and objective aspects). Firstly, an infrared and visible fusion image quality assessment dataset was constructed, including 60 pairs of infrared and visible images captured in various scenes, along with 540 fusion images with different types and degrees of distortions. Additionally, a subjective evaluation dataset of 16,200 subjective scores by 30 participants was further provided for the fused image. Secondly, to overcome the challenging assessment for infrared and visible fusion images without a real reference image, an interesting multi‐domain pseudo‐reference image quality assessment model (MPIQAM) is proposed, by comprehensively considering the thermal radiation information distortion, texture information distortion, and overall naturalness of the fused image. The proposed MPIQAM was compared with 18 mainstream objective metrics, and the experimental findings showcased a commendable level of competitiveness.

Abstract Background Innovative attempt to explore the feasibility and accuracy of using indocyanine green fluorescence (ICGF) to identify the intersegmental plane by the target segmental veins preferential ligation during thoracoscopic segmentectomy. Methods A retrospective analysis was conducted on clinical data of 32 consecutive patients who underwent thoracoscopic segmentectomy with intersegmental plane identification using both ICGF and inflation-deflation method after target segmental veins prioritized blocking at Nanjing Chest Hospital from December 2022 to June 2023. Preoperative three-dimensional reconstruction was used to identify the target segment and the anatomical structure of the arteries, veins, and bronchi. After ligating the target segmental veins during surgery, the first intersegmental plane was immediately identified and marked with an electrocoagulation device using an inflation-deflation method. Subsequently, the second intersegmental plane was determined using the ICGF method. Finally, the consistency of the two intersegmental planes was evaluated. Results All the 32 patients successfully completed thoracoscopic segmentectomy without ICG-related complications and perioperative death. The average operation time was (98.59 ± 20.72) min, the average intraoperative blood loss was (45.31 ± 35.65) ml, and the average postoperative chest tube removal time was (3.5 ± 1.16) days. The average postoperative hospital stay was (4.66 ± 1.29) days, and the average tumor margin width was (26.96 ± 5.86) mm. The intersegmental plane determined by ICGF method was basically consistent with inflation-deflation method in all patients. Conclusion The ICGF can safely and accurately identify the intersegmental plane by target segmental veins preferential ligation during thoracoscopic segmentectomy, which is a beneficial exploration and important supplement to the simplified thoracoscopic anatomical segmentectomy.

Abstract Co-doping of phosphorus and boron elements into crystalline silicon quantum dot (c-Si QD) is an effective approach for enhancing the photoluminescence (PL) performance. In this paper, we report on the preparation of hydrogenated silicon nitride (SiNx:H) thin films embedded with phosphorus-boron co-doped c-Si QDs via plasma enhanced chemical vapor deposition route. Mixed dilution including hydrogen (H2) and argon (Ar) is applied in the in-situ deposition process for optimizing the deposition process. The P-B co-doped c-Si QD/SiNx:H thin films exhibit a wide range of PL spectra. The emission is greatly improved especially for the short-wavelength light when compared to the SiOx:H thin film containing P-B co-doped c-Si QDs. The effects of H2/Ar flow ratio on the structural and optical characteristics of thin films are systematically investigated through a series of characterizations. Experimental results show that various properties, such as crystallinity, QD size, optical band gap and doping concentrations, are effectively controlled by tuning H2/Ar flow ratio. Based on the red-shift of QCE-related PL peak, the successful P-B co-doping into Si QDs are verified. Finally, a comprehensive discussion has been made to analyze the influence of H2-Ar mixed dilution on the film growth and impurity doping in detail in this paper.

At present, with the vigorous development of power Internet of Things, how to sustain and supply energy for a long time has become a technical problem that tens of thousands of sensors need to face. Triboelectric nanogenerator (TENG), as a new high efficiency energy supply technology, has a very broad application prospect in the field of sensor self-power supply. In this paper, based on the working principle of triboelectricity, a multi-layer elastic TENG energy harvesting device for transmission line breeze vibration is designed by using the vertical contact of the multi-layer elastic structure to separate the TENG, and the transmission line breeze vibration state is simulated by the output performance test platform of the vibration energy collector. The basic electrical characteristics of energy collectors at different frequencies and amplitudes are tested, and combined with power management strategies, a self-power supply system for vibration energy acquisition for transmission line breeze vibration is constructed, which realized efficient acquisition and conversion of wide-band vibration energy and completed energy supply for commercial sensors. Finally, the validity and feasibility of the vibration energy collection device designed in this paper are verified by the vibration reduction model of the transmission line. The vibration energy acquisition self-power supply system designed in this paper can effectively convert the broadband vibration energy of the transmission line into electric energy, and has the characteristics of simple structure and good economy, which can provide a new solution to solve the power supply problem of sensors for power Internet of Things.

Abstract Background High-grade serous ovarian cancer (HGSOC) is a common gynecologic malignancy with a poor prognosis. The traditional Chinese medicine formula Erzhimaoling decoction (EZMLD) has anticancer potential. This study aims to elucidate the anticancer effects of EZMLD on HGSOC in vitro and in vivo. Materials and methods EZMLD-containing serum was prepared from Sprague–Dawley rats for treating SKOV3 ovarian cancer cells at varying concentrations for 24 h and 48 h to determine the IC50. Concentrations of 0%, 5%, and 10% for 24 h were chosen for subsequent in vitro experiments. The roles of METTL3 and METTL14 in SKOV3 cells were explored by overexpressing these genes and combining EZMLD with METTL3/14 knockdown. Investigations focused on cell viability and apoptosis, apoptosis-related protein expression, and KRT8 mRNA m6A modification. For in vivo studies, 36 BALB/c nude mice were divided into six groups involving EZMLD (6.75, 13.5, and 27 g/kg) and METTL3 or METTL14 knockdowns, with daily EZMLD gavage for two weeks. Results In vitro, EZMLD-containing serum had IC50 values of 8.29% at 24 h and 5.95% at 48 h in SKOV3 cells. EZMLD-containing serum decreased SKOV3 cell viability and increased apoptosis. EZMLD upregulated METTL3/14 and FAS-mediated apoptosis proteins, while downregulating Keratin 8 (KRT8). EZMLD increased KRT8 mRNA m6A methylation. METTL3/14 overexpression reduced SKOV3 cell viability and increased apoptosis, while METTL3/14 knockdown mitigated EZMLD's effects. In vivo, EZMLD suppressed SKOV3 xenografts growth, causing significant apoptosis and modulating protein expression. Conclusions EZMLD has therapeutic potential for ovarian cancer and may be considered for other cancer types. Future research may explore its broader effects beyond cell apoptosis.

AIM: To investigate the effect of a new type of rhinodacryocystostomy combined with fluticasone propionate on patients with chronic dacryocystitis.METHODS: A total of 100 patients(100 eyes)with chronic dacryocystitis who admitted to our hospital between January 2021 and December 2022 were enrolled in the prospective study. The patients in the study were divided into a control group(n=50)and an observation group(n=50)based on their admission order and number. Patients in the control group were treated with novel rhinodacryocystostomy, while patients in the observation group were treated with a new type of rhinodacryocystostomy combined with fluticasone propionate. The preoperative and postoperative best corrected visual acuity(BCVA), quality of life score, and recurrence of chronic dacryocystitis were compared between the two groups of patients.RESULTS: All patients completed the postoperative 6 mo follow-up, and the total effective rate of patients in the observation group was better than that of patients in the control group(98% vs 84%, P=0.001). There was no significant difference in preoperative and postoperative BCVA between the two groups(P>0.05). Preoperatively, there was no significant difference in the quality of life scores between the two groups of patients(P>0.05); At 6 mo postoperatively, the quality of life scores of patients in the control group, including physical function, psychological function, social function, and material life status, were lower than those in the observation group(all P

Abstract Objective Accumulating evidence supports the idea that inflammation may contribute to the pathophysiology of major depressive disorder (MDD). Duloxetine, a serotonin-norepinephrine reuptake inhibitor, exhibits anti-inflammatory effects both in vitro and in vivo. In this study, we investigated the impact of duloxetine on changes in serum proinflammatory cytokine levels among individuals diagnosed with MDD. Methods A cohort of 23 drug-naïve individuals diagnosed with MDD and 23 healthy controls were included in this study. The severity of depressive symptoms was evaluated using the 24-item Hamilton Depression Scale (HAMD-24). A panel of 7 proinflammatory cytokines, including interleukin-1β (IL-1β), IL-2, IL-6, IL-8, IL-12, tumor necrosis factor-α (TNF-α), and interferon-γ (IFN-γ), were quantified using multiplex Luminex assays. The levels of serum cytokines in healthy controls and patients with MDD were compared at baseline. All patients received duloxetine at a dosage range of 40–60 mg/day for a duration of 4 weeks. The HAMD-24 scores and serum cytokine levels were compared before and after duloxetine treatment. Results Compared with healthy controls, patients with MDD had significantly greater levels of IL-2, IL-6, IL-8, IL-12, TNF-α, and IFN-γ (P 0.05). Conclusions These findings present compelling evidence, potentially for the first time, indicating that duloxetine treatment may effectively reduce the serum concentrations of IL-8, IL-12, and IFN-γ in individuals diagnosed with MDD. However, the precise mechanisms underlying this effect remain unclear and warrant further investigation.

Tunnel boring machines (TBMs) have been widely utilised in tunnel construction due to their high efficiency and reliability. Accurately predicting TBM performance can improve project time management, cost control, and risk management. This study aims to use deep learning to develop real-time models for predicting the penetration rate (PR). The models are built using data from the Changsha metro project, and their performances are evaluated using unseen data from the Zhengzhou Metro project. In one-step forecast, the predicted penetration rate follows the trend of the measured penetration rate in both training and testing. The autoregressive integrated moving average (ARIMA) model is compared with the recurrent neural network (RNN) model. The results show that univariate models, which only consider historical penetration rate itself, perform better than multivariate models that take into account multiple geological and operational parameters (GEO and OP). Next, an RNN variant combining time series of penetration rate with the last-step geological and operational parameters is developed, and it performs better than other models. A sensitivity analysis shows that the penetration rate is the most important parameter, while other parameters have a smaller impact on time series forecasting. It is also found that smoothed data are easier to predict with high accuracy. Nevertheless, over-simplified data can lose real characteristics in time series. In conclusion, the RNN variant can accurately predict the next-step penetration rate, and data smoothing is crucial in time series forecasting. This study provides practical guidance for TBM performance forecasting in practical engineering.

Wildlife conservation and management in human-dominated landscapes are major concerns for wildlife ecologists and managers. The dynamics of human disturbance, combined with seasonal limitations in the availability of nutritious foods, may restrict wildlife population growth and recovery. However, understanding how large mammal species adjust their population distribution in forest habitats with seasonal changes in food and disturbances requires a deeper and more extensive analysis. In this study, we found that three ungulate species, roe deer (Capreolus pygargus), sika deer (Cervus nippon), and wild boar (Sus scrofa), employ robust, conservative, and flexible distribution strategies, respectively, to adapt to the effects of seasonal changes and human disturbances. Moreover, croplands, villages, and grazing have some negative effects on the distribution of roe deer and sika deer, while wild boar can be highly abundant near human land use. Additionally, roe deer, sika deer, and wild boar are also affected by the abundance of shrub species they consume. During the cold season, the populations of the Amur tiger (Panthera tigris altaica) and Amur leopard (P. pardus orientalis) were primarily located near roads and dense forests, respectively. In the warm season, the distribution of both big cats was influenced by prey abundance, and Amur tigers also avoided grazing livestock. Nevertheless, the negative effects of human land use on Amur tigers and wild boars increased during the warm season, which was attributed to more frequent human activities during that time. Consequently, it is crucial to implement season-specific habitat management, particularly by regulating human disturbances during the warm season, in order to promote the recovery and expansion of populations of big cats and ungulates.

Abstract Objective It is known that cytokines play a role in both depression and anxiety. This study aimed to compare the levels of multiple cytokines in patients with first-episode drug-naive major depressive disorder (MDD) with or without anxiety and analyze the correlation between the level of depression or anxiety and the serum cytokine levels. Methods The study involved 55 patients with first-episode drug-naive MDD. To assess anxiety symptoms, the 14-item HAMA was used. MDD patients were divided into two groups: 23 MDD patients without anxiety and 32 MDD patients with anxiety. The measurement of 37 cytokines was conducted. Serum cytokine levels between patients with MDD without anxiety and anxiety were compared. In multiple linear regression models, the relationship between the group and abnormal cytokines was explored. The receiver operating characteristic (ROC) curve analysis was performed to estimate diagnostic performance of serum cytokines in discriminating MDD patients with anxiety from MDD patients without anxiety. A correlation was evaluated between the scores of HAMD or HAMA and the serum cytokine levels. Results In MDD patients with anxiety, IL-17 C and CCL17 levels were significantly lower than in MDD patients without anxiety (all P 0.05). The results of multiple linear regression models revealed that after controlling for other independent variables, group was not a significant independent predictor of serum IL-17 C or CCL17 (all P > 0.05). The AUC values of IL-17 C and CCL17 were 0.643 and 0.637, respectively, in discriminating MDD patients with anxiety from MDD patients without anxiety. The results of partial correlation analyses showed the scores of HAMD were negatively correlated with the IL-17 C (r = -0.314, P = 0.021) levels with sex as a covariate. Conclusions The findings suggest that there is a potential absence of disparity in the levels of circulating cytokines among individuals diagnosed with first-episode drug-naïve MDD, regardless of the presence or absence of comorbid anxiety.

【Objective】 Peanut is an important economic crop and the main planting method is plastic film mulching. It is necessary to study the effects of different degradable film mulching and ordinary plastic film mulching on the soil hydrothermal environment and growth of peanut. 【Method】 Field experiments were conducted at the Experimental Station, China Agricultural University, Rizhao City, Shandong Province, China in 2022 to investigate effects of degradable film and ordinary plastic film mulching on soil hydrothermal conditions and peanut growth and select the type of degradable film suitable for peanut planting. Five mulching treatments (white plastic mulching, WM; black plastic mulching, BM; white degradable mulching, WDM; black degradable mulching, BDM; and no film mulching, CK) were applied. 【Result】 ①The results indicated that degradable mulching had significant effects on soil hydrothermal condition, yield, and water use efficiency. The mean soil temperature at 0-25 cm depth during seedling stage in white degradable mulching treatment was 0.8 ℃ higher than that in white plastic mulching treatment, and that in black degradable mulching treatment was 0.6 ℃ higher than that in black plastic mulching treatment. The mean soil temperature at 5 cm depth at night during seedling stage in white degradable mulching treatment was 0.8 ℃ higher than that in white plastic mulching treatment, and that in black degradable mulching treatment was 0.6 ℃ higher than that in black plastic mulching treatment. The difference of accumulated effective temperature at night during seedling stage in white degradable mulching treatment was 88 ℃ higher than that in white plastic mulching treatment, and that in black degradable mulching treatment was 60 ℃ higher than that in black plastic mulching treatment. The soil water content during whole growth stage in white degradable mulching treatment was 11.7% higher than that in white plastic mulching treatment, and that in black degradable mulching treatment was 7.2% higher than that in black plastic mulching treatment. ②Compared with yield and water use efficiency in white plastic mulching, black plastic mulching and black degradable mulching treatment, the peanut yield in white degradable mulching treatment increased by 5.3%, 6.6% and 17.3%, respectively, and the water use efficiency in white degradable mulching treatment increased by 7.8%, 11.7% and 22.8%, respectively. ③The degradation rate of white degradable film was 14.6% higher than that of black degradable film. 【Conclusion】 The degradable film has similar warming and moisture conservation effects with ordinary plastic film in the hydrothermal environment in peanut planting; the peanut yield and the degradation rate of film in white biodegradable film treatment was higher. So white biodegradable film is the most suitable alternative in Shandong peanut planting.

Three-dimensional plasma nanostructures with high light–thermal conversion efficiency show the prospect of industrialization in various fields and have become a research hotspot in areas of light–heat utilization, solar energy capture, and so on. In this paper, a simple chemical synthesis method is proposed to prepare gold nanoparticles, and the electrophoretic deposition method is used to assemble large-area three-dimensional gold nanostructures (3D-GNSs). The light–thermal water evaporation monitoring and surface-enhanced Raman scattering (SERS) measurements of 3D-GNSs were performed via theoretical simulation and experiments. We reveal the physical processes of local electric field optical enhancement and the light–thermal conversion of 3D-GNSs. The results show that with the help of the efficient optical trapping and super-hydrophilic surface properties of 3D-GNSs, they have a significant effect in accelerating water evaporation, which was increased by nearly eight times. At the same time, the three-dimensional SERS substrates based on gold nanosphere particles (GNSPs) and gold nanostar particles (GNSTs) had limited sensitivities of 10−10 M and 10−12 M to R6G molecules, respectively. Therefore, 3D-GNSs show strong competitiveness in the fields of solar-energy-induced water purification and the Raman trace detection of organic molecules.

AimsTo evaluate the safety, tolerability, pharmacokinetics (PK), and pharmacodynamics (PD) of cetagliptin (CAS number:2243737-33-7) in Chinese patients with type 2 diabetes mellitus (T2DM). A population PK/PD model was developed to quantify the PK and PD characteristics of cetagliptin in patients.Materials and methods32 Chinese adults with T2DM were enrolled in this study. The subjects were randomly assigned to receive either cetagliptin (50 mg or 100 mg), placebo, or sitagliptin (100 mg) once daily for 14 days. Blood samples were collected for PK and PD analysis. Effects on glucose, insulin, C-peptide, and glucagon were evaluated following an oral glucose tolerance test (OGTT) (day15). Effects on HbA1c and glycated albumin (GA), and safety assessments were also conducted. Meanwhile, a population PK/PD model was developed by a sequential two-step analysis approach using Phoenix.ResultsFollowing multiple oral doses, cetagliptin was rapidly absorbed and the mean half-life were 34.9-41.9 h. Steady-state conditions were achieved after 1 week of daily dosing and the accumulation was modest. The intensity and duration of DPP-4 inhibition induced by 50 mg cetagliptin were comparable with those induced by sitagliptin, and 100 mg cetagliptin showed a much longer sustained DPP-4 inhibition (≥80%) than sitagliptin. Compared with placebo group, plasma active GLP-1 AUEC0-24h increased by 2.20- and 3.36-fold in the 50 mg and 100 mg cetagliptin groups. A decrease of plasma glucose and increase of insulin and C-peptide were observed following OGTT in cetagliptin groups. Meanwhile, a tendency of reduced GA was observed, whereas no decreasing trend was observed in HbA1c. All adverse events related to cetagliptin and sitagliptin were assessed as mild. A population PK/PD model was successfully established. The two-compartment model and Sigmoid-Emax model could fit the observed data well. Total bilirubin (TBIL) was a covariate of volume of peripheral compartment distribution (V2), and V2 increased with the increase of TBIL.ConclusionsCetagliptin was well tolerated, inhibited plasma DPP-4 activity, increased plasma active GLP-1 levels, and exhibited a certain trend of glucose-lowering effect in patients with T2DM. The established population PK/PD model adequately described the PK and PD characteristics of cetagliptin.

Due to the widespread applications of change detection technology in urban change analysis, environmental monitoring, agricultural surveillance, disaster detection, and other domains, the task of change detection has become one of the primary applications of Earth orbit satellite remote sensing data. However, the analysis of dual-temporal change detection (CD) remains a challenge in high-resolution optical remote sensing images due to the complexities in remote sensing images, such as intricate textures, seasonal variations in imaging time, climatic differences, and significant differences in the sizes of various objects. In this paper, we propose a novel U-shaped architecture for change detection. In the encoding stage, a multi-branch feature extraction module is employed by combining CNN and transformer networks to enhance the network’s perception capability for objects of varying sizes. Furthermore, a multi-branch aggregation module is utilized to aggregate features from different branches, providing the network with global attention while preserving detailed information. For dual-temporal features, we introduce a spatiotemporal discrepancy perception module to model the context of dual-temporal images. Particularly noteworthy is the construction of channel attention and token attention modules based on the transformer attention mechanism to facilitate information interaction between multi-level features, thereby enhancing the network’s contextual awareness. The effectiveness of the proposed network is validated on three public datasets, demonstrating its superior performance over other state-of-the-art methods through qualitative and quantitative experiments.

Summary: Under stress conditions, translationally stalled mRNA and associated proteins undergo liquid-liquid phase separation and condense into cytoplasmic foci called stress granules (SGs). Many viruses hijack SGs for their pathogenesis; however, whether pathogenic bacteria also exploit this pathway remains unknown. Here, we report that members of the OspC family of Shigella flexneri induce SG formation in infected cells. Mechanistically, the OspC effectors target multiple subunits of the host translation initiation factor 3 complex by ADP-riboxanation. The modification of eIF3 leads to translational arrest and thus the formation of SGs. Furthermore, OspC-mediated SGs are beneficial for S. flexneri replication within infected host cells, and bacterial strains unable to induce SGs are attenuated for virulence in a murine model of infection. Our findings reveal a mechanism by which bacterial pathogens induce SG assembly by inactivating host translational machinery and promote bacterial proliferation in host cells.

BackgroundThe current prevalence of Herpes simplex virus type 2 (HSV-2) infection is notably high, with individuals afflicted by HSV-2 facing recurrent outbreaks, challenges in achieving remission, and an elevated risk of HIV infection. This study aims to investigate the relationship between alcohol consumption and HSV-2 infection.MethodsThe data for this study were sourced from 7257 participants who took part in the National Health and Nutrition Examination Survey (NHANES) from 2009 to 2016. The target population consisted of adults with reliable HSV-2 plasma results, and alcohol consumption was assessed using self-report methods. We evaluated the odds ratio (OR) and 95% confidence interval (CI) for the association between alcohol consumption and HSV-2 infection. These estimations were derived from a logistic regression model that was adjusted for key confounding factors. Subgroup analysis specifically focused on alcohol consumption, and the interaction between HSV-2 infection, alcohol consumption, and other variables was assessed through stratified analysis.ResultsAmong the 7,257 participants included, 89.8% (6,518/7,257) reported varying levels of alcohol consumption history. Compared to individuals who never drinkers, the adjusted odds ratios (ORs) for former drinkers, light drinkers, moderate drinkers, and heavy drinkers were 1.79 (95% CI: 1.34-2.4, p < 0.001), 1.38 (95% CI: 1.07-1.77, p = 0.012), 1.49 (95% CI: 1.15-1.94, p = 0.003), and 1.47 (95% CI: 1.14-1.9, p = 0.003), respectively. The results remained stable in subgroup analyses and sensitivity analyses.ConclusionCurrent research indicates that individuals with a history of alcohol consumption exhibit a higher risk of HSV-2 infection compared to those who have never drinkers.

To explore the role of policy in developing the film and animation industry to better utilize the service function of animation industry policies. This paper constructs a two-dimensional analysis model of the policy, selects and codes the policy text, and uses it as the explanatory variable in multiple linear regression. At the same time, the comprehensive evaluation of the development level of the film and animation industry was selected as the explanatory variable, the index system was constructed by using the diamond theory, and the public factors and weights of the evaluation were determined based on the factor analysis method. Finally, a regression equation was established through multiple regression analysis to explore the influence of policies on the production and revenue of film and television animation and product quality, and the correlation coefficients of the regression equation were determined. Implementing the film and television animation policy resulted in a 56.8% increase in the industry’s main business revenue. At the 0.05 level, the effect was statistically and economically significant.

With the development of artificial intelligence, the ability to capture the background characteristics of hyperspectral imagery (HSI) has improved, showing promising performance in hyperspectral anomaly detection (HAD) tasks. However, existing methods proposed in recent years still suffer from certain limitations: (1) Constraints are lacking in the deep feature learning process in terms of the issue of the absence of prior background and anomaly information. (2) Hyperspectral anomaly detectors with traditional self-supervised deep learning methods fail to ensure prioritized reconstruction of the background. (3) The architecture of fully connected deep networks in hyperspectral anomaly detectors leads to low utilization of spatial information and the destruction of the original spatial relationship in hyperspectral imagery and disregards the spectral correlation between adjacent pixels. (4) Hypotheses or assumptions for background and anomaly distributions restrict the performance of many hyperspectral anomaly detectors because the distributions of background land covers are usually complex and not assumable in real-world hyperspectral imagery. In consideration of the above problems, in this paper, we propose a novel fully convolutional auto-encoder based on dual clustering and latent feature adversarial consistency (FCAE-DCAC) for HAD, which is carried out with self-supervised learning-based processing. Firstly, density-based spatial clustering of applications with a noise algorithm and connected component analysis are utilized for successive spectral and spatial clustering to obtain more precise prior background and anomaly information, which facilitates the separation between background and anomaly samples during the training of our method. Subsequently, a novel fully convolutional auto-encoder (FCAE) integrated with a spatial–spectral joint attention (SSJA) mechanism is proposed to enhance the utilization of spatial information and augment feature expression. In addition, a latent feature adversarial consistency network with the ability to learn actual background distribution in hyperspectral imagery is proposed to achieve pure background reconstruction. Finally, a triplet loss is introduced to enhance the separability between background and anomaly, and the reconstruction residual serves as the anomaly detection result. We evaluate the proposed method based on seven groups of real-world hyperspectral datasets, and the experimental results confirm the effectiveness and superior performance of the proposed method versus nine state-of-the-art methods.

The combination of electrospun nanofibers and nanoparticles is opening up potential in the field of bifunctional materials. Herein, polystyrene (PS, Mw ≈ 260, 000), polyvinyl pyrrolidone (PVP, Mw ≈1, 300, 000), and poly(methyl methacrylate) (PMMA, Mw ≈ 350, 000) bifunctional fibers containing Tb(acac) _3 phen complexes (acac: acetylacetone, phen: 1,10-phenanthroline) and Fe _3 O _4 nanoparticles (NPs) were synthesized by single-fluid electrospinning method. The structure of bifunctional microfibers was characterized by x-ray diffraction (XRD), scanning electron microscope (SEM), transmission electron microscope (TEM), energy spectrum (EDAX) and infrared spectrum (IR). The average diameters of PS, PVP and PMMA bifunctional fibers are 1.65, 0.313 and 0.571 μm, respectively. The TEM images indicated that Fe _3 O _4 NPs were successfully incorporated into bifunctional fibers. No absorption peaks of terbium complexes and Fe _3 O _4 NPs can be seen in the IR spectra of bifunctional fibers. The luminescent and magnetic properties of bifunctional fibers were also investigated. Due to the change of ligands environment, the main excitation peaks blue shifted about 5–8 nm in the bifunctional fibers. Bifunctional fibers exhibited characteristic emission of Tb ^3+ ions. The polymers have no effect on the fluorescence lifetimes of terbium complexes. All bifunctional fibers were soft ferromagnetic. In addition, mechanical performances of these nanofibers were also studied. The maximum stress, strain corresponding to the maximum stress and elastic modulus of sample PVP-Fe-Tb is the largest, which indicates that the tensile performance of sample PVP-Fe-Tb is the best.

Rationale: Hormone receptor-positive, HER2-negative advanced breast cancer complicated by pulmonary lymphangitic carcinomatosis (PLC) poses significant therapeutic challenges due to the lack of standardized treatment protocols. Despite various therapeutic interventions and supportive care, prognosis remains dismal., Patient Concerns: Herein, a 48-year-old Chinese woman presented with a persistent cough, unresponsive to anti-infective treatment for 1 month. A computed tomography (CT) scan revealed lymphatic vessel infiltration and a diffuse nodular pattern, suggestive of PLC., Diagnoses: Hormone receptor-positive, HER2-negative advanced breast cancer complicated by PLC., Interventions: The patient was treated with a regimen comprising low-dose apatinib, capecitabine, and albumin-bound paclitaxel., Outcomes: The patient achieved a partial response, with a progression-free survival exceeding beyond ten months. Symptoms of dyspnea and dry cough significantly improved, alongside a notable reduction in lymphangitic carcinomatosis., Lessons: This case highlights the potential antitumor activity of apatinib in breast cancer patients with presenting with PLC. While further studies are necessary, this therapeutic approach could represent a viable option for managing breast cancer in the context of a visceral crisis. The case also emphasizes the importance of individualized treatment strategies and further research to substantiate these promising findings., Competing Interests: The authors have no conflicts of interest to disclose., (Copyright © 2024 the Author(s). Published by Wolters Kluwer Health, Inc.)