Evolution in key indicators of maternal and child health across the wealth gradient in 41 sub-Saharan African countries, 1986-2019.

BACKGROUND: Aggregate trends can be useful for summarizing large amounts of information, but this can obscure important distributional aspects. Some population subgroups can be worse off even as averages climb, for example. Distributional information can identify health inequalities, which is essential to understanding their drivers and possible remedies. METHODS: Using publicly available Demographic and Health Survey (DHS) data from 41 sub-Saharan African countries from 1986 to 2019, we analyzed changes in coverage for eight key maternal and child health indicators: first dose of measles vaccine (MCV1); Diphtheria-Pertussis-Tetanus (DPT) first dose (DPT1); DPT third dose (DPT3); care-seeking for diarrhea, acute respiratory infections (ARI), or fever; skilled birth attendance (SBA); and having four antenatal care (ANC) visits. To evaluate whether coverage diverged or converged over time across the wealth gradient, we computed several dispersion metrics including the coefficient of variation across wealth quintiles. Slopes and 5-year moving averages were computed to identify overall long-term trends. RESULTS: Average coverage increased for all quintiles and indicators, although the range and the speed at which they increased varied widely. There were small changes in the wealth-related gap for SBA, ANC, and fever. The wealth-related gap of vaccination-related indicators (DPT1, DPT3, MCV1) decreased over time. Compared to 2017, the wealth-gap between richest and poorest quintiles in 1995 was 7 percentage points larger for ANC and 17 percentage points larger for measles vaccination. CONCLUSIONS: Maternal and child health indicators show progress, but the distributional effects show differential evolutions in inequalities. Several reasons may explain why countries had smaller wealth-related gap trends in vaccination-related indicators compared to others. In addition to service delivery differences, we hypothesize that the allocation of development assistance for health, the prioritization of vaccine-preventable diseases on the global agenda, and indirect effects of structural adjustment programs on health system-related indicators might have played a role.

Rational Design of Lipid Nanoparticles for Enhanced mRNA Vaccine Delivery via Machine Learning.

Since the coronavirus pandemic, mRNA vaccines have revolutionized the field of vaccinology. Lipid nanoparticles (LNPs) are proposed to enhance mRNA delivery efficiency; however, their design is suboptimal. Here, a rational method for designing LNPs is explored, focusing on the ionizable lipid composition and structural optimization using machine learning (ML) techniques. A total of 213 LNPs are analyzed using random forest regression models trained with 314 features to predict the mRNA expression efficiency. The models, which predict mRNA expression levels post-administration of intradermal injection in mice, identify phenol as the dominant substructure affecting mRNA encapsulation and expression. The specific phospholipids used as components of the LNPs, as well as the N/P ratio and mass ratio, are found to affect the efficacy of mRNA delivery. Structural analysis highlights the impact of the carbon chain length on the encapsulation efficiency and LNP stability. This integrated approach offers a framework for designing advanced LNPs and has the potential to unlock the full potential of mRNA therapeutics.

Financial hardship associated with catastrophic out-of-pocket spending tied to primary care services in low- and lower-middle-income countries: findings from a modeling study.

BACKGROUND: Financial risk protection (FRP) is a key component of universal health coverage (UHC): all individuals must be able to obtain the health services they need without experiencing financial hardship. In many low-income and lower-middle-income countries, however, the health system fails to provide sufficient protection against high out-of-pocket (OOP) spending on health services. In 2018, OOP health spending comprised approximately 40% of current health expenditures in low-income and lower-middle-income countries. METHODS: We model the household risk of catastrophic health expenditures (CHE), conditional on having a given disease or condition-defined as OOP health spending that exceeds a threshold percentage (10, 25, or 40%) of annual income-for 29 health services across 13 disease categories (e.g., diarrheal diseases, cardiovascular diseases) in 34 low-income and lower-middle-income countries. Health services were included in the analysis if delivered at the primary care level and part of the Disease Control Priorities, 3rd edition "highest priority package." Data were compiled from several publicly available sources, including national health accounts, household surveys, and the published literature. A risk of CHE, conditional on having disease, was modeled as depending on usage, captured through utilization indicators; affordability, captured via the level of public financing and OOP health service unit costs; and income. RESULTS: Across all countries, diseases, and health services, the risk of CHE (conditional on having a disease) would be concentrated among poorer quintiles (6.8% risk in quintile 1 vs. 1.3% in quintile 5 using a 10% CHE threshold). The risk of CHE would be higher for a few disease areas, including cardiovascular disease and mental/behavioral disorders (7.8% and 9.8% using a 10% CHE threshold), while lower risks of CHE were observed for lower cost services. CONCLUSIONS: Insufficient FRP stands as a major barrier to achieving UHC, and risk of CHE is a major problem for health systems in low-income and lower-middle-income countries. Beyond its threat to the financial stability of households, CHE may also lead to worse health outcomes, especially among the poorest for whom both ill health and financial risk are most severe. Modeling the risk of CHE associated with specific disease areas and services can help policymakers set progressive health sector priorities. Decision-makers could explicitly include FRP as a criterion for consideration when assessing the health interventions for inclusion in national essential benefit packages.

Biochar Production and Demineralization Characteristics of Food Waste for Fuel Conversion.

The pyrolysis of food waste has high economic potential and produces several value-added products, such as gas, bio-oil, and biochar. In South Korea, biochar production from food waste is prohibited, because dioxins are generated during combustion caused by the chloride ions arising from the high salt content. This study is the first to examine the water quality and the applicability of food waste-based biochar as solid refuse fuel (SRF) based on a demineralization process. The calorific value increased after demineralization due to the removal of ionic substances and the high carbon content. The chloride ion removal rate after demineralization increased with the increasing pyrolysis temperature. A proximate analysis of biochar indicated that the volatile matter decreased, while ash and fixed carbon increased, with increasing pyrolysis temperature. At 300 °C pyrolysis temperature, all domestic bio-SRF standards were met. The organic matter concentration in water decreased with increasing carbonization temperature, and the concentrations of soluble harmful substances, such as volatile organic compounds (VOCs), were within the standards or non-detectable. These results suggest that biochar can be efficiently generated from food waste while meeting the emission standards for chloride ions, dissolved VOCs, ash, and carbon.

RHOA protein expression correlates with clinical features in gastric cancer: a systematic review and meta-analysis.

BACKGROUND: Gastric cancer (GC) is one of the most fatal cancers worldwide and is generally only detected after it has progressed to an advanced stage. Since there is a lack of comprehensive data on RHOA protein expression of patients with GC, this study utilized a systematic review and meta-analysis to address the limitation. The objective of this meta-analysis was to link GC clinical features with RHOA protein high- vs. low-expressing patients with GC. METHODS: The PubMed and Web of Science were used for a systematic literature review of GC related to RHOA. The included studies were obtained from two literature databases from past to Aug 31, 2021, by searching keywords. This meta-analysis followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. The odds ratios (ORs) and 95% confidential intervals (CIs) for clinical features were estimated according to the high and low protein expression levels of RhoA. The mean effect sizes of ORs were obtained using the random-effects and fixed-effects models of meta-analysis. Heterogeneity of the studies was assesed by using statistics: τ2, I2; and Q values. The symmetry of funnel plots were inspected for publication bias. RESULTS: Finally, 10 studies including 1,389 patients with GC (735 RHOA-positive and 654 RHOA-negative) were eligible for our meta-analysis to estimate associations between the protein expression and clinical features (e.g., Union for International Cancer Control [UICC] stage progression, differentiation, Lauren histological classification, and vascular invasion). In our meta-analysis, RHOA positive expression was determined to have a statistically significant association with UICC stage progression (P = 0.02) and poorly differentiated status (P = 0.02). The association between RHOA positivity and Lauren subtypes was not statistically significant (P = 0.07). CONCLUSIONS: This meta-analysis suggested that RhoA protein expression in patients with GC was associated with clinical features: UICC stage progression and poorly differentiated status. Our findings are inconclusive but indicate that high RHOA protein expressing patients with GC could predict advanced UICC stages. A large prospective cohort study is required for validation in future.

Rhamnolipid-coated W/O/W double emulsion nanoparticles for efficient delivery of doxorubicin/erlotinib and combination chemotherapy.

BACKGROUND: Combination therapy using more than one drug can result in a synergetic effect in clinical treatment of cancer. For this, it is important to develop an efficient drug delivery system that can contain multiple drugs and provide high accumulation in tumor tissue. In particular, simultaneous and stable loading of drugs with different chemical properties into a single nanoparticle carrier is a difficult problem. RESULTS: We developed rhamnolipid-coated double emulsion nanoparticles containing doxorubicin and erlotinib (RL-NP-DOX-ERL) for efficient drug delivery to tumor tissue and combination chemotherapy. The double emulsion method enabled simultaneous loading of hydrophilic DOX and hydrophobic ERL in the NPs, and biosurfactant RL provided stable surface coating. The resulting NPs showed fast cellular uptake and synergetic tumor cell killing in SCC7 cells. In real-time imaging, they showed high accumulation in SCC7 tumor tissue in mice after intravenous injection. Furthermore, enhanced tumor suppression was observed by RL-NP-DOX-ERL in the same mouse model compared to control groups using free drugs and NPs containing a single drug. CONCLUSIONS: The developed RL-NP-DOX-ERL provided efficient delivery of DOX and ERL to tumor tissue and successful tumor therapy with a synergetic effect. Importantly, this study demonstrated the promising potential of double-emulsion NPs and RL coating for combination therapy.

Performance Comparisons of AlexNet and GoogLeNet in Cell Growth Inhibition IC50 Prediction.

Drug responses in cancer are diverse due to heterogenous genomic profiles. Drug responsiveness prediction is important in clinical response to specific cancer treatments. Recently, multi-class drug responsiveness models based on deep learning (DL) models using molecular fingerprints and mutation statuses have emerged. However, for multi-class models for drug responsiveness prediction, comparisons between convolution neural network (CNN) models (e.g., AlexNet and GoogLeNet) have not been performed. Therefore, in this study, we compared the two CNN models, GoogLeNet and AlexNet, along with the least absolute shrinkage and selection operator (LASSO) model as a baseline model. We constructed the models by taking drug molecular fingerprints of drugs and cell line mutation statuses, as input, to predict high-, intermediate-, and low-class for half-maximal inhibitory concentration (IC50) values of the drugs in the cancer cell lines. Additionally, we compared the models in breast cancer patients as well as in an independent gastric cancer cell line drug responsiveness data. We measured the model performance based on the area under receiver operating characteristic (ROC) curves (AUROC) value. In this study, we compared CNN models for multi-class drug responsiveness prediction. The AlexNet and GoogLeNet showed better performances in comparison to LASSO. Thus, DL models will be useful tools for precision oncology in terms of drug responsiveness prediction.

Association between level of suicide risk, characteristics of suicide attempts, and mental disorders among suicide attempters.

BACKGROUND: Past attempted suicide is a strong predictor of future suicide risk, but the risk varies among suicide attempters. Hence, it is important to clarify distinguishing features of lifetime attempters with a high level of current suicide risk for efficient preventive management. METHODS: We compared characteristics of suicide attempts and clinical characteristics among high-, moderate-, and low-risk attempters. Among the total of 6022 participants in the Korean Epidemiologic Catchment Area study, 193 reported a suicide attempt in their lifetime, 36 of which had high, 126 moderate, and 30 low levels of current suicide risk (1 incomplete response). RESULTS: High-risk suicide attempters had more past attempts compared with moderate- and low-risk suicide attempters. Suicide attempts were closely linked to a wide range of psychiatric comorbidities regardless of degree of current level of suicide risk, but the relative risk for having at least one mental disorder was the highest in high-risk attempters. Specifically, the relative risks for depressive disorder, anxiety disorders including obsessive-compulsive disorder and post-traumatic stress disorder, and substance use disorders were higher in high-risk attempters, and relative risk for somatoform disorder was higher in low-risk attempters than others. CONCLUSIONS: Our findings indicated that special attention is required for suicide attempters with a history of repeated attempts and current mental disorders, particularly anxiety disorders.

A cross-sectional study about associations between personality characteristics and mental health service utilization in a Korean national community sample of adults with psychiatric disorders.

BACKGROUND: Personality traits are not only associated with psychiatric symptoms, but also with treatment seeking behavior. Our purpose was to examine the relationship between mental health service utilization and personality characteristics in a nationwide community sample of Korean adults. METHOD: Of the 6022 subjects aged 18-74 years who participated in the Korean Epidemiologic Catchment Area study, 1544 (25.6%) with a lifetime diagnosis of any DSM-IV psychiatric disorder were analyzed. Diagnostic assessments were based on the Composite International Diagnostic Interview and personality constructs were measured by Big Five Personality Inventory-10. RESULTS: Of the 1544 participants, 275 (17.8%) had used mental health services. Multivariate analyses revealed positive associations between mental health service utilization and both neuroticism and openness, and an inverse association between mental health service utilization and agreeableness. CONCLUSIONS: These findings suggest that specific personality traits may have a role in treatment-seeking behaviors for mental health problems independent of the psychiatric disorder.

Association between energy drink intake, sleep, stress, and suicidality in Korean adolescents: energy drink use in isolation or in combination with junk food consumption.

BACKGROUND: A considerable amount of research suggests that the frequent use of caffeinated energy drinks may be associated with undesirable effects, particularly so in children and adolescents. This study aimed to investigate the associations between energy drink intake and mental health problems, in isolation or in combination with junk food consumption, in a nationally representative sample of Korean adolescents. METHODS: Data from the 2015 Korean Youth Risk Behavior Web-Based Survey, collected from 68,043 adolescents aged 12-18 years (mean age 15.09 ± 1.72 years), were analyzed. Questionnaires were administered to collect information related to dietary behavior including energy drink intake and junk food consumption. Single item measures of sleep dissatisfaction, stress, depression, suicidal ideation, suicide plan, and suicide attempt were also administered. Associations between energy drink intake and sleep dissatisfaction, perceived severe stress, persistent depressive mood, and suicidality were investigated, and a multivariate approach was taken so that additional variance from demographic and lifestyle factors could be controlled for statistically. RESULTS: Energy drink intake was significantly associated with sleep dissatisfaction (adjusted odd ratios [AORs] = 1.64 and 1.25), severe stress (AORs = 2.23 and 1.38), depressive mood (AOR = 2.59 and 1.51), suicidal ideation (AORs = 3.14 and 1.43), suicide plan (AORs = 4.65 and 1.78), and suicide attempt (AORs = 6.79 and 1.91), with a higher risk for more frequent use of energy drinks (≥5 times/wk) than for less frequent use (1-4 times/wk). The detrimental effect of energy drinks on mental health was particularly prominent in frequent junk food consumers. CONCLUSIONS: Our data suggest that energy drink intake had detrimental effects related to stress, sleep dissatisfaction, mood, and suicidality, in isolation or in combination with junk food consumption, in Korean adolescents. However, the cross-sectional study design prevents our ability to assess causal relationships.

HIF1A protein expression is correlated with clinical features in gastric cancer: an updated systematic review and meta-analysis.

To elucidate the correlation of HIF1A with clinicopathologic characteristics in patients with gastric cancer (GC), we conducted a systematic review and meta-analysis. We searched PubMed, Embase and Web of Science for studies on GC and HIF1A, covering studies published until January 31st, 2022. We calculated odds ratios (ORs) and 95% confidence intervals (CIs) for clinical characteristics based on high and low HIF1A protein levels. We used random-effects and fixed-effects meta-analysis methods to determine mean effect sizes of ORs and evaluated publication heterogeneity with τ2, I2, and Q values. Additionally, we generated funnel plots to inspect publication bias. Our meta-analysis included 20 publications with 3416 GC patients to estimate the association between high or low HIF1A expression and clinical characteristics. Positive HIF1A expression was significantly associated with T stage progression (OR: 2.46; 95% CI 1.81-3.36; P < 0.01), TNM stage progression (OR: 2.50; 95% CI 1.61-3.87; P < 0.01), lymph node metastasis (OR: 2.06; 95% CI 1.44-2.94; P < 0.01), undifferentiated status (OR: 1.83; 95% CI 1.45-2.32; P < 0.01), M stage progression (OR: 2.34; 95% CI 1.46-3.77; P < 0.01), Borrmann stage progression (OR: 1.48; 95% CI 1.02-2.15; P = 0.04), larger tumor size (OR: 1.27; 95% CI 1.06-1.52; P < 0.01), vascular invasion (OR: 1.94; 95% CI 1.38-2.72; P < 0.01), and higher vascular endothelial growth factor (VEGF) protein expression (OR: 2.61; 95% CI 1.79-3.80; P < 0.01) in our meta-analysis. GC Patients highly expressing HIF1A protein might be prone to tumor progression, poorly differentiated GC cell types, and a high VEGF expression.

Enhancing the potential application of food-waste biochar as a sustainable bio-solid fuel: Analysis of post-treatment and combustion behavior.

Food-waste biochar holds significant potential as a bio-solid fuel for achieving carbon neutrality; however, its high content of sodium (Na), potassium (K), calcium (Ca), chlorine (Cl), and nitrogen, inhibits its potential use. This study explored the effects of post-treatment with ascorbic, acetic, citric, and iminodiacetic acids on the properties of food-waste biochar and volatile ionic substances to establish a foundation for assessing both the environmental impact and practical use of food waste. Post-treatment with organic acids achieved 92% Cl-removal efficiency and induced deformation of the functional groups of food-waste biochar surfaces, leading to the re-adsorption of alkali and alkaline earth metals. This re-adsorption of alkali metal ions showed a distinct correlation with NOx mitigation. The amount of re-adsorbed Na and K varied based on the types of organic acids, resulting in different NOx emission reduction effects. Iminodiacetic acid was particularly effective in alleviating Ca and PO4 volatilization, whereas citric acid exhibited the highest Ca elution performance, and the Ca-contained leachate is a potential source of CO2 storage through indirect mineral carbonation. Acetic acid is the most feasible alternative, considering both economic and environmental aspects. The findings suggest that the post-treatment of food-waste biochar effectively mitigates air pollutants during combustion and is beneficial for sustainable biosolid fuel production and bio-waste management.

Injectable and Self-Curing Single-Component Hydrogel for Stem Cell Encapsulation and In Vivo Bone Regeneration.

An ideal hydrogel for stem cell therapy would be injectable and efficiently promote stem cell proliferation and differentiation in body. Herein, an injectable, single-component hydrogel with hyaluronic acid (HA) modified with phenylboronic acid (PBA) and spermidine (SM) is introduced. The resulting HAps (HA-PBA-SM) hydrogel is based on the reversible crosslinking between the diol and the ionized PBA, which is stabilized by the SM. It has a shear-thinning property, enabling its injection through a syringe to form a stable hydrogel inside the body. In addition, HAps hydrogel undergoes a post-injection "self-curing," which stiffens the hydrogel over time. This property allows the HAps hydrogel to meet the physical requirements for stem cell therapy in rigid tissues, such as bone, while maintaining injectability. The hydrogel enabled favorable proliferation of human mesenchymal stem cells (hMSCs) and promoted their differentiation and mineralization. After the injection of hMSCs-containing HAps into a rat femoral defect model, efficient osteogenic differentiation of hMSCs and bone regeneration is observed. The study demonstrates that simple cationic modification of PBA-based hydrogel enabled efficient gelation with shear-thinning and self-curing properties, and it would be highly useful for stem cell therapy and in vivo bone regeneration.

A performance evaluation of drug response prediction models for individual drugs.

Drug response prediction is important to establish personalized medicine for cancer therapy. Model construction for predicting drug response (i.e., cell viability half-maximal inhibitory concentration [IC50]) of an individual drug by inputting pharmacogenomics in disease models remains critical. Machine learning (ML) has been predominantly applied for prediction, despite the advent of deep learning (DL). Moreover, whether DL or traditional ML models are superior for predicting cell viability IC50s has to be established. Herein, we constructed ML and DL drug response prediction models for 24 individual drugs and compared the performance of the models by employing gene expression and mutation profiles of cancer cell lines as input. We observed no significant difference in drug response prediction performance between DL and ML models for 24 drugs [root mean squared error (RMSE) ranging from 0.284 to 3.563 for DL and from 0.274 to 2.697 for ML; R2 ranging from -7.405 to 0.331 for DL and from -8.113 to 0.470 for ML]. Among the 24 individual drugs, the ridge model of panobinostat exhibited the best performance (R2 0.470 and RMSE 0.623). Thus, we selected the ridge model of panobinostat for further application of explainable artificial intelligence (XAI). Using XAI, we further identified important genomic features for panobinostat response prediction in the ridge model, suggesting the genomic features of 22 genes. Based on our findings, results for an individual drug employing both DL and ML models were comparable. Our study confirms the applicability of drug response prediction models for individual drugs.

Improvement of phoswich detector-based ß+/γ-ray discrimination algorithm with deep learning.

BACKGROUND: Positron probes can accurately localize malignant tumors by directly detecting positrons emitted from positron-emitting radiopharmaceuticals that accumulate in malignant tumors. In the conventional method for direct positron detection, multilayer scintillator detection and pulse shape discrimination techniques are used. However, some γ-rays cannot be distinguished by conventional methods. Accordingly, these γ-rays are misidentified as positrons, which may increase the error rate of positron detection. PURPOSE: To analyze the energy distribution in each scintillator of the multilayer scintillator detector to distinguish true positrons and γ-rays and to improve the positron detection algorithm by discriminating true and false positrons. METHODS: We used Autoencoder, an unsupervised deep learning architecture, to obtain the energy distribution data in each scintillator of the multilayer scintillator detector. The Autoencoder was trained to separate the combined signals generated from the multilayer scintillator detector into two signals of each scintillator. An energy window was then applied to the energy distribution obtained using the trained Autoencoder to distinguish true positrons from false positrons. Finally, the performance of the proposed method and conventional positron detection algorithm was evaluated in terms of the sensitivity and error rate for positron detection. RESULTS: The energy distribution map obtained using the trained Autoencoder was proven to be similar to that of the simulated results. Furthermore, the proposed method demonstrated a 29.79% (+0.42%p) increase in positron detection sensitivity compared to the conventional method, both having an equal error rate of 0.48%. However, when both methods were set to have the same sensitivity of 1.83%, the proposed method had an error rate that was 25.0% (-0.16%p) lower than that of the conventional method. CONCLUSIONS: We proposed and developed an Autoencoder-based positron detection algorithm that can discriminate between true and false positrons with a smaller error rate than conventional methods. We verified that the proposed method could increase the positron detection sensitivity while maintaining a low error rate compared to the conventional method. If the proposed algorithm is implemented in handheld positron detection probes or cameras, diseases such as cancers can be more accurately localized in a shorter time compared with using traditional methods.

Characterization of the genomic alterations in poorly differentiated thyroid cancer.

Poorly differentiated thyroid carcinoma (PDTC) is a subtype of thyroid cancer that has a high rate of metastasis or recurrence and a relatively poor prognosis. However, there are few studies that have been conducted on PDTC at the whole protein-coding gene scale. Here, we performed genomic profiling of 15 patients with PDTC originated from follicular thyroid carcinoma using whole exome sequencing and also performed gene functional enrichment analysis of differentially expressed genes (DEGs) for three patients. Further, we investigated genetic variants associated with PDTC progression and the characteristics of clinical pathology. We revealed somatic genomic alterations in the RAF1, MAP2K2, and AKT2 genes that were not reported in previous studies. We confirmed frequent occurrences in the RAS gene in patients with PDTC; the genetic alterations were associated with the RAS-RAF-MEK-ERK/JNK, PI3K-AKT-mTOR signaling pathways, and the cell cycle. DEG analysis showed that immune response was lower in cancer tissues than in normal tissues. Through the association analysis of somatic mutations and the characteristics of clinical pathology from patients with PDTC, the somatic mutations of ABCA12, CLIP1, and ATP13A3 were significantly associated with a vascular invasion phenotype. By providing molecular genetic insight on PDTC, this study may contribute to the discovery of novel therapeutic target candidates.

Organic and inorganic nanomedicine for combination cancer therapies.

In many cases, a single mode of cancer therapy shows limited efficacy in treating complex and heterogeneous tumors. To improve cancer treatment, combining chemo-, photodynamic-, photothermal-, radio-, and immunotherapy is clinically recognized. When different therapeutic treatments are combined, they often show synergetic effects that further improve therapeutic outcomes. In this review, we introduce nanoparticle (NP)-based combination cancer therapies that use organic and inorganic NPs. Liposomes, polymers, and exosomes can be prepared with amphiphilic properties, high physical stability, and low immune response to treat cancers in a multimodal way. Inorganic NPs, including upconversion, plasmonic, and mesoporous silica NPs, have emerged as a new technology for photodynamic-, photothermal-, and immunotherapy. These NPs can simultaneously carry multiple drug molecules and deliver them efficiently to tumor tissue, as demonstrated in many studies. In addition to reviewing recent advances in organic and inorganic NPs used in combination therapy for cancers, we also discuss their rational design and the outlook for future nanomedicine development.

Efficient drug supply in stem cell cytosol via pore-forming saponin nanoparticles promotes in vivo osteogenesis and bone regeneration.

Directional differentiation of stem cells is a key step in stem cell therapy. In this study, we developed saponin-based nanoparticles (Ad-SNPs) containing dexamethasone (Dex) and alpha-lipoic acid (ALA) to promote osteogenic differentiation of human mesenchymal stem cells (hMSCs) and bone regeneration. The Ad-SNPs can achieve rapid cellular uptake through a pore-forming effect without cytotoxic cationic charges. They also provide extended retention in cell cytosol due to their uptake route. These properties are advantageous in efficiently supplying drugs to the hMSCs. The combination of Dex and ALA facilitated mitochondrial fusion and prevented reactive oxygen species-induced DNA damage. It also helped to preserve mitochondrial dynamics, and the efficient supply of it provided by the Ad-SNPs induced differentiation of hMSCs into osteoblasts. The Ad-SNPs showed outstanding performance in osteoblast differentiation, maturation, and mineralization in 3D culture compared with NPs without saponin and with free drugs. When Ad-SNP-treated hMSCs were tested in a rat femoral bone defect model, they showed the fastest regeneration of bones and complete repair in the shortest period among all groups. To the best of our knowledge, this study is the first application of pore-forming saponin-based NPs with rapid cellular uptake and extended retention to stem cell therapy, and we demonstrated their promising potential in bone regeneration and efficient delivery of Dex and ALA.

Oncogenic signaling pathways and hallmarks of cancer in Korean patients with acral melanoma.

Acral melanoma (AM), a rare subtype of cutaneous melanoma, shows higher incidence in Asians, including Koreans, than in Caucasians. However, the genetic modification associated with AM in Koreans is not well known and has not been comprehensively investigated in terms of oncogenic signaling, and hallmarks of cancer. We performed whole-exome and RNA sequencing for Korean patients with AM and acquired the genetic alterations and gene expression profiles. KIT alterations (previously known to be recurrent alterations in AM) and CDK4/CCND1 copy number amplifications were identified in the patients. Genetic and transcriptomic alterations in patients with AM were functionally converge to the hallmarks of cancer and oncogenic pathways, including 'proliferative signal persistence', 'apoptotic resistance', and 'activation of invasion and metastasis', despite the heterogeneous somatic mutation profiles of Korean patients with AM. This study may provide a molecular understanding for therapeutic strategy for AM.

Overcoming the obstacles of current photodynamic therapy in tumors using nanoparticles.

Photodynamic therapy (PDT) has been applied in clinical treatment of tumors for a long time. However, insufficient supply of pivotal factors including photosensitizer (PS), light, and oxygen in tumor tissue dramatically reduces the therapeutic efficacy of PDT. Nanoparticles have received an influx of attention as drug carriers, and recent studies have demonstrated their promising potential to overcome the obstacles of PDT in tumor tissue. Physicochemical optimization for passive targeting, ligand modification for active targeting, and stimuli-responsive release achieved efficient delivery of PS to tumor tissue. Various trials using upconversion NPs, two-photon lasers, X-rays, and bioluminescence have provided clues for efficient methods of light delivery to deep tissue. Attempts have been made to overcome unfavorable tumor microenvironments via artificial oxygen generation, Fenton reaction, and combination with other chemical drugs. In this review, we introduce these creative approaches to addressing the hurdles facing PDT in tumors. In particular, the studies that have been validated in animal experiments are preferred in this review over proof-of-concept studies that were only performed in cells.