Hearing loss prevalence and burden of disease in China: Findings from provincial-level analysis.

BACKGROUND: Without timely and effective rehabilitation, hearing loss may profoundly affect human life quality. China has a large population of hearing-impaired individuals, which imposes a heavy health burden on society. Moreover, this population is projected to increase rapidly owing to China's aging society. METHODS: We used data from a population-representative epidemiological investigation of hearing loss and ear diseases in four Chinese provinces. We estimated the national prevalence using multiple linear regression of the age-group proportions and prevalence in 31 provinces with clustering analysis. We used years lived with disability (YLDs) to analyze the disease burden and forecasted the prevalence of hearing loss by 2060 in China. RESULTS: An estimated 115 million people had moderate-to-complete hearing loss in 2015 across the 31 provinces of China (8.4% of 1.37 billion people). Of these, 85.7% were older than age 50 years (99 million people) and 2.4% were younger than 20 years old (2.8 million people). Of all YLDs attributable to hearing loss, 68.9% were attributable to moderate-to-complete cases. By 2060, a projected 242 million people in China will have moderate-to-complete hearing loss, a 110.0% increase from 2015. CONCLUSIONS: The hearing loss prevalence in China is high. Population aging and socioeconomic factors substantially affect the prevalence and severity of hearing loss and the disease burden. The prevalence and severity of hearing loss are unevenly distributed across different provinces. Future public health policies should take these trends and regional variations into account.

Social-economic transitions and vulnerability to extreme temperature events from 1960 to 2020 in Chinese cities.

Climate change leads to more frequent and intense extreme temperature events, causing a significant number of excess deaths. Using an epidemiological approach, we analyze all-cause deaths related to heatwaves and cold spells in 2,852 Chinese counties from 1960 to 2020. Economic losses associated with these events are determined through the value of statistical life. Findings reveal that cold-related cumulative excess deaths (1,133 thousand) are approximately 2.5 times higher than heat-related deaths, despite an increase in heat-related fatalities in recent decades. Monetized mortality due to heat-related events is estimated at 1,284 billion CNY, while cold-related economic loss is 1,510 billion CNY. Notably, cities located in colder regions experience more heat-related excess deaths, and vice versa. Economic development does not significantly reduce mortality risks to heatwaves across China. This study provides insights into the spatial-temporal heterogeneity of heatwaves and cold spells mortality, essential for policymakers ensuring long-term climate adaptation and sustainability.

Regulating Cholesterol in Tumorigenesis: A Novel Paradigm for Tumor Nanotherapeutics.

During the past decade, "membrane lipid therapy", which involves the regulation of the structure and function of tumor cell plasma membranes, has emerged as a new strategy for cancer treatment. Cholesterol is an important component of the tumor plasma membrane and serves an essential role in tumor initiation and progression. This review elucidates the role of cholesterol in tumorigenesis (including tumor cell proliferation, invasion/metastasis, drug resistance, and immunosuppressive microenvironment) and elaborates on the potential therapeutic targets for tumor treatment by regulating cholesterol. More meaningfully, this review provides an overview of cholesterol-integrated membrane lipid nanotherapeutics for cancer therapy through cholesterol regulation. These strategies include cholesterol biosynthesis interference, cholesterol uptake disruption, cholesterol metabolism regulation, cholesterol depletion, and cholesterol-based combination treatments. In summary, this review demonstrates the tumor nanotherapeutics based on cholesterol regulation, which will provide a reference for the further development of "membrane lipid therapy" for tumors.

Circ-Plod2 destabilizes Mpo mRNA by binding to IGF2BP2 to promote osteogenic differentiation of bone marrow mesenchymal stem cells.

Osteogenic differentiation, proliferation, and/or apoptosis of bone marrow mesenchymal stem cells (BMSCs) are involved in the progression of postmenopausal osteoporosis (PMO). However, circular RNA (circRNA)-mediated changes in the cellular function of BMSCs in PMO are still unclear. This study revealed the excellent ability of circ-Plod2 to promote osteogenic differentiation of BMSCs and its molecular mechanisms. In this study, ovariectomized (OVX) rats and control (Sham) rats were used to simulate PMO. Initially, we found that the expression of circ-Plod2 in OVX BMSCs is down-regulated and the expression of the Mpo gene is up-regulated by sequencing and verification. Further, we confirmed that circ-Plod2 is located in the cytoplasm and belongs to exon-type circRNA. Interestingly, circ-Plod2 promotes Mpo-dependent osteogenic differentiation of BMSCs without affecting proliferation, apoptosis, adipogenic differentiation, or chondrogenic differentiation of BMSCs. Mechanistically, we demonstrated that circ-Plod2 specifically binds IGF2BP2 to form an RNA-protein complex that destabilizes Mpo mRNA. Overexpression of circ-Plod2 in the bone marrow cavity effectively alleviated osteoporosis in OVX rats and inhibited the expression of MPO in BMSCs. Together, this study reveals that circ-Plod2 destabilizes Mpo mRNA by binding to IGF2BP2 to promote osteogenic differentiation of BMSCs to alleviate osteoporosis. The findings of this study may provide biomarkers for the diagnosis of PMO, and may also provide potential strategies for the clinical treatment of PMO.

PSTPIP2 regulates synovial macrophages polarization and dynamics via ERß in the joint microenvironment.

BACKGROUND: The cytoskeletal protein, PSTPIP2, is associated with inflammation and is predominantly expressed in macrophages. Previous data have shown that PSTPIP2 inhibits articular bone damage in arthritic rats. The aim of this study is to explore the molecular mechanism of PSTPIP2's resistance to bone erosion. METHODS: In the current study, peripheral blood and surgically excised synovial tissue from RA patients, DBA/1 mice, Pstpip2CreR26-ZsGreen reporter mice, and Esr2fl/fl/Adgre-Cre tool mice were used for in vivo studies. Adeno-associated viral vector was used to overexpress PSPTIP2 protein in vivo. RESULTS: We found that The level of PSTPIP2 in synovial macrophages is negatively correlated with RA disease activity, which is mediated by synovial macrophages polarization. PSTPIP2hi synovial macrophages form a tight immunological barrier in the lining layer. Notably, the ability of PSTPIP2 to regulate synovial macrophages polarization is dependent on ERß. Additionally, PSTPIP2 regulates the dynamics of synovial macrophages via ERß. CONCLUSIONS: Together, this study reveals that PSTPIP2 regulates synovial macrophages polarization and dynamics via ERß to form an immunological barrier (F4/80+PSTPIP2hi cell-enriched zone) for the joints. Thus, local modulation of PSTPIP2 expression in the joint microenvironment may be a potential strategy for controlling bone erosion in rheumatoid arthritis. PSTPIP2 regulates synovial macrophages polarization and dynamics via ERß to form F4/80+PSTPIP2hi cellular barrier in joint microenvironment.

Construction of Degradable and Amphiphilic Triblock Polymer Carriers for Effective Delivery of siRNA.

The development of effective and safe delivery carriers is one of the prerequisites for the clinical translation of siRNA-based therapeutics. In this study, a library of 144 functional triblock polymers using ring-opening polymerization (ROP) and thiol-ene click reaction is constructed. These triblock polymers are composed of hydrophilic poly (ethylene oxide) (PEO), hydrophobic poly (ε-caprolactone) (PCL), and cationic amine blocks. Three effective carriers are discovered by high-throughput screening of these polymers for siRNA delivery to HeLa-Luc cells. In vitro evaluation shows that siLuc-loaded nanoparticles (NPs) fabricated with leading polymer carriers exhibit sufficient knockdown of luciferase genes and relatively low cytotoxicity. The chemical structure of polymers significantly affects the physicochemical properties of the resulting siRNA-loaded NPs, which leads to different cellular uptake of NPs and endosomal escape of loaded siRNA and thus the overall in vitro siRNA delivery efficacy. After systemic administration to mice with xenograft tumors, siRNA NPs based on P2-4.5A8 are substantially accumulated at tumor sites, suggesting that PEO and PCL blocks are beneficial for improving blood circulation and biodistribution of siRNA NPs. This functional triblock polymer platform may have great potential in the development of siRNA-based therapies for the treatment of cancers.

Cell membrane-camouflaged PLGA biomimetic system for diverse biomedical application.

The emerging cell membrane (CM)-camouflaged poly(lactide-co-glycolide) (PLGA) nanoparticles (NPs) (CM@PLGA NPs) have witnessed tremendous developments since coming to the limelight. Donning a novel membrane coat on traditional PLGA carriers enables combining the strengths of PLGA with cell-like behavior, including inherently interacting with the surrounding environment. Thereby, the in vivo defects of PLGA (such as drug leakage and poor specific distribution) can be overcome, its therapeutic potential can be amplified, and additional novel functions beyond drug delivery can be conferred. To elucidate the development and promote the clinical transformation of CM@PLGA NPs, the commonly used anucleate and eukaryotic CMs have been described first. Then, CM engineering strategies, such as genetic and nongenetic engineering methods and hybrid membrane technology, have been discussed. The reviewed CM engineering technologies are expected to enrich the functions of CM@PLGA for diverse therapeutic purposes. Third, this article highlights the therapeutic and diagnostic applications and action mechanisms of PLGA biomimetic systems for cancer, cardiovascular diseases, virus infection, and eye diseases. Finally, future expectations and challenges are spotlighted in the concept of translational medicine.

Hyaluronic Acid-Modified Nanoplatforms as a Vector for Targeted Delivery of Autophagy-Related Gene to the Endometriotic Lesions in Mice.

This investigation probed endometriosis treatment using targeted nanoparticles (NPs) to modulate autophagic activity. To that end, a novel form of polymer-based NP gene delivery platform consisting of polyethyleneimine (PEI) conjugated to stearic acid (SA) and nucleotides (DNA/siRNAs) and enclosed by hyaluronic acid (HA) was prepared. CD44 is highly upregulated in cystic lesions, and HA-CD44 binding in this specific nanoplatform was used to achieve targeted drug delivery to CD44-expression endometriotic tissues. The expression of autophagy-related genes was modulated to explore the importance of this process in the development of endometriosis. By inducing autophagic activity, we were able to reduce the size of endometriotic cysts and suppress the development of ectopic endometrium. To further confirm the relationship between autophagic activity and this disease in humans and animals, numbers of autophagic vesicles and autophagic protein expression were assessed in lesion tissue samples from patients, revealing there may be consistency between animal and human data. Overall, these data revealed the ability of this (PEI-SA/DNA) HA gene delivery system to regulate autophagic activity and, thereby, aid in the treatment of endometriosis.

Short-chain fatty acids regulate B cells differentiation via the FFA2 receptor to alleviate rheumatoid arthritis.

BACKGROUND AND PURPOSE: Short-chain fatty acids (SCFAs) are metabolites from gut microbes involved in the host's inflammatory response and immunity. The aim of this study was to investigate the role of SCFAs in rheumatoid arthritis (RA) and possible mechanisms. EXPERIMENTAL APPROACH: Gut microbiota diversity in mice was analysed by 16S rDNA sequencing. SCFAs levels were analysed by gas chromatography mass spectrometry. T and B cells were analysed by flow cytometry. Bone damage was analysed by micro-CT and X-ray. Histopathological status was analysed by HE staining. Proteins in tissues were analysed by immunohistochemistry and PCR. Mice with CD19+ B cells deficient in FFA2 receptors were used to explore the molecular mechanisms involved. KEY RESULTS: Levels of acetate, propionate, butyrate, and valerate were decreased in RA patients, and the first three correlated positively with the frequency of Bregs but not Tregs in peripheral blood. Administration of the three SCFAs prior to the onset of collagen-induced arthritis in mice improved arthritic symptoms, increased the Bregs frequency, and decreased transitional B and follicular B cell frequency. However, the preceding phenomena could not be observed in mice with CD19+ B cells deficient in FFA2 receptors. The effects of the three SCFAs in RA were dependent on FFA2 receptors but were independent of the other five B cell receptors (FFA3 receptor, HCA2 receptor, PPARγ, Olfr-78, and AhR). CONCLUSIONS AND IMPLICATIONS: SCFAs regulate B cells differentiation via FFA2 receptors to alleviate RA. This provides new insights into the treatment of RA from an immunological and microbiological perspective.

Probabilistic Prediction of Nonadherence to Psychiatric Disorder Medication from Mental Health Forum Data: Developing and Validating Bayesian Machine Learning Classifiers.

Background: Medication nonadherence represents a major burden on national health systems. According to the World Health Organization, increasing medication adherence may have a greater impact on public health than any improvement in specific medical treatments. More research is needed to better predict populations at risk of medication nonadherence. Objective: To develop clinically informative, easy-to-interpret machine learning classifiers to predict people with psychiatric disorders at risk of medication nonadherence based on the syntactic and structural features of written posts on health forums. Methods: All data were collected from posts between 2016 and 2021 on mental health forum, administered by Together 4 Change, a long-running not-for-profit organisation based in Oxford, UK. The original social media data were annotated using the Tool for the Automatic Analysis of Syntactic Sophistication and Complexity (TAASSC) system. Through applying multiple feature optimisation techniques, we developed a best-performing model using relevance vector machine (RVM) for the probabilistic prediction of medication nonadherence among online mental health forum discussants. Results: The best-performing RVM model reached a mean AUC of 0.762, accuracy of 0.763, sensitivity of 0.779, and specificity of 0.742 on the testing dataset. It outperformed competing classifiers with more complex feature sets with statistically significant improvement in sensitivity and specificity, after adjusting the alpha levels with Benjamini-Hochberg correction procedure. Discussion. We used the forest plot of multiple logistic regression to explore the association between written post features in the best-performing RVM model and the binary outcome of medication adherence among online post contributors with psychiatric disorders. We found that increased quantities of 3 syntactic complexity features were negatively associated with psychiatric medication adherence: "dobj_stdev" (standard deviation of dependents per direct object of nonpronouns) (OR, 1.486, 95% CI, 1.202-1.838, P < 0.001), "cl_av_deps" (dependents per clause) (OR, 1.597, 95% CI, 1.202-2.122, P, 0.001), and "VP_T" (verb phrases per T-unit) (OR, 2.23, 95% CI, 1.211-4.104, P, 0.010). Finally, we illustrated the clinical use of the classifier with Bayes' monograph which gives the posterior odds and their 95% CI of positive (nonadherence) versus negative (adherence) cases as predicted by the best-performing classifier. The odds ratio of the posterior probability of positive cases was 3.9, which means that around 10 in every 13 psychiatric patients with a positive result as predicted by our model were following their medication regime. The odds ratio of the posterior probability of true negative cases was 0.4, meaning that around 10 in every 14 psychiatric patients with a negative test result after screening by our classifier were not adhering to their medications. Conclusion: Psychiatric medication nonadherence is a large and increasing burden on national health systems. Using Bayesian machine learning techniques and publicly accessible online health forum data, our study illustrates the viability of developing cost-effective, informative decision aids to support the monitoring and prediction of patients at risk of medication nonadherence.

Ghrelin Mitigates High-Glucose-Induced Oxidative Damage and Apoptosis in Lens Epithelial Cells.

Oxidative stress induced by high glucose (HG) plays an important role in the mechanism of diabetic cataract. Evidence has shown that effects from oxidative stress induced damage of lens or human lens epithelial (HLE) cells. Antioxidant supplementation is a plausible strategy to avoid oxidative stress and maintain the function of lens. Ghrelin have been used in treatment of many diseases. In this study, we found that ghrelin attenuated HG-induced loss of cell viability, reduced oxidative damage, and cell apoptosis in HLE cells. Ghrelin inhibited apoptosis through the downregulation of Bax and the upregulation of Bcl-2. Our results suggest that ghrelin could be considered as a promising therapeutic intervention for diabetic cataract. We also observed rat lens transparent in cultured media and examined lens histopathological changes. The results showed that ghrelin could inhibit the histopathological injury of lenses and ultrastructural changes induced by HG. In conclusion, ghrelin may play a role in the treatment of ocular diseases involving diabetic cataract.

The role of short-chain fatty acids in immunity, inflammation and metabolism.

Short-chain fatty acids (SCFAs) are carboxylic acids with carbon atom numbers less than 6, which are important metabolites of gut microbiome. Existing research shows that SCFAs play a vital role in the health and disease of the host. First, SCFAs are the key energy source for colon and ileum cells, and affect the intestinal epithelial barrier and defense functions by regulating related gene expression. Second, SCFAs regulate the function of innate immune cells to participate in the immune system, such as macrophages, neutrophils and dendritic cells. Third, SCFAs can also regulate the differentiation of T cells and B cells and the antigen-specific adaptive immunity mediated by them. Besides, SCFAs are raw materials for sugar and lipid synthesis, which provides a theoretical basis for studying the potential role of SCFAs in regulating energy homeostasis and metabolism. There are also studies showing that SCFAs inhibit tumor cell proliferation and promote apoptosis. In this article, we summarized in detail the role of SCFAs in immunity, inflammation and metabolism, and briefly introduced the role of SCFAs in tumor cell survival. It provides a systematic theoretical basis for the study of SCFAs as potential drugs to promote human health.

Developing Machine Learning and Statistical Tools to Evaluate the Accessibility of Public Health Advice on Infectious Diseases among Vulnerable People.

BACKGROUND: From Ebola, Zika, to the latest COVID-19 pandemic, outbreaks of highly infectious diseases continue to reveal severe consequences of social and health inequalities. People from low socioeconomic and educational backgrounds as well as low health literacy tend to be affected by the uncertainty, complexity, volatility, and progressiveness of public health crises and emergencies. A key lesson that governments have taken from the ongoing coronavirus pandemic is the importance of developing and disseminating highly accessible, actionable, inclusive, coherent public health advice, which represent a critical tool to help people with diverse cultural, educational backgrounds and varying abilities to effectively implement health policies at the grassroots level. OBJECTIVE: We aimed to translate the best practices of accessible, inclusive public health advice (purposefully designed for people with low socioeconomic and educational background, health literacy levels, limited English proficiency, and cognitive/functional impairments) on COVID-19 from health authorities in English-speaking multicultural countries (USA, Australia, and UK) to adaptive tools for the evaluation of the accessibility of public health advice in other languages. METHODS: We developed an optimised Bayesian classifier to produce probabilistic prediction of the accessibility of official health advice among vulnerable people including migrants and foreigners living in China. We developed an adaptive statistical formula for the rapid evaluation of the accessibility of health advice among vulnerable people in China. RESULTS: Our study provides needed research tools to fill in a persistent gap in Chinese public health research on accessible, inclusive communication of infectious diseases' prevention and management. For the probabilistic prediction, using the optimised Bayesian machine learning classifier (GNB), the largest positive likelihood ratio (LR+) 16.685 (95% confidence interval: 4.35, 64.04) was identified when the probability threshold was set at 0.2 (sensitivity: 0.98; specificity: 0.94). CONCLUSION: Effective communication of health risks through accessible, inclusive, actionable public advice represents a powerful tool to reduce health inequalities amidst health crises and emergencies. Our study translated the best-practice public health advice developed during the pandemic into intuitive machine learning classifiers for health authorities to develop evidence-based guidelines of accessible health advice. In addition, we developed adaptive statistical tools for frontline health professionals to assess accessibility of public health advice for people from non-English speaking backgrounds.

Detecting Symptom Errors in Neural Machine Translation of Patient Health Information on Depressive Disorders: Developing Interpretable Bayesian Machine Learning Classifiers.

Background: Due to its convenience, wide availability, low usage cost, neural machine translation (NMT) has increasing applications in diverse clinical settings and web-based self-diagnosis of diseases. Given the developing nature of NMT tools, this can pose safety risks to multicultural communities with limited bilingual skills, low education, and low health literacy. Research is needed to scrutinise the reliability, credibility, usability of automatically translated patient health information. Objective: We aimed to develop high-performing Bayesian machine learning classifiers to assist clinical professionals and healthcare workers in assessing the quality and usability of NMT on depressive disorders. The tool did not require any prior knowledge from frontline health and medical professionals of the target language used by patients. Methods: We used Relevance Vector Machine (RVM) to increase generalisability and clinical interpretability of classifiers. It is a typical sparse Bayesian classifier less prone to overfitting with small training datasets. We optimised RVM by leveraging automatic recursive feature elimination and expert feature refinement from the perspective of health linguistics. We evaluated the diagnostic utility of the Bayesian classifier under different probability cut-offs in terms of sensitivity, specificity, positive and negative likelihood ratios against clinical thresholds for diagnostic tests. Finally, we illustrated interpretation of RVM tool in clinic using Bayes' nomogram. Results: After automatic and expert-based feature optimisation, the best-performing RVM classifier (RVM_DUFS12) gained the highest AUC (0.8872) among 52 competing models with distinct optimised, normalised features sets. It also had statistically higher sensitivity and specificity compared to other models. We evaluated the diagnostic utility of the best-performing model using Bayes' nomogram: it had a positive likelihood ratio (LR+) of 4.62 (95% C.I.: 2.53, 8.43), and the associated posterior probability (odds) was 83% (5.0) (95% C.I.: 73%, 90%), meaning that approximately 10 in 12 English texts with positive test are likely to contain information that would cause clinically significant conceptual errors if translated by Google; it had a negative likelihood ratio (LR-) of 0.18 (95% C.I.: 0.10,0.35) and associated posterior probability (odds) was 16% (0.2) (95% C.I: 10%, 27%), meaning that about 10 in 12 English texts with negative test can be safely translated using Google.

Supporting Risk-Aware Use of Online Translation Tools in Delivering Mental Healthcare Services among Spanish-Speaking Populations.

Neural machine translation technologies are having increasing applications in clinical and healthcare settings. In multicultural countries, automatic translation tools provide critical support to medical and health professionals in their interaction and exchange of health messages with migrant patients with limited or non-English proficiency. While research has mainly explored the usability and limitations of state-of-the-art machine translation tools in the detection and diagnosis of physical diseases and conditions, there is a persistent lack of evidence-based studies on the applicability of machine translation tools in the delivery of mental healthcare services for vulnerable populations. Our study developed Bayesian machine learning algorithms using relevance vector machine to support frontline health workers and medical professionals to make better informed decisions between risks and convenience of using online translation tools when delivering mental healthcare services to Spanish-speaking minority populations living in English-speaking countries. Major strengths of the machine learning classifier that we developed include scalability, interpretability, and adaptability of the classifier for diverse mental healthcare settings. In this paper, we report on the process of the Bayesian machine learning classifier development through automatic feature optimisation and the interpretation of the classifier-enabled assessment of the suitability of original English mental health information for automatic online translation. We elaborate on the interpretation of the assessment results in clinical settings using statistical tools such as positive likelihood ratios and negative likelihood ratios.

Transcriptome-Based Analysis Reveals Therapeutic Effects of Resveratrol on Endometriosis in aRat Model.

INTRODUCTION: Endometriosis (EMs) is associated with severe chronic pelvic pain and infertility and the development of improved EMs treatment options is an ongoing focus. In this study, we investigated the effects of resveratrol on EMs and analyzed transcriptional changes in the lesions of model rats before and after resveratrol treatment. METHODS: We established arat model of endometriosis through the trans-implantation of endometrial fragments to the peritoneal wall and then used resveratrol as treatment. We then analyzed the results using RNA sequencing of the lesion tissues of each of the model rats before resveratrol treatment and the reduced lesion tissues after the treatment. Examinations of anatomy, biochemistry, immunohistochemical staining and flow cytometry examinations were also conducted. Other trans-implanted rats were also given sham treatments as sham-treatment control and other untrans-implanted rats served as sham-operation controls. RESULTS: In addition to the obvious lesions observed in the model rats, there were significant differences in the glucose tolerance, macrophage M1/M2 polarization, and adipocyte sizes between the treated model rats and sham (control) rats. Resveratrol treatment in the model rats showed significant efficacy and positive therapeutic effect. Transcriptional analysis showed that the effects of resveratrol on the endometriosis model rats were manifested by alterations in the PPAR, insulin resistance, MAPK and PI3K/Akt signaling pathways. Correspondingly, changes in PPARγ activation, M1/M2 polarization and lipid metabolism were also detected after resveratrol treatment. DISCUSSION: Our study revealed that resveratrol treatment displayed efficient therapeutic effects for EMs model rats, probably through its important roles in anti-inflammation, immunoregulation and lipid-related metabolism regulation.

Silica-Based Nanoframeworks Involved Hepatocellular Carcinoma Theranostic.

Silica-based nanoframeworks have been extensively studied for diagnosing and treating hepatocellular carcinoma (HCC). Several reviews have summarized the advantages and disadvantages of these nanoframeworks and their use as drug-delivery carriers. Encouragingly, these nanoframeworks, especially those with metal elements or small molecular drugs doping into the skeleton structure or modifying onto the surface of nanoparticles, could be multifunctional components participating in HCC diagnosis and treatment rather than functioning only as drug-delivery carriers. Therefore, in this work, we described the research progress of silica-based nanoframeworks involved in HCC diagnosis (plasma biomarker detection, magnetic resonance imaging, positron emission tomography, photoacoustic imaging, fluorescent imaging, ultrasonography, etc.) and treatment (chemotherapy, ferroptotic therapy, radiotherapy, phototherapy, sonodynamic therapy, immunotherapy, etc.) to clarify their roles in HCC theranostics. Further, the future expectations and challenges associated with silica-based nanoframeworks were highlighted. We believe that this review will provide a comprehensive understanding for researchers to design novel, functional silica-based nanoframeworks that can effectively overcome HCC.

Drug fever induced by carboplatin-based regimens: Higher incidence in a women's hospital.

OBJECTIVE: Previous studies have reported low incidence of carboplatin-related drug fever in early cancer treatment cycles. This study describes and analyzes relatively higher incidence rate of carboplatin-related drug fever associated with gynecologic cancer chemotherapy in order to allay anxiety in patients and avoid unnecessary interventions. MATERIALS AND METHODS: All gynecologic cancer cases treated with carboplatin in a women's hospital in 2017 and 2018 were retrospectively reviewed and analyzed. Data for patients who experienced carboplatin-induced drug fever and those who received the same treatment but did not experience drug fever were compared for statistical significance. Risk factors for drug fever were identified by logistic regression analysis. RESULTS: In total, 318 females with a mean age of 52 years were included in the analysis. Drug fever was observed in 25 patients (7.86%) in 45 cycles of total 1605 carboplatin-containing infusions. Fever occurred at a median of the third (range: 1-7) cycle, starting at 10.62 h (range: 1.18-50.35 h) after carboplatin infusion, and was generally controlled within 3 days. After chemotherapy rechallenge, the mean frequency of drug fever was 2 times per patient (range: 1-4 times). There were 35/45 drug fever incidents (77.78%) that were classified as grade II; in 15/45 cases (33.33%), antibiotic treatment was immediately initiated to prevent infection. Younger age was found to be a risk factor for drug fever following carboplatin treatment (odds ratio = 0.126, 95% confidence interval: 0.025-0.628; p < 0.05). CONCLUSIONS: The retrospective analysis demonstrated that carboplatin-induced drug fever, which occurred on post treatment 3 days, was a type of delayed hypersensitivity reaction with an incidence rate of 7.86% in gynecologic cancer. Younger age was identified as a risk factor. Drug fever is generally tolerated by patients, who insist on chemotherapy. Knowledge of carboplatin-induced drug fever may help physicians reach timely recognition for appropriate interventions.

mRNA delivery via non-viral carriers for biomedical applications.

As an emerging new class of nucleic acid drugs, messenger RNA (mRNA) has huge potential in immunotherapy, regenerative medicine, vaccine, and gene editing. Comparing with siRNA and pDNA, mRNA is more vulnerable to nucleases in vivo. However, the lack of effective and safe delivery methods impedes the broad application of mRNA-based therapeutics. Up to now, the delivery of mRNA remains largely unexplored, and therefore, is a hot topic in the field of gene therapy. In this review, we will summarize the ongoing challenges in mRNA-based therapeutics and unmet requirements for delivery vehicles in terms of the unique structure of mRNA. We then highlight the advancement in mRNA delivery in both fundamental research and clinical applications. Finally, a prospective will be proposed upon reviewing the current progress in mRNA delivery.

Use of Machine Learning Algorithms to Predict the Understandability of Health Education Materials: Development and Evaluation Study.

BACKGROUND: Improving the understandability of health information can significantly increase the cost-effectiveness and efficiency of health education programs for vulnerable populations. There is a pressing need to develop clinically informed computerized tools to enable rapid, reliable assessment of the linguistic understandability of specialized health and medical education resources. This paper fills a critical gap in current patient-oriented health resource development, which requires reliable and accurate evaluation instruments to increase the efficiency and cost-effectiveness of health education resource evaluation. OBJECTIVE: We aimed to translate internationally endorsed clinical guidelines to machine learning algorithms to facilitate the evaluation of the understandability of health resources for international students at Australian universities. METHODS: Based on international patient health resource assessment guidelines, we developed machine learning algorithms to predict the linguistic understandability of health texts for Australian college students (aged 25-30 years) from non-English speaking backgrounds. We compared extreme gradient boosting, random forest, neural networks, and C5.0 decision tree for automated health information understandability evaluation. The 5 machine learning models achieved statistically better results compared to the baseline logistic regression model. We also evaluated the impact of each linguistic feature on the performance of each of the 5 models. RESULTS: We found that information evidentness, relevance to educational purposes, and logical sequence were consistently more important than numeracy skills and medical knowledge when assessing the linguistic understandability of health education resources for international tertiary students with adequate English skills (International English Language Testing System mean score 6.5) and high health literacy (mean 16.5 in the Short Assessment of Health Literacy-English test). Our results challenge the traditional views that lack of medical knowledge and numerical skills constituted the barriers to the understanding of health educational materials. CONCLUSIONS: Machine learning algorithms were developed to predict health information understandability for international college students aged 25-30 years. Thirteen natural language features and 5 evaluation dimensions were identified and compared in terms of their impact on the performance of the models. Health information understandability varies according to the demographic profiles of the target readers, and for international tertiary students, improving health information evidentness, relevance, and logic is critical.