Serum iron level is independently associated with sarcopenia: a retrospective study.

Sarcopenia greatly reduces the quality of life of the elderly, and iron metabolism plays an important role in muscle loss. This study aimed to investigate the association between iron status and sarcopenia. A total of 286 adult patients hospitalized between 2019 and 2021 were included in this study, of which 117 were diagnosed with sarcopenia. Serum iron, total iron binding capacity (TIBC), transferrin, and transferrin saturation levels were compared between groups with and without sarcopenia and were included in the logistic analyses, with significant variables further included in the logistic regression model for the prediction of sarcopenia. Serum iron, TIBC, and transferrin levels decreased significantly in the sarcopenia group (p < 0.05), and were negatively associated with handgrip strength, relative skeletal muscle index, and multiple test performances (p < 0.05). Multivariate logistic analysis showed that sex, age, body mass index (BMI), and serum iron level were independent risk factors for sarcopenia. In the final logistic regression model, male sex (odds ratio [OR] 3.65, 95% confidence interval [CI] 1.67-7.98), age > 65 years (OR 5.40, 95% CI 2.25-12.95), BMI < 24 kg/m2 (OR 0.17, 95% CI 0.08-0.36), and serum iron < 10.95 µmol/L (OR 0.39, 95% CI 0.16-0.93) were included. Our study supported the impact of iron metabolism on muscle strength and performance.

The Prognosis Performance of a Neutrophil- and Lymphocyte-Associated Gene Mutation Score in a Head and Neck Cancer Cohort.

The treatment of head and neck squamous cell carcinomas (HNSCCs) is multimodal, and chemoradiotherapy (CRT) is a critical component. However, the availability of predictive or prognostic markers in patients with HNSCC is limited. Inflammation is a well-documented factor in cancer, and several parameters have been studied, with the neutrophil-to-lymphocyte ratio (NLR) being the most promising. The NLR is the most extensively researched clinical biomarker in various solid tumors, including HNSCC. In our study, we collected clinical and next-generation sequencing (NGS) data with targeted sequencing information from 107 patients with HNSCC who underwent CRT. The difference in the NLR between the good response group and the poor response group was significant, with more patients having a high NLR in the poor response group. We also examined the genetic alterations linked to the NLR and found a total of 41 associated genes across eight common pathways searched from the KEGG database. The overall mutation rate was low, and there was no significant mutation difference between the low- and high-NLR groups. Using a multivariate binomial generalized linear model, we identified three candidate genes (MAP2K2, MAP2K4, and ABL1) that showed significant results and were used to create a gene mutation score (GMS). Using the NLR-GMS category, we noticed that the high-NLR-GMS group had significantly shorter relapse-free survival compared to the intermediate- or low-NLR-GMS groups.

High G9a Expression in DLBCL and Its Inhibition by Niclosamide to Induce Autophagy as a Therapeutic Approach.

BACKGROUND: Diffuse large B-cell lymphoma (DLBCL) is a malignant lymphoid tumor disease that is characterized by heterogeneity, but current treatment does not benefit all patients, which highlights the need to identify oncogenic genes and appropriate drugs. G9a is a histone methyltransferase that catalyzes histone H3 lysine 9 (H3K9) methylation to regulate gene function and expression in various cancers. METHODS: TCGA and GTEx data were analyzed using the GEPIA2 platform. Cell viability under drug treatment was assessed using Alamar Blue reagent; the interaction between G9a and niclosamide was assessed using molecular docking analysis; mRNA and protein expression were quantified in DLBCL cell lines. Finally, G9a expression was quantified in 39 DLBCL patient samples. RESULTS: The TCGA database analysis revealed higher G9a mRNA expression in DLBCL compared to normal tissues. Niclosamide inhibited DLBCL cell line proliferation in a time- and dose-dependent manner, reducing G9a expression and increasing p62, BECN1, and LC3 gene expression by autophagy pathway regulation. There was a correlation between G9a expression in DLBCL samples and clinical data, showing that advanced cancer stages exhibited a higher proportion of G9a-expressing cells. CONCLUSION: G9a overexpression is associated with tumor progression in DLBCL. Niclosamide effectively inhibits DLBCL growth by reducing G9a expression via the cellular autophagy pathway; therefore, G9a is a potential molecular target for the development of therapeutic strategies for DLBCL.

Ontology-driven and weakly supervised rare disease identification from clinical notes.

BACKGROUND: Computational text phenotyping is the practice of identifying patients with certain disorders and traits from clinical notes. Rare diseases are challenging to be identified due to few cases available for machine learning and the need for data annotation from domain experts. METHODS: We propose a method using ontologies and weak supervision, with recent pre-trained contextual representations from Bi-directional Transformers (e.g. BERT). The ontology-driven framework includes two steps: (i) Text-to-UMLS, extracting phenotypes by contextually linking mentions to concepts in Unified Medical Language System (UMLS), with a Named Entity Recognition and Linking (NER+L) tool, SemEHR, and weak supervision with customised rules and contextual mention representation; (ii) UMLS-to-ORDO, matching UMLS concepts to rare diseases in Orphanet Rare Disease Ontology (ORDO). The weakly supervised approach is proposed to learn a phenotype confirmation model to improve Text-to-UMLS linking, without annotated data from domain experts. We evaluated the approach on three clinical datasets, MIMIC-III discharge summaries, MIMIC-III radiology reports, and NHS Tayside brain imaging reports from two institutions in the US and the UK, with annotations. RESULTS: The improvements in the precision were pronounced (by over 30% to 50% absolute score for Text-to-UMLS linking), with almost no loss of recall compared to the existing NER+L tool, SemEHR. Results on radiology reports from MIMIC-III and NHS Tayside were consistent with the discharge summaries. The overall pipeline processing clinical notes can extract rare disease cases, mostly uncaptured in structured data (manually assigned ICD codes). CONCLUSION: The study provides empirical evidence for the task by applying a weakly supervised NLP pipeline on clinical notes. The proposed weak supervised deep learning approach requires no human annotation except for validation and testing, by leveraging ontologies, NER+L tools, and contextual representations. The study also demonstrates that Natural Language Processing (NLP) can complement traditional ICD-based approaches to better estimate rare diseases in clinical notes. We discuss the usefulness and limitations of the weak supervision approach and propose directions for future studies.

Information dissemination during public health emergencies: analysing the international flow of COVID-19-related news.

A large-scale exchange of information between media across national borders is frequently observed when a worldwide public health emergency occurs. This study investigated the global news citation network in the early stage of the COVID-19 pandemic by analysing the network structure at different levels to identify important nodes and the relationships among news organisations. The results show that COVID-19-related international news flow had a complex and unequal pattern, with a few countries and media outlets occupying a prominent place in the network and three media groups played key but different roles in disseminating the news. It was jointly influenced by national traits, the relatedness between countries, and the pandemic emergency with public health risks. From a global perspective, the media of the United States, mainland China, and the United Kingdom played the most important parts in collaboration within the world media system in the early stage of the COVID-19 pandemic.

Ad5-nCoV booster and Omicron variant breakthrough infection following two doses of inactivated vaccine elicit comparable antibody levels against Omicron variants.

Little information is available for antibody levels against SARS-CoV-2 variants of concern induced by Omicron breakthrough infection and a third booster with an inactivated vaccine (InV) or Ad5-nCoV in people with completion of two InV doses. Plasma was collected from InV pre-vaccinated Omicron-infected patients (OIPs), unvaccinated OIPs between 0 and 22 days, and healthy donors (HDs) 14 days or 6 months after the second doses of an InV and 14 days after a homogenous booster or heterologous booster of Ad5-nCoV. Anti-Wuhan-, Anti-Delta-, and Anti-Omicron-receptor binding domain (RBD)-IgG titers were detected using enzyme-linked immunosorbent assay. InV pre-vaccinated OIPs had higher anti-Wuhan-, anti-Delta-, and anti-Omicron-RBD-IgG titers compared to unvaccinated OIPs. Anti-Wuhan-RBD-IgG titers sharply increased in InV pre-vaccinated OIPs 0-5 days postinfection (DPI), while the geometric mean titers (GMTs) of anti-Delta- and anti-Omicron-RBD-IgG were 3.3-fold and 12.0-fold lower. Then, the GMT of anti-Delta- and anti-Omicron-RBD-IgG increased to 35 112 and 28 186 during 11-22 DPI, about 2.6-fold and 3.2-fold lower, respectively, than the anti-Wuhan-RBD-IgG titer. The anti-Wuhan-, anti-Delta-, and anti-Omicron-RBD-IgG titers declined over time in HDs after two doses of an InV, with 25.2-fold, 5.6-fold, and 4.5-fold declination, respectively, at 6 months relative to the titers at 14 days after the second vaccination. Anti-Wuhan-, anti-Delta-, and anti-Omicron-RBD-IgG titers elicited by a heterologous Ad5-nCoV booster were significantly higher than those elicited by an InV booster, comparable to those in InV pre-vaccinated OIPs. InV and Ad5-nCoV boosters could improve humoral immunity against Omicron variants. Of these, the Ad5-nCoV booster is a better alternative.

A survey on clinical natural language processing in the United Kingdom from 2007 to 2022.

Much of the knowledge and information needed for enabling high-quality clinical research is stored in free-text format. Natural language processing (NLP) has been used to extract information from these sources at scale for several decades. This paper aims to present a comprehensive review of clinical NLP for the past 15 years in the UK to identify the community, depict its evolution, analyse methodologies and applications, and identify the main barriers. We collect a dataset of clinical NLP projects (n = 94; £ = 41.97 m) funded by UK funders or the European Union's funding programmes. Additionally, we extract details on 9 funders, 137 organisations, 139 persons and 431 research papers. Networks are created from timestamped data interlinking all entities, and network analysis is subsequently applied to generate insights. 431 publications are identified as part of a literature review, of which 107 are eligible for final analysis. Results show, not surprisingly, clinical NLP in the UK has increased substantially in the last 15 years: the total budget in the period of 2019-2022 was 80 times that of 2007-2010. However, the effort is required to deepen areas such as disease (sub-)phenotyping and broaden application domains. There is also a need to improve links between academia and industry and enable deployments in real-world settings for the realisation of clinical NLP's great potential in care delivery. The major barriers include research and development access to hospital data, lack of capable computational resources in the right places, the scarcity of labelled data and barriers to sharing of pretrained models.

Exposure to Nature Sounds through a Mobile Application in Daily Life: Effects on Learning Performance among University Students.

Previous studies have revealed the restorative effects of exposure to natural environments on psychological well-being and cognitive performance. Recent studies have reported the effects of exposure to nature sounds (e.g., the sounds of birds, rainfall, and waves) through a mobile application on reducing students' mental fatigue and improving their cognitive performance. However, it remains unknown whether exposure to nature sounds through a mobile application may influence students' learning performance. To address the gap, we conducted a study with 71 university students. During the four-week intervention, 36 students in the experimental group were exposed to nature sounds through a free mobile application for at least 30 consecutive minutes per day when working on academic-related tasks; 35 students in the control group did not have such exposure when working on similar tasks. The results show that students in the experimental group outperformed those in the control group in their engagement in deep learning, frequency of academic procrastination, and academic self-efficacy. The findings reveal the promising effects of exposure to nature sounds through a mobile application on improving students' learning performance. The implications of the findings are discussed.

Prediction and Analysis of Dynamic Changes of College Students' Ideological and Political Changes Based on Multiple Regression.

The construction of a harmonious society requires college students to coordinate their ideological, political, and moral qualities with social development and the needs of the times. Through the investigation and analysis of the ideological, political, and moral qualities of college students, on the one hand, we can see that the ideological, political, and moral qualities of college students are generally positive and healthy. On the other hand, it also exposes the outstanding problems in the ideological and political aspects of college students and the shortcomings of the ideological and political work in colleges and universities. This paper analyzes the dynamic changes of college students' ideological and political changes and further studies the relationship between various indicators and students' ideological and moral qualities through multiple linear regression analysis.

Risk Communication in the Alert Phase of the COVID-19 Pandemic: Analysis of News Flow at National and Global Levels.

This study examined the global media citation network of COVID-19-related news at two stages of the pandemic alert phase, i.e., the national level alert stage and the global level alert stage. The findings reveal that the small-world pattern and scale-free property of media citation networks contributed to the rapid spread of COVID-19-related news around the world. Within the networks, a small number of media outlets from a few countries formed the backbone of the network to control the risk communication; meanwhile, many media of geographical and cultural similarities formed cross-border collaborative cliques in the periphery of the network. When the alert phase escalated from the national level to the global level, the network demonstrated a number of changes. The findings contribute to the understanding of risk communication for international public health emergencies by taking into account the network perspective and evolutionary nature of public health emergencies in analysis.

Emodin Ameliorates the Efficacy of Carfilzomib in Multiple Myeloma Cells via Apoptosis and Autophagy.

BACKGROUND: Carfilzomib, the proteasome inhibitor, can increase the overall survival rate of multiple myeloma (MM) patients undergoing targeted therapy. However, relapse and toxicity present great challenges for such treatment, so an urgent need for effective combination therapy is necessary. Emodin is a natural chemical compound that inhibits the proliferation of various cancers and can effectively combine with other treatments. In this study, we evaluated the sensitizing effect of emodin combined with carfilzomib on MM cells. METHODS: The cells were treated with emodin, carfilzomib, and a combination of drugs to determine their effects on cell proliferation and viability. The cell cycle distribution and reactive oxygen species (ROS) expression were measured by flow cytometry. The level of RNA and protein were analyzed through real-time qPCR and immunoblotting. RESULTS: Emodin acted synergistically with carfilzomib to reduce the proliferation and viability of MM cell lines in vitro. Furthermore, the combination of emodin and carfilzomib increased ROS production, inducing apoptosis and autophagy pathways via caspase-3, PARP, p62, and LC3B. CONCLUSIONS: These results provide a molecular target for combination therapy in MM patients.

Continue or not to continue? Attitudes towards deprescribing among community-dwelling older adults in China.

BACKGROUND: Inappropriate prescribing of medications and polypharmacy among older adults are associated with a wide range of adverse outcomes. It is critical to understand the attitudes towards deprescribing-reducing the use of potentially inappropriate medications (PIMs)-among this vulnerable group. Such information is particularly lacking in low - and middle-income countries. METHODS: In this study, we examined Chinese community-dwelling older adults' attitudes to deprescribing as well as individual-level correlates. Through the community-based health examination platform, we performed a cross-sectional study by personally interviews using the revised Patients' Attitudes Towards Deprescribing (rPATD) questionnaire (version for older adults) in two communities located in Suzhou, China. We recruited participants who were at least 65 years and had at least one chronic condition and one prescribed medication. RESULTS: We included 1,897 participants in the present study; the mean age was 73.8 years (SD = 6.2 years) and 1,023 (53.9%) were women. Most of older adults had one chronic disease (n = 1,364 [71.9%]) and took 1-2 regular drugs (n = 1,483 [78.2%]). Half of the participants (n = 947, 50%) indicated that they would be willing to stop taking one or more of their medicines if their doctor said it was possible, and 924 (48.7%) older adults wanted to cut down on the number of medications they were taking. We did not find individual level characteristics to be correlated to attitudes to deprescribing. CONCLUSIONS: The proportions of participants' willingness to deprescribing were much lower than what prior investigations among western populations reported. It is important to identify the factors that influence deprescribing and develop a patient-centered and practical deprescribing guideline that is suitable for Chinese older adults.

Impact of moderate-to-severe obstructive sleep apnea on aggressive clinicopathological features of papillary thyroid carcinoma.

OBJECTIVE: Obstructive sleep apnea (OSA) may to be strongly associated with cancer mortality. The risk hazards of OSA regarding aggressive features of papillary thyroid carcinoma (PTC) remain unclear. The main objective of this study was to explore the relationship between OSA and aggressive features of PTC. METHODS: We prospectively studied 210 patients (54 men, 156 women; age 43 ± 13 years) with PTC. Indices of sleep respiratory disturbance and oxygen desaturation were determined by polysomnography with the apnea-hypopnea index (AHI) and lowest oxygen saturation (LSaO2), respectively. PTC aggressive features were assessed by postoperative histopathological analysis. Multivariant logistic regression models adjusting for demographic and OSA-related factors were generated to determine OSA risk hazards for aggressive PTC features. RESULTS: The prevalence of moderate-to-severe OSA (defined as AHI of >15) was 20% in PTC patients. Those in the moderate-to-severe OSA group had higher BMI and more aggressive PTC features. Moderate-to-severe OSA was associated with increased odds of larger tumor size (OR, 4.31; 95% CI, 1.79-10.37; p = 0.001), capsular invasion (OR, 2.96; 95% CI, 1.42-6.16; p = 0.004), multifocality (OR, 3.11; 95% CI, 1.52-6.39; p = 0.002), central (OR, 4.7; 95% CI, 1.77-12.49; p = 0.003) and lateral (OR, 5.94; 95% CI, 2.27-15.54; p < 0.001) cervical lymph node metastasis, and BRAF mutation (OR, 2.88; 95% CI, 1.31-6.31; p = 0.008). Moderate to severe hypoxemia did not correlated with aggressive PTC behaviors. CONCLUSIONS: OSA is a common respiratory disturbance in PTC. Aggressive PTC features in patients with moderate-to-severe OSA implicate OSA as a cause of cancer progression. Respiratory disturbance events have a greater impact on PTC aggressiveness than hypoxia.

The Effects of Using a Nature-Sound Mobile Application on Psychological Well-Being and Cognitive Performance Among University Students.

Many university students have been struggling with multiple challenges that may cause mental fatigue. Exposure to the natural environment is found to have restorative effects on mental fatigue, which can be explained by its benefits in physiological, psychological, and cognitive aspects. While the natural environment contains both visual and auditory elements, research on the effects of auditory elements, such as nature sounds, is underdeveloped and limited to laboratory settings. It remains unclear what are the effects of exposure to nature sounds in daily life settings. The study was conducted with 71 students from a university, who were randomly assigned to the experimental group using a nature-sound mobile application in daily life and the control group not using the application. After a 4-week exposure to the intervention, the students in the experimental group outperformed their counterparts in the control group on psychological well-being reflected in positive affect, as well as cognitive performance reflected in flow state, attention (in terms of alerting) and working memory (in terms of accuracy and reaction time). The findings reveal the positive impact of exposure to relaxing nature sounds on university students' psychological well-being and cognitive performance, as well as the potential of mobile applications to provide easy exposure to nature sounds.

Preoperative CT-Based Radiomics Combined With Nodule Type to Predict the Micropapillary Pattern in Lung Adenocarcinoma of Size 2 cm or Less: A Multicenter Study.

PURPOSE: To construct an optimal radiomics model for preoperative prediction micropapillary pattern (MPP) in adenocarcinoma (ADC) of size ≤ 2 cm, nodule type was used for stratification to construct two radiomics models based on high-resolution computed tomography (HRCT) images. MATERIALS AND METHODS: We retrospectively analyzed patients with pathologically confirmed ADC of size ≤ 2 cm who presented to three hospitals. Patients presenting to the hospital with the greater number of patients were included in the training set (n = 2386) and those presenting to the other two hospitals were included in the external validation set (n = 119). HRCT images were used for delineation of region of interest of tumor and extraction of radiomics features; dimensionality reduction was performed for the features. Nodule type was used to stratify the data and the random forest method was used to construct two models for preoperative prediction MPP in ADC of size ≤ 2 cm. Model 1 included all nodule types and model 2 included only solid nodules. The receiver operating characteristic curve was used to assess the prediction performance of the two models and independent validation was used to assess its generalizability. RESULTS: Both models predicted ADC with MPP preoperatively. The area under the curve (AUC) of prediction performance of models 1 and 2 were 0.91 and 0.78, respectively. The prediction performance of model 2 was lower than that of model 1. The AUCs in the external validation set were 0.81 and 0.72, respectively. The DeLong test showed statistically significant differences between the training and validation sets in model 1 (p = 0.0296) with weak generalizability. There was no statistically significant difference between the training and validation sets in model 2 (p = 0.2865) with some generalizability. CONCLUSION: Nodule type is an important factor that affects the performance of radiomics predictor model for MPP with ADC of size ≤ 2 cm. The radiomics prediction model constructed based on solid nodules alone, can be used to evaluate MPP and may contribute to proper surgical planning in patients with ADC of size ≤ 2 cm.

Rare Disease Identification from Clinical Notes with Ontologies and Weak Supervision.

The identification of rare diseases from clinical notes with Natural Language Processing (NLP) is challenging due to the few cases available for machine learning and the need of data annotation from clinical experts. We propose a method using ontologies and weak supervision. The approach includes two steps: (i) Text-to-UMLS, linking text mentions to concepts in Unified Medical Language System (UMLS), with a named entity linking tool (e.g. SemEHR) and weak supervision based on customised rules and Bidirectional Encoder Representations from Transformers (BERT) based contextual representations, and (ii) UMLS-to-ORDO, matching UMLS concepts to rare diseases in Orphanet Rare Disease Ontology (ORDO). Using MIMIC-III US intensive care discharge summaries as a case study, we show that the Text-to-UMLS process can be greatly improved with weak supervision, without any annotated data from domain experts. Our analysis shows that the overall pipeline processing discharge summaries can surface rare disease cases, which are mostly uncaptured in manual ICD codes of the hospital admissions.

CT Radiomics for the Preoperative Prediction of Ki67 Index in Gastrointestinal Stromal Tumors: A Multi-Center Study.

PURPOSE: This study established and verified a radiomics model for the preoperative prediction of the Ki67 index of gastrointestinal stromal tumors (GISTs). MATERIALS AND METHODS: A total of 344 patients with GISTs from three hospitals were divided into a training set and an external validation set. The tumor region of interest was delineated based on enhanced computed-tomography (CT) images to extract radiomic features. The Boruta algorithm was used for dimensionality reduction of the features, and the random forest algorithm was used to construct the model for radiomics prediction of the Ki67 index. The receiver operating characteristic (ROC) curve was used to evaluate the model's performance and generalization ability. RESULTS: After dimensionality reduction, a feature subset having 21 radiomics features was generated. The generated radiomics model had an the area under curve (AUC) value of 0.835 (95% confidence interval(CI): 0.761-0.908) in the training set and 0.784 (95% CI: 0.691-0.874) in the external validation cohort. CONCLUSION: The radiomics model of this study had the potential to predict the Ki67 index of GISTs preoperatively.

Computed Tomography-Based Radiomics Model to Preoperatively Predict Microsatellite Instability Status in Colorectal Cancer: A Multicenter Study.

OBJECTIVES: To establish and validate a combined radiomics model based on radiomics features and clinical characteristics, and to predict microsatellite instability (MSI) status in colorectal cancer (CRC) patients preoperatively. METHODS: A total of 368 patients from four hospitals, who underwent preoperative contrast-enhanced CT examination, were included in this study. The data of 226 patients from a single hospital were used as the training dataset. The data of 142 patients from the other three hospitals were used as an independent validation dataset. The regions of interest were drawn on the portal venous phase of contrast-enhanced CT images. The filtered radiomics features and clinical characteristics were combined. A total of 15 different discrimination models were constructed based on a feature selection strategy from a pool of 3 feature selection methods and a classifier from a pool of 5 classification algorithms. The generalization capability of each model was evaluated in an external validation set. The model with high area under the curve (AUC) value from the training set and without a significant decrease in the external validation set was final selected. The Brier score (BS) was used to quantify overall performance of the selected model. RESULTS: The logistic regression model using the mutual information (MI) dimensionality reduction method was final selected with an AUC value of 0.79 for the training set and 0.73 for the external validation set to predicting MSI. The BS value of the model was 0.12 in the training set and 0.19 in the validation set. CONCLUSION: The established combined radiomics model has the potential to predict MSI status in CRC patients preoperatively.