Effects of WeChat-Based Mindfulness-Based Stress Reduction on Psychological Resilience and Quality of Life Among Patients Undergoing Loose Body Removal Surgery.

Background: The knee joint, a structurally intricate component, is highly susceptible to injuries, profoundly impacting the afflicted patients' life quality. Among these injuries, loose body removal stands out, historically managed through conventional open surgical interventions. However, such procedures entail complications, including infection and extended recovery periods, fostering postoperative psychological distress like anxiety and depression. Objective: This study aims to assess the impact of WeChat-based mindfulness-based stress reduction (MBSR) on psychological resilience and quality of life in patients undergoing loose body removal. Methods: A cohort of 87 patients undergoing surgical treatment for loose body removal in our hospital between November 2018 and August 2019 was enlisted. They were randomly allocated to either receive routine postoperative nursing (control group) or participate in WeChat-based MBSR (observation group) using a random number table method. Assessment parameters encompassed anxiety, depression, mindfulness levels, quality of life, personality traits, and psychological resilience. Results: After treatment, patients in the observation group exhibited significantly reduced levels of depression, anxiety, and heightened mindfulness compared to the control group (P < .05). Moreover, the observation group reported superior quality of life and personality traits compared to the control group (P < .05). Additionally, patients in the observation group demonstrated enhanced psychological resilience compared to the control group (P < .05). Conclusions: WeChat-based MBSR emerges as a promising intervention, significantly alleviating depression and anxiety while enhancing the quality of life and psychological resilience in patients undergoing loose body removal surgery, suggesting its potential for widespread clinical application.

Development and validation of a nomogram for predicting in-hospital mortality of intensive care unit patients with liver cirrhosis.

BACKGROUND: Liver cirrhosis patients admitted to intensive care unit (ICU) have a high mortality rate. AIM: To establish and validate a nomogram for predicting in-hospital mortality of ICU patients with liver cirrhosis. METHODS: We extracted demographic, etiological, vital sign, laboratory test, comorbidity, complication, treatment, and severity score data of liver cirrhosis patients from the Medical Information Mart for Intensive Care IV (MIMIC-IV) and electronic ICU (eICU) collaborative research database (eICU-CRD). Predictor selection and model building were based on the MIMIC-IV dataset. The variables selected through least absolute shrinkage and selection operator analysis were further screened through multivariate regression analysis to obtain final predictors. The final predictors were included in the multivariate logistic regression model, which was used to construct a nomogram. Finally, we conducted external validation using the eICU-CRD. The area under the receiver operating characteristic curve (AUC), decision curve, and calibration curve were used to assess the efficacy of the models. RESULTS: Risk factors, including the mean respiratory rate, mean systolic blood pressure, mean heart rate, white blood cells, international normalized ratio, total bilirubin, age, invasive ventilation, vasopressor use, maximum stage of acute kidney injury, and sequential organ failure assessment score, were included in the multivariate logistic regression. The model achieved AUCs of 0.864 and 0.808 in the MIMIC-IV and eICU-CRD databases, respectively. The calibration curve also confirmed the predictive ability of the model, while the decision curve confirmed its clinical value. CONCLUSION: The nomogram has high accuracy in predicting in-hospital mortality. Improving the included predictors may help improve the prognosis of patients.

The Clinical Investigation on Varicocele and Sperm Quality in the Tibet an Plateau of China: A Prospectively Planned Retrospective Cohort Study.

Varicoceles are a common cause of male infertility, affecting up to 35% of men undergoing fertility evaluations. This study aims to investigate the potential influence of altitude and residence time on the occurrence of varicoceles, as well as on sperm quality and sterility in plateau areas. A total of 168 patients with varicocele were enrolled in the study, and the study population was divided into groups based on their direct exposure to different high altitudes due to their living locations. The internal diameter in Quiet breath (Dr), internal diameter in Valsalva maneuver (Dv), reflux peak value, and reflux time are gradually increased accompanied with altitude elevation and residence time extension. The number of cases above 4,500 m also increased with the severity of varicocele, and the altitude of clinical types was higher than that of subclinical types of varicocele. Especially above 4,500 m, the Dv, Dr, reflux peak value, and reflux time all increased with the severity of varicocele. The severity of varicocele was positively correlated with the residence time in plateau area. Patients with residence time of more than 1 year had higher values of Dr, Dv, differentiation time, reflux peak value, and reflux time than those with residence time of less than 1 year. Compared to 3,650 m, patients with varicocele in 4,500 m also have worse semen quality. Both altitude and residence time are strongly positively related to the severity and incidence rate of varicocele in plateau areas.

The First Case Report of Inactive Nontuberculous Mycobacterial Pulmonary Disease (NTM-PD) in a Pneumoconiosis Patient Caused by Mycobacterium europaeum in China.

We reported a 51-year-old male electric welder with stage I pneumoconiosis, who had no significant cough, sputum, fever, chest pain, or other discomfort. However, regular physical examination at our hospital revealed bilateral pulmonary nodules with cavity formation. Blood routine, liver or kidney function, and infection-related biomarkers, including interleukin-6 (IL-6), high-sensitivity C-reactive protein (hs-CRP), and procalcitonin (PCT), were normal. Sputum and alveolar lavage fluid (BALF) acid-fast bacilli (AFB) smears, BALF Mycobacterium tuberculosis (TB) PCR, and T-SPOT.TB were negative. The nucleic acid sequence of Mycobacterium europaeum was detected by BALF metagenomic next-generation sequencing (mNGS), which was confirmed by the subsequent positive culture for NTM. Considering stable conditions, no significant discomfort, and no significant changes in the lung lesion, the patient was diagnosed with inactive nontuberculous mycobacterial pulmonary disease (NTM-PD).

d-type peptides based fluorescent probes for "turn on" sensing of heparin.

Developing "turn on" fluorescent probes was desirable for the detection of the effective anticoagulant agent heparin in clinical applications. Through combining the aggregation induced emission (AIE) fluorogen tetraphenylethene (TPE) and heparin specific binding peptide AG73, the promising "turn on" fluorescent probe TPE-1 has been developed. Nevertheless, although TPE-1 could achieve the sensitive and selective detection of heparin, the low proteolytic stability and undesirable poor solubility may limit its widespread applications. In this study, seven TPE-1 derived fluorescent probes were rationally designed, efficiently synthesized and evaluated. The stability and water solubility were systematically estimated. Especially, to achieve real-time monitoring of proteolytic stability, the novel Abz/Dnp-based "turn on" probes that employ the internally quenched fluorescent (IQF) mechanism were designed and synthesized. Moreover, the detection ability of synthetic fluorescent probes for heparin were systematically evaluated. Importantly, the performance of d-type peptide fluorescent probe XH-6 indicated that d-type amino acid substitutions could significantly improve the proteolytic stability without compromising its ability of heparin sensing, and attaching solubilizing tag 2-(2-aminoethoxy) ethoxy) acid (AEEA) could greatly enhance the solubility. Collectively, this study not only established practical strategies to improve both the water solubility and proteolytic stability of "turn on" fluorescent probes for heparin sensing, but also provided valuable references for the subsequent development of enzymatic hydrolysis-resistant d-type peptides based fluorescent probes.

The role of fungi in the diagnosis of colorectal cancer.

Colorectal cancer (CRC) is a prevalent tumour with high morbidity rates worldwide, and its incidence among younger populations is rising. Early diagnosis of CRC can help control the associated mortality. Fungi are common microorganisms in nature. Recent studies have shown that fungi may have a similar association with tumours as bacteria do. As an increasing number of tumour-associated fungi are discovered, this provides new ideas for the diagnosis and prognosis of tumours. The relationship between fungi and colorectal tumours has also been recently identified by scientists. Therefore, this paper describes the limitations and prospects of the application of fungi in diagnosing CRC and predicting CRC prognosis.

Association between dietary fatty acids and urinary incontinence.

Background: Dietary nutrient intake contributes to urination; however, the association between dietary nutrient intake, especially that of fat, and urinary incontinence (UI) is not well understood. The most common types of UI include stress UI (SUI) and urgency UI (UUI). Objective: To investigate the potential effect(s) of dietary fat intake on UI and explore its mechanism of action in relation to body mass index (BMI). Methods: A cross-sectional survey of data from 15,121 individuals (20-85 years of age) from the National Health and Nutrition Examination Survey (2001-2008), a random population-based sample, was performed. Data regarding dietary nutrient intake were collected through 24 h dietary recall interviews. UI and covariate data were collected through in-person interviews. UI was assessed according to the American Urological Association Symptom Index. The odds ratio (OR) for SUI and UUI were calculated using multivariate logistic regression analysis. The mediation effect was estimated using observational mediation analysis. Results: Higher total fat intake was positively associated with increased odds for developing UI (OR 1.44 [95% confidence interval (CI) 1.08-1.93]). Females who consumed more saturated fatty acids (SFA), monounsaturated fatty acids (MUFA), and polyunsaturated fatty acids (PUFA) were more likely to develop SUI. BMI partially explained the association between total fat, SFA, MUFA, and PUFA and SUI; the proportions of the mediation effect of BMI were 14.7%, 13.0%, 18.7%, and 16.3%, respectively. Conclusions: Results of this study emphasize the key role of dietary fat intake in the prevalence of UI. Higher fat intake was positively associated with UI and BMI partially mediated the effect of fat intake on SUI.

The association of the prothrombin A19911G single-nucleotide polymorphism and the risk of venous thromboembolism: A systematic review and meta-analysis.

BACKGROUND: The study employed meta-analysis to provide a comprehensive synthesis of evidence regarding the association between the prothrombin A19911G polymorphism and the risk of venous thromboembolism (VTE). METHOD: The databases were searched to identify studies investigating the association between the prothrombin A19911G polymorphism and the risk of VTE. Meta-analysis was conducted using Stata 14.0 software. RESULTS: A total of five literature studies were included, involving 14,001 participants. Meta-analysis demonstrated that prothrombin A19911G polymorphism increased the risk of VTE (G vs A: OR = 1.17, 95% CI = 1.11-1.22, p < .00001; GG + AG vs AA: OR = 1.22, 95% CI = 1.13-1.31, p < .00001; GG vs AG + AA: OR = 1.23, 95% CI = 1.14-1.33, p < .00001; AG vs AA: OR = 1.15, 95% CI = 1.06-1.25, p = .0006; GG vs AA: OR = 1.34, 95% CI = 1.22-1.48, p < .00001). CONCLUSION: The polymorphism of prothrombin A19911G enhances the susceptibility to VTE.

TCMM: A unified database for traditional Chinese medicine modernization and therapeutic innovations.

Mining the potential of traditional Chinese medicine (TCM) in treating modern diseases requires a profound understanding of its action mechanism and a comprehensive knowledge system that seamlessly bridges modern medical insights with traditional theories. However, existing databases for modernizing TCM are plagued by varying degrees of information loss, which impede the multidimensional dissection of pharmacological effects. To address this challenge, we introduce traditional Chinese medicine modernization (TCMM), the currently largest modernized TCM database that integrates pioneering intelligent pipelines. By aligning high-quality TCM and modern medicine data, TCMM boasts the most extensive TCM modernization knowledge, including 20 types of modernized TCM concepts such as prescription, ingredient, target and 46 biological relations among them, totaling 3,447,023 records. We demonstrate the efficacy and reliability of TCMM with two features, prescription generation and knowledge discovery, the outcomes show consistency with biological experimental results. A publicly available web interface is at https://www.tcmm.net.cn/.

Design and optimization of haze prediction model based on particle swarm optimization algorithm and graphics processor.

With the rapid expansion of industrialization and urbanization, fine Particulate Matter (PM2.5) pollution has escalated into a major global environmental crisis. This pollution severely affects human health and ecosystem stability. Accurately predicting PM2.5 levels is essential. However, air quality forecasting currently faces challenges in processing vast data and enhancing model accuracy. Deep learning models are widely applied for their superior learning and fitting abilities in haze prediction. Yet, they are limited by optimization challenges, long training periods, high data quality needs, and a tendency towards overfitting. Furthermore, the complex internal structures and mechanisms of these models complicate the understanding of haze formation. In contrast, traditional Support Vector Regression (SVR) methods perform well with complex non-linear data but struggle with increased data volumes. To address this, we developed CUDA-based code to optimize SVR algorithm efficiency. We also combined SVR with Genetic Algorithms (GA), Sparrow Search Algorithm (SSA), and Particle Swarm Optimization (PSO) to identify the optimal haze prediction model. Our results demonstrate that the model combining intelligent algorithms with Central Processing Unit-raphics Processing Unit (CPU-GPU) heterogeneous parallel computing significantly outpaces the PSO-SVR model in training speed. It achieves a computation time that is 6.21-35.34 times faster. Compared to other models, the Particle Swarm Optimization-Central Processing Unit-Graphics Processing Unit-Support Vector Regression (PSO-CPU-GPU-SVR) model stands out in haze prediction, offering substantial speed improvements and enhanced stability and reliability while maintaining high accuracy. This breakthrough not only advances the efficiency and accuracy of haze prediction but also provides valuable insights for real-time air quality monitoring and decision-making.

Dual-functional ionic liquids additive enables dendrite-free Zn anode with ultra-long cycle life over one year.

Zn anodes suffer from the formation of uncontrolled dendrites aggravated by the uneven electric field and the insulating by-product accumulation in aqueous zinc-ion batteries (AZIBs). Here, an effective strategy implemented by 1-butyl-3-methylimidazolium hydrogen sulfate (BMIHSO4) additive is proposed to synergistically tune the crystallographic orientation of zinc deposition and suppress the formation of zinc hydroxide sulfate for enhancing the reversibility on Zn anode surface. As a competing cation, BMI+ is proved to preferably adsorb on Zn-electrode compared with H2O molecules, which shields the "tip effect" and inhibits the Zn-deposition agglomerations to inducing the horizontal growth along Zn (002) crystallographic texture. Simultaneously, the protonated BMIHSO4 additives could remove the detrimental OH- in real-time to fundamentally eliminate the accumulation of 6Zn(OH)2·ZnSO4·4H2O and Zn4SO4(OH)6·H2O on Zn anode surface. Consequently, Zn anode exhibits an ultra-long cycling stability of one year (8762 h) at 0.2 mA cm-2/0.2 mAh cm-2, 3600 h at 2 mA cm-2/2 mAh cm-2 with a high plating cumulative capacity of 3.6 Ah cm-2, and a high average Coulombic efficiency of 99.6 % throughout 1000 cycles. This work of regulating Zn deposition texture combined with eliminating notorious by-products could offer a desirable way for stabilizing the Zn-anode/electrolyte interface in AZIBs.

Photocatalysis Meets Piezoelectricity in a Type-I Oxygen Vacancy-Rich BaTiO3/BiOBr Heterojunction: Mechanism Insights from Characterizations to DFT Calculations.

It is a challenging task to design a piezoelectric photocatalyst with excellent performance under mechanical agitation instead of ultrasonic irradiation. Integrating vacancy defects into a heterojunction seems to be an effective strategy for synergistically increasing its piezo-photocatalytic performance. For this goal, a two-step hydrothermal method was adopted to architect a type-I oxygen-vacancy-rich BaTiO3/BiOBr heterojunction to surge the degradation of Rhodamine B (RhB) under the combined action of simulated sunlight irradiation and mechanical agitation. Various instrumental techniques demonstrated the formation of a BaTiO3/BiOBr heterojunction with high crystallinity. The existence of surface oxygen vacancies was confirmed by XPS and EPR tests. PFM results manifested that this heterojunction had excellent piezoelectric properties, with a piezoelectric response value of 30.31 pm V-1. Comparative experiments indicated that RhB degradation efficiency under piezo-photocatalysis over this heterojunction largely exceeded the total sum of those under piezocatalysis and photocatalysis. h+, ·O2-, and 1O2 were the dominant reactive species for RhB degradation. The improved separation efficiency of photogenerated charges was verified by electrochemical measurements. DFT calculations indicated that the polarization of BaTiO3 could affect the electronic band structure of BiOBr. This work will provide comprehensive insights into piezo-photocatalytic mechanism at a microcosmic level and help to develop new-styled piezoelectric photocatalysts.

Bacteria-Targeting Nanoparticles with ROS-Responsive Antibiotic Release to Eradicate Biofilms and Drug-Resistant Bacteria in Endophthalmitis.

Background: Bacterial endophthalmitis is an acute progressive visual threatening disease and one of the most important causes of blindness worldwide. Current treatments are unsatisfactory due to the emergence of drug-resistant bacteria and the formation of biofilm. Purpose: The aim of our research was to construct a novel nano-delivery system with better antimicrobial and antibiofilm effects. Methods: This study developed a novel antibiotic nanoparticle delivery system (MXF@UiO-UBI-PEGTK), which is composed of (i) moxifloxacin (MXF)-loaded UiO-66 nanoparticle as the core, (ii) bacteria-targeting peptide ubiquicidin (UBI29-41) immobilized on UiO-66, and (iii) ROS-responsive poly (ethylene glycol)-thioketal (PEG-TK) as the surface shell. Then the important properties of the newly developed delivery system, including biocompatibility, toxicity, release percentage, thermal stability, ability of targeting bacteria, and synergistic antibacterial effects on bacterial biofilms and endophthalmitis, were evaluated. Results: In vitro, MXF@UiO-UBI-PEGTK exhibited significant antibiotic effects including the excellent antibiofilm property against Staphylococcus aureus, Pseudomonas aeruginosa, and methicillin-resistant Staphylococcus aureus at high levels of ROS. Moreover, MXF@UiO-UBI-PEGTK demonstrated outstanding efficacy in treating bacterial endophthalmitis in vivo. Conclusion: This novel nanoparticle delivery system with ROS-responsive and bacteria-targeted properties promotes the precise and effective release of drugs and has significant potential for clinical application of treating bacterial endophthalmitis.

Platelet Concentrates Preconditioning of Mesenchymal Stem Cells and Combined Therapies: Integrating Regenerative Strategies for Enhanced Clinical Applications.

This article presents a comprehensive review of the factors influencing the efficacy of mesenchymal stem cells (MSCs) transplantation and its association with platelet concentrates (PCs). It focuses on investigating the impact of PCs' composition, the age and health status of platelet donors, application methods, and environmental factors on the outcomes of relevant treatments. In addition, it delves into the strategies and mechanisms for optimizing MSCs transplantation with PCs, encompassing preconditioning and combined therapies. Furthermore, it provides an in-depth exploration of the signaling pathways and proteomic characteristics associated with preconditioning and emphasizes the efficacy and specific effects of combined therapy. The article also introduces the latest advancements in the application of biomaterials for optimizing regenerative medical strategies, stimulating scholarly discourse on this subject. Through this comprehensive review, the primary goal is to facilitate a more profound comprehension of the factors influencing treatment outcomes, as well as the strategies and mechanisms for optimizing MSCs transplantation and the application of biomaterials in regenerative medicine, offering theoretical guidance and practical references for related research and clinical practice.

Combination prediction method of students' performance based on ant colony algorithm.

Students' performance is an important factor for the evaluation of teaching quality in colleges. The prediction and analysis of students' performance can guide students' learning in time. Aiming at the low accuracy problem of single model in students' performance prediction, a combination prediction method is put forward based on ant colony algorithm. First, considering the characteristics of students' learning behavior and the characteristics of the models, decision tree (DT), support vector regression (SVR) and BP neural network (BP) are selected to establish three prediction models. Then, an ant colony algorithm (ACO) is proposed to calculate the weight of each model of the combination prediction model. The combination prediction method was compared with the single Machine learning (ML) models and other methods in terms of accuracy and running time. The combination prediction model with mean square error (MSE) of 0.0089 has higher performance than DT with MSE of 0.0326, SVR with MSE of 0.0229 and BP with MSE of 0.0148. To investigate the efficacy of the combination prediction model, other prediction models are used for a comparative study. The combination prediction model with MSE of 0.0089 has higher performance than GS-XGBoost with MSE of 0.0131, PSO-SVR with MSE of 0.0117 and IDA-SVR with MSE of 0.0092. Meanwhile, the running speed of the combination prediction model is also faster than the above three methods.

The burden of mental disorders in Asian countries, 1990-2019: an analysis for the global burden of disease study 2019.

Mental disorders are the leading contributors to the globally nonfatal burden of disease. This study was aimed to estimate the burden of mental disorders in Asian countries. Based on GBD 2019, the prevalence and disability-adjusted life of years (DALYs) rates with 95% uncertainty intervals (UI) were estimated in Asian countries. Predictions for the future burden of 8 selected countries, ranks of the burden of mental disorders and correlations with Sociodemographic Index (SDI) were also estimated. During the past 3 decades, while the number of DALYs of mental disorders increased from 43.9 million (95% UI: 32.5-57.2) to 69.0 million (95% UI: 51.0-89.7), the age-standardized rates of DALYs of mental disorders remained largely consistent from 1452.2 (95% UI: 1080.16-1888.53) per 100,000 population in 1990 to 1434.82 (95% UI: 1065.02-1867.27) per 100,000 population in 2019, ranked as the eighth most significant disease burden in Asia in 2019. Depressive disorders (37.2%) were the leading contributors to the age-standardized DALY rates of mental disorders in Asia, followed by anxiety disorders (21.5%). The age-standardized DALY rates in females were higher than their male counterparts, both peaked at 30-34 years. The age-standardized DALY rates were predicted to remain stable, with the number of DALYs presented an upward trend in the future. There was no significant correlation between the burden of mental disorders and SDI. All mental disorders ranked higher in 2019, compared in 1990. To reduce this burden, urgent measures for prevention, treatment, and rehabilitation for mental disorders need to be taken by Asian governments.

Reproductive toxicity of cadmium stress in male animals.

Cadmium (Cd) is a common heavy metal pollutant in the environment, and the widespread use of products containing Cd compounds in industry has led to excessive levels in the environment, which enter the animal body through the food chain, thus seriously affecting the reproductive development of animals. Related studies have reported that Cd severely affects spermatogonia development and spermatogenesis in animals. In contrast, the reproductive toxicity of Cd in males and its mechanism of action have not been clarified. Therefore, this paper reviewed the toxic effects of Cd on germ cells, spermatogonia somatic cells and hypothalamic-pituitary-gonadal axis (HPG axis) of male animals and its toxic action mechanisms of oxidative stress, apoptosis and autophagy from the perspectives of cytology, genetics and neuroendocrinology. The effects of Cd stress on epigenetic modification of reproductive development in male animals were also analyzed. We hope to provide a reference for the in-depth study of the toxicity of Cd on male animal reproduction.

Cytokine expression patterns: A single-cell RNA sequencing and machine learning based roadmap for cancer classification.

Cytokines are small protein molecules that exhibit potent immunoregulatory properties, which are known as the essential components of the tumor immune microenvironment (TIME). While some cytokines are known to be universally upregulated in TIME, the unique cytokine expression patterns have not been fully resolved in specific types of cancers. To address this challenge, we develop a TIME single-cell RNA sequencing (scRNA-seq) dataset, which is designed to study cytokine expression patterns for precise cancer classification. The dataset, including 39 cancers, is constructed by integrating 684 tumor scRNA-seq samples from multiple public repositories. After screening and processing, the dataset retains only the expression data of immune cells. With a machine learning classification model, unique cytokine expression patterns are identified for various cancer categories and pioneering applied to cancer classification with an accuracy rate of 78.01%. Our method will not only boost the understanding of cancer-type-specific immune modulations in TIME but also serve as a crucial reference for future diagnostic and therapeutic research in cancer immunity.

Reduced representative methylome profiling of cell-free DNA for breast cancer detection.

BACKGROUND: Whole-genome methylation sequencing of cfDNA is not cost-effective for tumor detection. Here, we introduce reduced representative methylome profiling (RRMP), which employs restriction enzyme for depletion of AT-rich sequence to achieve enrichment and deep sequencing of CG-rich sequences. METHODS: We first verified the ability of RRMP to enrich CG-rich sequences using tumor cell genomic DNA and analyzed differential methylation regions between tumor cells and normal whole blood cells. We then analyzed cfDNA from 29 breast cancer patients and 27 non-breast cancer individuals to detect breast cancer by building machine learning models. RESULTS: RRMP captured 81.9% CpG islands and 75.2% gene promoters when sequenced to 10 billion base pairs, with an enrichment efficiency being comparable to RRBS. RRMP allowed us to assess DNA methylation changes between tumor cells and whole blood cells. Applying our approach to cfDNA from 29 breast cancer patients and 27 non-breast cancer individuals, we developed machine learning models that could discriminate between breast cancer and non-breast cancer controls (AUC = 0.85), suggesting possibilities for truly non-invasive cancer detection. CONCLUSIONS: We developed a new method to achieve reduced representative methylome profiling of cell-free DNA for tumor detection.