Skeletal muscle index based on CT at the 12th thoracic spine level can predict osteoporosis and fracture risk: a propensity score-matched cohort study.

Background: Multiple studies have shown that skeletal muscle index (SMI) measured on abdominal computed tomography (CT) is strongly associated with bone mineral density (BMD) and fracture risk as estimated by the fracture risk assessment tool (FRAX). Although some studies have reported that SMI at the level of the 12th thoracic vertebra (T12) measured on chest CT images can be used to diagnose sarcopenia, it is regrettable that no studies have investigated the relationship between SMI at T12 level and BMD or fracture risk. Therefore, we further investigated the relationship between SMI at T12 level and FRAX-estimated BMD and fracture risk in this study. Methods: A total of 349 subjects were included in this study. After 1∶1 propensity score matching (PSM) on height, weight, hypertension, diabetes, hyperlipidemia, hyperuricemia, body mass index (BMI), age, and gender, 162 subjects were finally included. The SMI, BMD, and FRAX score of the 162 participants were obtained. The correlation between SMI and BMD, as well as SMI and FRAX, was assessed using Spearman rank correlation. Additionally, the effectiveness of each index in predicting osteoporosis was evaluated through the receiver operating characteristic (ROC) curve analysis. Results: The BMD of the lumbar spine (L1-4) demonstrated a strong correlation with SMI (r = 0.416, p < 0.001), while the BMD of the femoral neck (FN) also exhibited a correlation with SMI (r = 0.307, p < 0.001). SMI was significantly correlated with FRAX, both without and with BMD at the FN, for major osteoporotic fractures (r = -0.416, p < 0.001, and r = -0.431, p < 0.001, respectively) and hip fractures (r = -0.357, p < 0.001, and r = -0.311, p < 0.001, respectively). Moreover, the SMI of the non-osteoporosis group was significantly higher than that of the osteoporosis group (p < 0.001). SMI effectively predicts osteoporosis, with an area under the curve of 0.834 (95% confidence interval 0.771-0.897, p < 0.001). Conclusion: SMI based on CT images of the 12th thoracic vertebrae can effectively diagnose osteoporosis and predict fracture risk. Therefore, SMI can make secondary use of chest CT to screen people who are prone to osteoporosis and fracture, and carry out timely medical intervention.

[Traditional Chinese medicine preparations in medical institutions: current situation and inspirations on research and development of new traditional Chinese medicine preparations].

Traditional Chinese medicine(TCM) preparations in medical institutions embody the characteristics of TCM and are the source for the development of new TCM drugs. This study summarizes the current situation, existing problems, and development trends of the TCM preparations in medical institutions in 31 provinces across China. Furthermore, this paper puts forward the development path of new TCM preparations based on the requirements of registration and management regulations of TCM preparations, providing new ideas for promoting the inheritance, innovation, and development of TCM.

Quadrat soil pollen signal reflects plant important values in forests and shrublands from subtropical China.

Pollen analysis, a crucial tool in botany and ecology for examining historical biotic dynamics, has elicited debate owing to its complex link with vegetation. The challenge lies in discerning the ecological significance of pollen data. In this study, we conducted detailed quadrat surveys on Jinhua Mountain, subtropical China, analyzing topsoil pollen to determine whether pollen signals accurately reflect key ecological components in the forests and shrublands. We performed direct comparisons between pollen and plant compositions and calculated pollen percentages and plant Important Values (IVs) for each quadrat. The results indicate greater homogeneity in pollen composition across the study area compared to plant composition, particularly in the high percentage of Pinus pollen. However, distinct plant communities exhibited significantly different pollen compositions, as evidenced by the multi-response permutation test. This divergence aligns with variations in the dominant plant species across different communities. There were significant correlations between pollen percentages and plant IVs, with correlation coefficients of 0.55 (p < 0.001) at the quadrat level and 0.78 (p < 0.001) at the taxon level. These results support the utility of pollen analysis for representing ecologically significant values in subtropical Chinese forests and shrublands. Such correlations might also be extrapolated to pollen-based paleoecological studies.

Analysis of risk factors leading to anxiety and depression in patients with prostate cancer after castration and the construction of a risk prediction model.

BACKGROUND: Cancer patients often suffer from severe stress reactions psychologically, such as anxiety and depression. Prostate cancer (PC) is one of the common cancer types, with most patients diagnosed at advanced stages that cannot be treated by radical surgery and which are accompanied by complications such as bodily pain and bone metastasis. Therefore, attention should be given to the mental health status of PC patients as well as physical adverse events in the course of clinical treatment. AIM: To analyze the risk factors leading to anxiety and depression in PC patients after castration and build a risk prediction model. METHODS: A retrospective analysis was performed on the data of 120 PC cases treated in Xi'an People's Hospital between January 2019 and January 2022. The patient cohort was divided into a training group (n = 84) and a validation group (n = 36) at a ratio of 7:3. The patients' anxiety symptoms and depression levels were assessed 2 wk after surgery with the Self-Rating Anxiety Scale (SAS) and the Self-rating Depression Scale (SDS), respectively. Logistic regression was used to analyze the risk factors affecting negative mood, and a risk prediction model was constructed. RESULTS: In the training group, 35 patients and 37 patients had an SAS score and an SDS score greater than or equal to 50, respectively. Based on the scores, we further subclassified patients into two groups: a bad mood group (n = 35) and an emotional stability group (n = 49). Multivariate logistic regression analysis showed that marital status, castration scheme, and postoperative Visual Analogue Scale (VAS) score were independent risk factors affecting a patient's bad mood (P < 0.05). In the training and validation groups, patients with adverse emotions exhibited significantly higher risk scores than emotionally stable patients (P < 0.0001). The area under the curve (AUC) of the risk prediction model for predicting bad mood in the training group was 0.743, the specificity was 70.96%, and the sensitivity was 66.03%, while in the validation group, the AUC, specificity, and sensitivity were 0.755, 66.67%, and 76.19%, respectively. The Hosmer-Lemeshow test showed a χ2 of 4.2856, a P value of 0.830, and a C-index of 0.773 (0.692-0.854). The calibration curve revealed that the predicted curve was basically consistent with the actual curve, and the calibration curve showed that the prediction model had good discrimination and accuracy. Decision curve analysis showed that the model had a high net profit. CONCLUSION: In PC patients, marital status, castration scheme, and postoperative pain (VAS) score are important factors affecting postoperative anxiety and depression. The logistic regression model can be used to successfully predict the risk of adverse psychological emotions.

Identification and Validation of Hub Genes Related to Neutrophil Extracellular Traps-Mediated Cell Damage During Myocardial Infarction.

Purpose: Studies have shown that neutrophil-mediated formation of neutrophil extracellular traps (NETs) leads to increased inflammatory response and cellular tissue damage during myocardial infarction (MI). We aimed to identify and validate possible hub genes in the process of NETs-mediated cell damage. Methods: We performed an immune cell infiltration analysis of the MI transcriptome dataset based on CIBERSORT and ssGSEA algorithms. Gene expression profiles of NETs formation (GSE178883) were used to analyze the physiological processes of peripheral blood neutrophils after phorbol myristate acetate (PMA) stimulation. Bioinformatics and machine learning algorithms were utilized to find candidate hub genes based on NETs-related genes and transcriptome datasets (GSE66360 and GSE179828). We generated the receiver operating curve (ROC) to evaluate the diagnostic value of hub genes. Next, the correlation between hub genes and immune cells was analyzed using CIBERSORT, ssGSEA and xCell algorithms. Finally, we used quantitative real-time PCR (qRT-PCR) and immunohistochemistry to verify gene expression. Results: Immune cell infiltration analysis revealed that inflammatory cells such as neutrophils were highly expressed in the peripheral blood of patients with MI. Functional analysis of differentially expressed genes (DEGs) in GSE178883 indicated that the potential pathogenesis lies in immune terms. Using weighted gene co-expression network analysis (WGCNA) and machine learning algorithms, we finally identified the seven hub genes (FCAR, IL1B, MMP9, NFIL3, CXCL2, ICAM1, and ZFP36). The qRT-PCR results showed that IL-1B, MMP9, and NFIL3 mRNA expression was up-regulated in the MI group compared to the control. Immunohistochemical results showed high MMP9, IL-1B, and NFIL3 expression in the infarcted area compared to the non-infarcted area and sham-operated groups. Conclusion: We identified seven hub genes associated with NETs-mediated cellular damage during MI. Our results may provide insights into the mechanisms of neutrophil-mediated cell injury during MI.

The Role of Climate Change and Its Sensitivity on Long-Term Standardized Precipitation Evapotranspiration Index, Vegetation and Drought Changing Trends over East Asia.

Droughts have become more severe and frequent due to global warming. In this context, it is widely accepted that for drought assessments, both water supply (rainfall) and demand (standardized precipitation evapotranspiration index, SPEI) should be considered. Using SPEI, we explored the spatial-temporal patterns of dry and wet annual and seasonal changes in five sub-regions of East Asia during 1902-2018. These factors are linked to excess drought frequency and severity on the regional scale, and their effect on vegetation remains an important topic for climate change studies. Our results show that the SPEI significantly improved extreme drought and mostly affected the SPEI-06 and SPEI-12 growing seasons in East Asia during 1981-2018. The dry and wet annual SPEI trends mostly affect the five sub-regions of East Asia. The annual SPEI had two extremely dry spells during 1936-1947 and 1978-2018. Japan, South Korea, and North Korea are wet in the summer compared to other regions of East Asia, with drought frequency occurring at 51.4%, respectively. The mean drought frequencies in China and Mongolia are 57.4% and 54.6%. China and Mongolia are the driest regions in East Asia due to high drought frequency and duration. The spatial seasonal analysis of solar radiation (SR), water vapor pressure (WVP), wind speed (WS), vegetation condition index (VCI), temperature condition index (TCI), and vegetation health index (VHI) have confirmed that the East Asia region suffered from maximum drought events. The seasonal variation of SPEI shows no clear drying trends during summer and autumn seasons. During the winter and spring seasons, there was a dry trend in East Asia region. During 1902-1990, a seasonal SPEI presented diverse characteristics, with clear wet trends in Japan, Mongolia, and North Korea in four different growing seasons, with dry trends in China and South Korea. During 1991-2018, seasonal SPEI presented clear dry trends in Japan, Mongolia, and North Korea in different growing seasons, while China and South Korea showed a wet trend during the spring, autumn, and winter seasons. This ecological and climatic mechanism provides a good basis for the assessment of vegetation and drought-change variations within East Asia. An understandings of long-term vegetation trends and the effects of rainfall and SPEI on droughts of varying severity is essential for water resource management and climate change adaptation. Based on the results, water resources will increase under global warming, which may alleviate the water scarcity issue in the East Asia region.

Distribution Patterns of Gymnosperm Species along Elevations on the Qinghai-Tibet Plateau: Effects of Climatic Seasonality, Energy-Water, and Physical Tolerance Variables.

Climate change is one of the most prominent factors influencing the spatial distribution of plants in China, including gymnosperms. Climatic factors influence gymnosperm distribution along elevational gradients on the Qinghai-Xizang (Tibet) Plateau (QTP), and understanding how species adapt to these factors is important for identifying the impacts of global climate change. For the first time, we examined the county-level distribution of gymnosperm species on QTP using data from field surveys, published works, monographs, and internet sources. We used simulated distribution data of gymnosperms (N = 79) along the elevational gradients to investigate the overall impact of environmental variables in explaining the richness pattern of gymnosperms. Eighteen environmental variables were classified into three key variable sets (climatic seasonality, energy-water, and physical tolerance). We employed principal component analysis and generalized linear models to assess the impact of climatic variables on the gymnosperm's richness pattern. Gymnosperm species are unevenly distributed across the plateau and decline gradually from the southeast to the northwest. The altitudinal gradients have a unimodal relationship with the richness of gymnosperms, with the maximum species richness at an elevation of 3200 m. The joint effects of physical tolerance and energy-water predictors have explained the highest diversity of gymnosperms at mid-elevation. Because the richness peak correlates significantly with the wettest month's precipitation and moisture index, this confirms the significance of moisture on gymnosperm distributions due to increased precipitation during the wet season. Furthermore, our results provide evidence that climatic seasonality factors are involved in the decline of gymnosperm richness at high elevations. A total of 37% of gymnosperm species on QTP are listed as vulnerable, nearly threatened, or endangered, with elevations ranging from 600 m to 5300 m. As a result, we conclude that gymnosperms are at high risk of extinction because of the current climate fluctuations caused by global climate change. Our research offers fundamental data for the study and protection of gymnosperm species along the steepest elevation gradients.

MaxEnt model strategies to studying current and future potential land suitability dynamics of wheat, soybean and rice cultivation under climatic change scenarios in East Asia.

Climate change and variability are projected to alter the geographic suitability of lands for crops cultivation. Accurately predicting changes in the potential current and future land suitability distribution dynamics of wheat (Triticum aestivum), soybean (Glycine max) and rice (Oryza sativa) crops due to climate change scenarios is critical to adapting and mitigating the impacts of bioclimatic changes, and plays a significant role in securing food security in East Asia region. This study compiled large datasets of wheat, soybean and rice occurrence locations from GBIF and 19 bioclimatic variables obtained from the WorldClim database that affect crops growth. We recognized potential future suitable distribution regions for crops under the one socioeconomic pathway, (SSP585) for 2021-2040 and 2041-2060, using the MaxEnt model. The accuracy of the MaxEnt was highly significant with mean AUC values ranging from 0.833 to 0.882 for all models evaluated. The jackknife test revealed that for wheat, Bio4 and Bio12 contributed 17.6% and 12.6%, for soybean Bio10 and Bio12 contributed 15.6% and 49.5%, while for rice Bio12 and Bio14 contributed 12.9% and 36.0% to the MaxEnt model. In addition, cultivation aptitude for wheat, soybean, and rice increased in southeast China, North Korea, South Korea, and Japan, while decreasing in Mongolia and northwest China. Climate change is expected to increase the high land suitability for wheat, soybean, and rice in East Asia. Simulation results indicate an average decrease of unsuitable areas of -98.5%, -41.2% and -36.3% for wheat, soybean and rice from 2060 than that of current land suitability. In contrast, the high land suitable for wheat, soybean and rice cultivation is projected to increase by 75.1%, 68.5% and 81.9% from 2060 as compared with current. The findings of this study are of utmost importance in the East Asia region as they present an opportunity for policy makers to develop appropriate adaptation and mitigation strategies required to sustain crops distribution under future climates. Although the risks of wheat, soybean and rice cultivation may be significantly higher in the future because of high temperatures, heat waves, and droughts caused by climate change.

[Electrophysiological appropriateness technique based on TCM meridian theory for postoperative pain after urethral reconstruction: A real-world study].

OBJECTIVE: To investigate the clinical efficacy of electrophysiological appropriateness technique (EAT) therapy based on the traditional Chinese medicine (TCM) meridian theory in managing postoperative pain after urethral reconstruction surgery. METHODS: Using the real-world study approach, we enrolled 61 male patients undergoing urethral reconstruction and divided them into a control group (n = 30) and an observation group (n = 31), the former receiving patient-controlled intravenous analgesia (PCIA), while the latter PCIA plus EAT at 4 pairs of acupoints (Hegu, Neiguan, Zusanli and Sanyinjiao bilaterally) and the Ashi point, with 100 mg tramadol hydrochloride given orally as remedial analgesia in both groups in case of postoperative Visual Analogue Scale (VAS) score ≥4. We compared the VAS scores at 4, 12, 24 and 48 hours postoperatively, the dose of cumulative fentanyl used at 48 hours, the number of cases needing remedial analgesia, the time to first flatus and the incidence of adverse reactions between the two groups of patients. RESULTS: The VAS scores were markedly lower in the observation than in the control group at 4, 12, 24 and 48 hours after surgery (P < 0.05), with statistically significant differences in time-dependent effect and interactive effect (P < 0.05). Significant reduction was observed in the doses of cumulative fentanyl (P < 0.05) and remedial tramadol analgesia (P < 0.05), time to first flatus (P < 0.05), and incidence of adverse reactions (P < 0.05) in the observation group in comparison with the controls. CONCLUSION: Electrophysiological therapy based on the TCM meridian theory can safely and effectively alleviate postoperative pain after urethral reconstruction, reduce opioid consumption, and decrease adverse events.

Artificial intelligence in theranostics of gastric cancer, a review.

Gastric cancer (GC) is one of the commonest cancers with high morbidity and mortality in the world. How to realize precise diagnosis and therapy of GC owns great clinical requirement. In recent years, artificial intelligence (AI) has been actively explored to apply to early diagnosis and treatment and prognosis of gastric carcinoma. Herein, we review recent advance of AI in early screening, diagnosis, therapy and prognosis of stomach carcinoma. Especially AI combined with breath screening early GC system improved 97.4 % of early GC diagnosis ratio, AI model on stomach cancer diagnosis system of saliva biomarkers obtained an overall accuracy of 97.18 %, specificity of 97.44 %, and sensitivity of 96.88 %. We also discuss concept, issues, approaches and challenges of AI applied in stomach cancer. This review provides a comprehensive view and roadmap for readers working in this field, with the aim of pushing application of AI in theranostics of stomach cancer to increase the early discovery ratio and curative ratio of GC patients.

Erratum to "Phillyrin Mitigates Apoptosis and Oxidative Stress in Hydrogen Peroxide-Treated RPE Cells through Activation of the Nrf2 Signaling Pathway".

[This corrects the article DOI: 10.1155/2020/2684672.].

Iridoid glycosides from Morinda officinalis induce lysosomal biogenesis and promote autophagic flux to attenuate oxidative stress.

Morinda officinalis is a traditional Chinese tonic herb, and have been used in the treatment of multiple diseases. Here, three iridoid glycosides isolated from M. officinalis were evaluated for their roles in the autophagy-lysosomal pathway. All three iridoid glycosides could induce TFEB/TFE3-mediated lysosomal biogenesis and trigger autophagy. Interestingly, they promoted the nuclear import of TFEB/TFE3 without affecting their nuclear export, suggesting their role in the maintenance of lysosomal homeostasis. The results from this study shed light on the identification of autophagy activators from M. officinalis and provide a basis for developing them in the treatment of oxidative stress-involved diseases.

U-shaped association between plasma C-peptide and sarcopenia: A cross-sectional study of elderly Chinese patients with diabetes mellitus.

Limited research exists regarding the relationship between fasting plasma C-peptide levels and sarcopenia. As a result, our study aimed to examine this association in elderly Chinese diabetic patients. This cross-sectional study included 288 elderly patients with diabetes mellitus from the Fourth People's Hospital in Guiyang who were enrolled prospectively between March 2020 and February 2023. The independent variable of interest was fasting plasma C-peptide, while the dependent variable was sarcopenia. Data on several covariates, including demographic factors, lifestyle habits, co-morbidities, anthropometric indicators, and laboratory indicators, were also collected. Of the 288 participants, 27.43% (79/288) had sarcopenia. After adjusting for potential confounding variables, we found a U-shaped association between fasting plasma C-peptide levels and sarcopenia, with inflection points identified at approximately 774 pmol/L and 939 mmol/L. Within the range of 50-744 pmol/L, each 100 pmol/L increase in CysC was associated with a 37% decrease in the odds of sarcopenia (odds ratio [OR], 0.63; 95% confidence interval [CI], 0.49 to 0.83; P < 0.001). Additionally, within the range of 939-1694 pmol/L, each 100 pmol/L increase in fasting plasma C-peptide was associated with a 76% increase in the odds of sarcopenia (odds ratio [OR], 1.76; 95% confidence interval [CI], 1.11 to 2.81; P = 0.017). Our study revealed a U-shaped association between fasting plasma C-peptide levels and the likelihood of sarcopenia, with lower risk in the range of 774-939 pmol/L. These findings may assist in the development of more effective prevention and treatment strategies for sarcopenia in elderly diabetic patients.

The diagnosis interval influences risk factors of mortality in patients with co-existent active tuberculosis and lung cancer: a retrospective study.

BACKGROUND: Previous studies reported that tuberculosis (TB) is associated with an increased risk of lung cancer or the survival and mortality of lung cancer. However, the impact of coexisting TB on the survival of lung cancer patients was controversial. We aimed to identify risk factors on the survival rate of patients with co-existent active TB and lung cancer. METHODS: One hundred seventy-three patients diagnosed with active TB and lung cancer from January 2016 to August 2021 in Shanghai pulmonary hospital were selected and divided into two groups (≤ 6 months, > 6 months) according to the diagnosis interval between active TB and lung cancer (the order of diagnosis is not considered). The clinical characteristics and survival were analyzed. Univariate and multivariate logistic regression analyses were used to identify the risk factors for overall survival (OS). RESULTS: One hundred seventy-three patients were diagnosed with lung cancer and active TB. The study population exhibited a median age of 64 years, with a majority of 81.5% being male, 58.0% of patients had a history of smoking. Among those involved, 93.6% had pulmonary TB, 91.9% were diagnosed with non-small cell lung cancer (NSCLC), 76.9% were Eastern Cooperative Oncology Group (ECOG) 0-2 and 12.7% were ECOG 3-4. We observed better survival in the > 6 months group compared with the ≤ 6 months group (hazard ratio [HR] 0.456, 95% confidence interval [CI]:0.234-0.889, P = 0.017). The 1-, 3-, and 5- year OS rates were 94.2%, 80.3%, and 77.6%, respectively, in the > 6 months group and 88.3%, 63.8%, and 58.5%, respectively, in the ≤ 6 months group. Surgery (HR 0.193, [95% CI, 0.038-0.097]; P = 0.046) and ECOG Performance Status (HR 12.866, [95% CI, 2.730-60.638]; P = 0.001) were independent prognostic factors in the > 6 months group. CONCLUSIONS: Patients diagnosed with lung cancer and active TB for more than half a year have a significantly better prognosis than those diagnosed within half a year. ECOG Performance Status and surgery might possibly affect the outcomes of patients with co-existent active TB and lung cancer.

Favorable impact of a novel endoscopic auxiliary system (NEAS) on fluoroscopy-guided lithotripsy for difficult bile duct stones: A pilot study.

OBJECTIVES: Laser lithotripsy under fluoroscopic guidance is difficult to perform and risky due to its invisibility. In this study we aimed to investigate the efficacy and safety of a novel endoscopic auxiliary system (NEAS)-assisted lithotripsy under fluoroscopy for treating difficult common bile duct (CBD) stones. METHODS: Patients with difficult CBD stones who were treated with NEAS-assisted laser lithotripsy (NEAS group) or conventional mechanical lithotripsy (ML) under fluoroscopy (ML group) were retrospectively evaluated. The primary outcome was the complete stone clearance rate and the secondary outcomes included operation time, complications, and medical cost. RESULTS: Seventeen patients were treated with NEAS-assisted laser lithotripsy and 144 patients underwent ML. Using the propensity score matching analysis, 17 pairs of cases treated with NEAS-assisted lithotripsy and ML were included. Patients in the NEAS group showed a higher stone clearance rate than the ML group (94.1% vs 58.8%, P = 0.039), as well as shorter operation time (41.9 min vs 49.4 min, P < 0.001) and lower medical cost (USD 4607 vs USD 5014, P < 0.001). There was no significant difference in the complication rate between the two groups (5.9% vs 17.6%, P = 0.601). CONCLUSION: NEAS-assisted fluoroscopy-guided laser lithotripsy is feasible and safe, which may be a promising technique in fluoroscopy-guided laser lithotripsy for difficult CBD stones.

scGCC: Graph Contrastive Clustering With Neighborhood Augmentations for scRNA-Seq Data Analysis.

Single-cell RNA sequencing (scRNA-seq) has rapidly emerged as a powerful technique for analyzing cellular heterogeneity at the individual cell level. In the analysis of scRNA-seq data, cell clustering is a critical step in downstream analysis, as it enables the identification of cell types and the discovery of novel cell subtypes. However, the characteristics of scRNA-seq data, such as high dimensionality and sparsity, dropout events and batch effects, present significant computational challenges for clustering analysis. In this study, we propose scGCC, a novel graph self-supervised contrastive learning model, to address the challenges faced in scRNA-seq data analysis. scGCC comprises two main components: a representation learning module and a clustering module. The scRNA-seq data is first fed into a representation learning module for training, which is then used for data classification through a clustering module. scGCC can learn low-dimensional denoised embeddings, which is advantageous for our clustering task. We introduce Graph Attention Networks (GAT) for cell representation learning, which enables better feature extraction and improved clustering accuracy. Additionally, we propose five data augmentation methods to improve clustering performance by increasing data diversity and reducing overfitting. These methods enhance the robustness of clustering results. Our experimental study on 14 real-world datasets has demonstrated that our model achieves extraordinary accuracy and robustness. We also perform downstream tasks, including batch effect removal, trajectory inference, and marker genes analysis, to verify the biological effectiveness of our model.

Zinc/carbon nanomaterials inhibit antibiotic resistance genes by affecting quorum sensing and microbial community in cattle manure production.

This study used metagenomic sequencing to examine the effects of carbon-based zinc oxide nanoparticles (CZnONPs) and graphene-based zinc oxide nanoparticles (GZnONPs) on quorum sensing (QS), antibiotic resistance genes (ARGs) and microbial community changes during cattle manure production. The manure zinc content was significantly reduced in GZnONPs group. In the QS pathway, the autoinducer gene increases significantly in Control group, while the transporter and repressor genes experience a substantial increase in CZnONPs group. These results contributed to the significantly decreased the abundance of ARGs in GZnONPs group. The co-occurrence network analysis revealed a correlation between core ARGs and QS-related KEGG Orthology or ARGs' hosts, indicating that the selective pressure of zinc influences microbial QS, forming a unique ARG pattern in in vivo anaerobic fermentation. These findings suggest that implementing nutritional regulation in farming practices can serve as a preventive measure to mitigate the potential transmission of ARGs resulting from livestock waste.

Leveraging the E-commerce footprint for the surveillance of healthcare utilization.

The utilization of healthcare services serves as a barometer for current and future health outcomes. Even in countries with modern healthcare IT infrastructure, however, fragmentation and interoperability issues hinder the (short-term) monitoring of utilization, forcing policymakers to rely on secondary data sources, such as surveys. This deficiency may be particularly problematic during public health crises, when ensuring proper and timely access to healthcare acquires special importance. We show that, in specific contexts, online pharmacies' digital footprint data may contain a strong signal of healthcare utilization. As such, online pharmacy data may enable utilization surveillance, i.e., the monitoring of short-term changes in utilization levels in the population. Our analysis takes advantage of the scenario created by the first wave of the Covid-19 pandemic in Mainland China, where the virus' spread lead to pervasive and deep reductions of healthcare service utilization. Relying on a large sample of online pharmacy transactions with full national coverage, we first detect variation that is strongly consistent with utilization reductions across geographies and over time. We then validate our claims by contrasting online pharmacy variation against credit-card transactions for medical services. Using machine learning methods, we show that incorporating online pharmacy data into the models significantly improves the accuracy of utilization surveillance estimates.

Optimization of O/W Emulsion Solvent Evaporation Method for Itraconazole Sustained Release Microspheres.

Itraconazole, a commonly used antifungal drug in the clinic approved by U.S. Food and Drug Administration (FDA), has been gradually found to have anti-tumor, angiogenesis inhibition and other pharmacological activities. However, its poor water solubility and potential toxicity limited its clinical application. In order to improve the water solubility and reduce the side effects caused by the high concentration of itraconazole, a novel preparation method of itraconazole sustained release microspheres was established in this study. Firstly, five kinds of polylactic acid-glycolic acid (PLGA) microspheres loaded with itraconazole were prepared by oil/water (O/W) emulsion solvent evaporation and then characterized by infrared spectroscopy. Then the particle size and morphology of the microspheres were observed by scanning electron microscope (SEM) and transmission electron microscope (TEM). After that, the particle size distribution, drug loading rate, entrapment efficiency, and drug release experiments were evaluated. Our results showed the microspheres prepared in this study had uniform particle size distribution and good integrity. Further study found that the average drug loading of the five kinds of microspheres prepared with PLGA 7505, PLGA 7510, PLGA 7520, PLGA 5020 and PLGA 0020 were 16.88, 17.72, 16.72, 16.57, and 16.64%, respectively, and the encapsulation rate all reached about 100%. More surprisingly, the release experimental results showed that the microspheres prepared with PLGA 7520 did not show sudden release, showing good sustained release performance and high drug release rate. To sum up, this study optimized the preparation method of sustained-release microspheres without sudden release, which provides a new solution for the delivery of itraconazole in the clinic.