Automatic and robust estimation of sex and chronological age from panoramic radiographs using a multi-task deep learning network: a study on a South Korean population.

Sex and chronological age estimation are crucial in forensic investigations and research on individual identification. Although manual methods for sex and age estimation have been proposed, these processes are labor-intensive, time-consuming, and error-prone. The purpose of this study was to estimate sex and chronological age from panoramic radiographs automatically and robustly using a multi-task deep learning network (ForensicNet). ForensicNet consists of a backbone and both sex and age attention branches to learn anatomical context features of sex and chronological age from panoramic radiographs and enables the multi-task estimation of sex and chronological age in an end-to-end manner. To mitigate bias in the data distribution, our dataset was built using 13,200 images with 100 images for each sex and age range of 15-80 years. The ForensicNet with EfficientNet-B3 exhibited superior estimation performance with mean absolute errors of 2.93 ± 2.61 years and a coefficient of determination of 0.957 for chronological age, and achieved accuracy, specificity, and sensitivity values of 0.992, 0.993, and 0.990, respectively, for sex prediction. The network demonstrated that the proposed sex and age attention branches with a convolutional block attention module significantly improved the estimation performance for both sex and chronological age from panoramic radiographs of elderly patients. Consequently, we expect that ForensicNet will contribute to the automatic and accurate estimation of both sex and chronological age from panoramic radiographs.

Blockchain-Based Caching Architecture for DApp Data Security and Delivery.

Decentralized applications (DApps) built on blockchain technology offer a promising solution to issues caused by centralization. However, traditional DApps leveraging off-chain storage face performance challenges due to factors such as storage location, network speed, and hardware conditions. For example, decentralized storage solutions such as IPFS suffer from diminished download performance due to I/O constraints influenced by data access patterns. Aiming to enhance the Quality of Service (QoS) in DApps built on blockchain technology, this paper proposes a blockchain node-based distributed caching architecture that guarantees real-time responsiveness for users. The proposed architecture ensures data integrity and user data ownership through blockchain while maintaining cache data consistency through local blockchain data. By implementing local cache clusters on blockchain nodes, our system achieves rapid response times. Additionally, attribute-based encryption is applied to stored content, enabling secure content sharing and access control, which prevents data leakage and unauthorized access in unreliable off-chain storage environments. Comparative analysis shows that our proposed system achieves a reduction in request processing latency of over 89% compared to existing off-chain solutions, maintaining cache data consistency and achieving response times within 65 ms. This demonstrates the model's effectiveness in providing secure and high-performance DApp solutions.

Novel Enhanced Mammalian Cell Transient Expression Vector via Promoter Combination.

During the emergence of infectious diseases, evaluating the efficacy of newly developed vaccines requires antigen proteins. Available methods enhance antigen protein productivity; however, structural modifications may occur. Therefore, we aimed to construct a novel transient overexpression vector capable of rapidly producing large quantities of antigenic proteins in mammalian cell lines. This involved expanding beyond the exclusive use of the human cytomegalovirus (CMV) promoter, and was achieved by incorporating a transcriptional enhancer (CMV enhancer), a translational enhancer (woodchuck hepatitis virus post-transcriptional regulatory element), and a promoter based on the CMV promoter. Twenty novel transient expression vectors were constructed, with the vector containing the human elongation factor 1-alpha (EF-1a) promoter showing the highest efficiency in expressing foreign proteins. This vector exhibited an approximately 27-fold higher expression of enhanced green fluorescent protein than the control vector containing only the CMV promoter. It also expressed the highest level of severe acute respiratory syndrome coronavirus 2 receptor-binding domain protein. These observations possibly result from the simultaneous enhancement of the transcriptional activity of the CMV promoter and the human EF-1a promoter by the CMV enhancer. Additionally, the synergistic effect between the CMV and human EF-1a promoters likely contributed to the further enhancement of protein expression.

Autophagic activation in porcine oocytes is independent of meiotic progression.

In this study, we built on our previous research that discovered that autophagy activated the metaphase I stage during porcine oocytes in vitro maturation. We investigated the relationship between autophagy and oocyte maturation. First, we confirmed whether autophagy was activated differently by different media (TCM199 and NCSU-23) during maturation. Then, we investigated whether oocyte maturation affected autophagic activation. In addition, we examined whether the inhibition of autophagy affected the nuclear maturation rate of porcine oocytes. As for the main experiment, we measured LC3-II levels using western blotting after inhibition of nuclear maturation via cAMP treatment in an in vitro culture to clarify whether nuclear maturation affected autophagy. After autophagy inhibition, we also counted matured oocytes by treating them with wortmannin or a E64d and pepstatin A mixture. Both groups, which had different treatment times of cAMP, showed the same levels of LC3-II, while the maturation rates were about four times higher after cAMP 22 h treatment than that of the 42 h treatment group. This indicated that neither cAMP nor nuclear status affected autophagy. Autophagy inhibition during in vitro oocyte maturation with wortmannin treatment reduced oocyte maturation rates by about half, while autophagy inhibition by the E64d and pepstatin A mixture treatment did not significantly affect the oocyte maturation. Therefore, wortmannin itself, or the autophagy induction step, but not the degradation step, is involved in the oocyte maturation of porcine oocytes. Overall, we propose that oocyte maturation does not stand upstream of autophagy activation, but autophagy may exist upstream of oocyte maturation.

A Federated Blockchain Architecture for File Storage with Improved Latency and Reliability in IoT DApp Services.

Blockchain technology can address data falsification, single point of failure (SPOF), and DDoS attacks on centralized services. By utilizing IoT devices as blockchain nodes, it is possible to solve the problem that it is difficult to ensure the integrity of data generated by using current IoT devices. However, as the amount of data generated by IoT devices increases, scalability issues are inevitable. As a result, large amounts of data are managed on external cloud storage or distributed file storage. However, this has the disadvantage of being outside the blockchain network. This makes it difficult to ensure reliability and causes high latency during data download and upload. To address these limitations, we propose a method for managing large amounts of data in the local storage node of a blockchain network with improved latency and reliability. Each blockchain network node stores data, which is synchronized and recovered based on reaching a consensus between smart contracts in a cluster network. The cluster network consists of a service leader node that serves as a gateway for services and a cluster node that stores service data in storage. The blockchain network stores synchronization and recovery metadata created in the cluster network. In addition, we showed that the performance of smart contract execution, network transmission, and metadata generation, which are elements of the proposed consensus process, is not significantly affected. In addition, we built a service leader node and a cluster node by implementing the proposed structure. We compared the performance (latency) of IoT devices when they utilized the proposed architecture and existing external distributed storage. Our results show improvements up to 4 and 10 times reduction in data upload (store) and download latency, respectively.

IoT-Chain and Monitoring-Chain Using Multilevel Blockchain for IoT Security.

In general, the Internet of Things (IoT) relies on centralized servers due to limited computing power and storage capacity. These server-based architectures have vulnerabilities such as DDoS attacks, single-point errors, and data forgery, and cannot guarantee stability and reliability. Blockchain technology can guarantee reliability and stability with a P2P network-based consensus algorithm and distributed ledger technology. However, it requires the high storage capacity of the existing blockchain and the computational power of the consensus algorithm. Therefore, blockchain nodes for IoT data management are maintained through an external cloud, an edge node. As a result, the vulnerability of the existing centralized structure cannot be guaranteed, and reliability cannot be guaranteed in the process of storing IoT data on the blockchain. In this paper, we propose a multi-level blockchain structure and consensus algorithm to solve the vulnerability. A multi-level blockchain operates on IoT devices, and there is an IoT chain layer that stores sensor data to ensure reliability. In addition, there is a hyperledger fabric-based monitoring chain layer that operates the access control for the metadata and data of the IoT chain to lighten the weight. We propose an export consensus method between the two blockchains, the Schnorr signature method, and a random-based lightweight consensus algorithm within the IoT-Chain. Experiments to measure the blockchain size, propagation time, consensus delay time, and transactions per second (TPS) were conducted using IoT. The blockchain did not exceed a certain size, and the delay time was reduced by 96% to 99% on average compared to the existing consensus algorithm. In the throughput tests, the maximum was 1701 TPS and the minimum was 1024 TPS.

Super.FELT: supervised feature extraction learning using triplet loss for drug response prediction with multi-omics data.

BACKGROUND: Predicting the drug response of a patient is important for precision oncology. In recent studies, multi-omics data have been used to improve the prediction accuracy of drug response. Although multi-omics data are good resources for drug response prediction, the large dimension of data tends to hinder performance improvement. In this study, we aimed to develop a new method, which can effectively reduce the large dimension of data, based on the supervised deep learning model for predicting drug response. RESULTS: We proposed a novel method called Supervised Feature Extraction Learning using Triplet loss (Super.FELT) for drug response prediction. Super.FELT consists of three stages, namely, feature selection, feature encoding using a supervised method, and binary classification of drug response (sensitive or resistant). We used multi-omics data including mutation, copy number aberration, and gene expression, and these were obtained from cell lines [Genomics of Drug Sensitivity in Cancer (GDSC), Cancer Cell Line Encyclopedia (CCLE), and Cancer Therapeutics Response Portal (CTRP)], patient-derived tumor xenografts (PDX), and The Cancer Genome Atlas (TCGA). GDSC was used for training and cross-validation tests, and CCLE, CTRP, PDX, and TCGA were used for external validation. We performed ablation studies for the three stages and verified that the use of multi-omics data guarantees better performance of drug response prediction. Our results verified that Super.FELT outperformed the other methods at external validation on PDX and TCGA and was good at cross-validation on GDSC and external validation on CCLE and CTRP. In addition, through our experiments, we confirmed that using multi-omics data is useful for external non-cell line data. CONCLUSION: By separating the three stages, Super.FELT achieved better performance than the other methods. Through our results, we found that it is important to train encoders and a classifier independently, especially for external test on PDX and TCGA. Moreover, although gene expression is the most powerful data on cell line data, multi-omics promises better performance for external validation on non-cell line data than gene expression data. Source codes of Super.FELT are available at  https://github.com/DMCB-GIST/Super.FELT .

LncRNA LINC00240 suppresses invasion and migration in non-small cell lung cancer by sponging miR-7-5p.

BACKGROUND: lncRNAs have important roles in regulating cancer biology. Accumulating evidence has established a link between the dysregulation of lncRNAs and microRNA in cancer progression. In previous studies, miR-7-5p has been found to be significantly down-regulated in mesenchymal-like lung cancer cell lines and directly regulated EGFR. In this work, we investigated the lncRNA partner of miR-7-5p in the progression of lung cancer. METHODS: We investigated the expression of miR-7-5p and the lncRNA after transfection with an miR-7-5p mimics using a microarray. The microarray results were validated using quantitative real time-polymerase Chain Reaction (qRT-PCR). The regulatory effects of lncRNA on miR-7-5p and its target were evaluated by changes in the expression of miR-7-5p after transfection with siRNAs for lncRNA and the synthesis of full-length lncRNA. The effect of miR-7-5p on lncRNA and the miRNA target was evaluated after transfection with miRNA mimic and inhibitor. The role of lncRNA in cancer progression was determined using invasion and migration assays. The level of lncRNA and EGFR in lung cancer and normal lung tissue was analyzed using TCGA data. RESULTS: We found that LINC00240 was downregulated in lung cancer cell line after miR-7-5p transfection with an miR-7-5p mimic. Further investigations revealed that the knockdown of LINC00240 induced the overexpression of miR-7-5p. The overexpression of miR-7-5p diminished cancer invasion and migration. The EGFR expression was down regulated after siRNA treatment for LINC00240. Silencing LINC00240 suppressed the invasion and migration of lung cancer cells, whereas LINC00240 overexpression exerted the opposite effect. The lower expression of LINC00240 in squamous lung cancer was analyzed using TCGA data. CONCLUSIONS: Taken together, LINC00240 acted as a sponge for miR-7-5p and induced the overexpression of EGFR. LINC00240 may represent a potential target for the treatment of lung cancer.

Non-viral, direct neuronal reprogramming from human fibroblast using a polymer-functionalized nanodot.

Among various strategies to treat neurodegenerative disorders, cell replacement therapies have drawn much attention recently. Such a trend led to the increase in demand for the rare and specialized cells, followed by the outburst development of various cell reprogramming strategies. However, several limitations on these conventional methods remain to be solved, including the genetic instability of the viral vectors and the high cytotoxicity or poor performance of the non-viral carriers. Therefore, non-viral methods need to be developed to ensure safe and efficient cell reprogramming. Here, we introduce a polymer-modified nano-reagent (Polymer-functionalized Nanodot, PolyN) for the safe and efficient, non-viral direct cell reprogramming. PolyN facilitated the highly efficient contemporary overexpression of the transgene compared to the conventional reagent. With our nano-reagent, we demonstrated the SOX2-mediated cell reprogramming and successfully generated the neuron-like cell from the human fibroblast.

Empowering Parents to Protect Children during COVID-19 with Message Strategy Based on Efficacy, Threat Levels, and Channel Preferences.

A national survey (n = 500) was administered in March 2020 at the peak of COVID-19uncertainty to access parents' perceived abilities to protect children during the COVID-19 pandemic. Using the threat/efficacy matrix in Witte's (1992) extended parallel processing model (EPPM), parents' behavioral intentions to protect children from coronavirus and their perceived COVID-19 knowledge levels are examined based on their positions within the matrix. The results indicated that (1) there is a positive relationship between the perceived threat of COVID-19 to parents and to their children; (2) parents' behavioral intentions and knowledge levels varied depending on their perceived threat/efficacy levels; (3) perceived threat level was the bigger motivator to use channels for COVID-19 information. Theoretical and practical implications are discussed.

Self-Efficacy and Other Considerations in Performance of Risk-Reducing Behaviors during a Major Disease Outbreak.

This study uses the construct of crisis self-efficacy to examine the importance of different considerations in individuals' decisions to perform recommended safeguarding behaviors during the 2016 Zika virus outbreak. A national survey in the U.S. (n = 370) was administered using a validated crisis self-efficacy scale in late summer 2016, amidst the global crisis. Relationships between crisis self-efficacy and preparedness were assessed. Other considerations such as risk, proximity, and time/money resources required were examined for their importance in decisions to follow safeguarding protocol. Crisis self-efficacy significantly predicted evaluations of preparedness but not of perceived Zika risk. Also, advisories issued from state and federal health agencies influenced individuals' perceptions of preparedness. Results indicate public health information officers should assess publics' levels of crisis self-efficacy in strategic message design. State and federal advisories were important to individual assessments of preparedness. Further, results show the threat needs to be presented to audiences in clear, specific terms.

Perceived Knowledge as [Protective] Power: Parents' Protective Efficacy, Information-Seeking, and Scrutiny during COVID-19.

During the COVID-19 pandemic, parents were issued numerous, sometimes changing, safeguarding directives including social distancing, mask use, hygiene, and stay-at-home orders. Enacting these behaviors for the parent presented challenges, but the responsibility for children to follow protocol properly was an even more daunting undertaking. Self-efficacy is one of the most power predictors of health behavior and has been adapted to a context-specific crisis self-efficacy scale conducted on March20, 2020, captures real-time perceptions of parents as coronavirus anxieties peaked. The study reveals a relationship between self- and protective efficacy that is mediated by parents' assessments of how informed they are about COVID-19. It also examines the role of perceived knowledge on information-seeking and scrutiny of pandemic information found online. Important directions for future research to develop the protective efficacy construct emerge as well as evidence of the rich applied and theoretical value of a deeper understanding of the perceived ability to perform recommended actions to protect another.

Prediction of Myoelectric Biomarkers in Post-Stroke Gait.

Electromyography (EMG) is sensitive to neuromuscular changes resulting from ischemic stroke and is considered a potential predictive tool of post-stroke gait and rehabilitation management. This study aimed to evaluate the potential myoelectric biomarkers for the classification of stroke-impaired muscular activity of the stroke patient group and the muscular activity of the control healthy adult group. We also proposed an EMG-based gait monitoring system consisting of a portable EMG device, cloud-based data processing, data analytics, and a health advisor service. This system was investigated with 48 stroke patients (mean age 70.6 years, 65% male) admitted into the emergency unit of a hospital and 75 healthy elderly volunteers (mean age 76.3 years, 32% male). EMG was recorded during walking using the portable device at two muscle positions: the bicep femoris muscle and the lateral gastrocnemius muscle of both lower limbs. The statistical result showed that the mean power frequency (MNF), median power frequency (MDF), peak power frequency (PKF), and mean power (MNP) of the stroke group differed significantly from those of the healthy control group. In the machine learning analysis, the neural network model showed the highest classification performance (precision: 88%, specificity: 89%, accuracy: 80%) using the training dataset and highest classification performance (precision: 72%, specificity: 74%, accuracy: 65%) using the testing dataset. This study will be helpful to understand stroke-impaired gait changes and decide post-stroke rehabilitation.

Driving-Induced Neurological Biomarkers in an Advanced Driver-Assistance System.

Physiological signals are immediate and sensitive to neurological changes resulting from the mental workload induced by various driving environments and are considered a quantifying tool for understanding the association between neurological outcomes and driving cognitive workloads. Neurological assessment, outside of a highly-equipped clinical setting, requires an ambulatory electroencephalography (EEG) headset. This study aimed to quantify neurological biomarkers during a resting state and two different scenarios of driving states in a virtual driving environment. We investigated the neurological responses of seventeen healthy male drivers. EEG data were measured in an initial resting state, city-roadways driving state, and expressway driving state using a portable EEG headset in a driving simulator. During the experiment, the participants drove while experiencing cognitive workloads due to various driving environments, such as road traffic conditions, lane changes of surrounding vehicles, the speed limit, etc. The power of the beta and gamma bands decreased, and the power of the delta waves, theta, and frontal theta asymmetry increased in the driving state relative to the resting state. Delta-alpha ratio (DAR) and delta-theta ratio (DTR) showed a strong correlation with a resting state, city-roadways driving state, and expressway driving state. Binary machine-learning (ML) classification models showed a near-perfect accuracy between the resting state and driving state. Moderate classification performances were observed between the resting state, city-roadways state, and expressway state in multi-class classification. An EEG-based neurological state prediction approach may be utilized in an advanced driver-assistance system (ADAS).

Deep Learning-Based Stroke Disease Prediction System Using Real-Time Bio Signals.

The emergence of an aging society is inevitable due to the continued increases in life expectancy and decreases in birth rate. These social changes require new smart healthcare services for use in daily life, and COVID-19 has also led to a contactless trend necessitating more non-face-to-face health services. Due to the improvements that have been achieved in healthcare technologies, an increasing number of studies have attempted to predict and analyze certain diseases in advance. Research on stroke diseases is actively underway, particularly with the aging population. Stroke, which is fatal to the elderly, is a disease that requires continuous medical observation and monitoring, as its recurrence rate and mortality rate are very high. Most studies examining stroke disease to date have used MRI or CT images for simple classification. This clinical approach (imaging) is expensive and time-consuming while requiring bulky equipment. Recently, there has been increasing interest in using non-invasive measurable EEGs to compensate for these shortcomings. However, the prediction algorithms and processing procedures are both time-consuming because the raw data needs to be separated before the specific attributes can be obtained. Therefore, in this paper, we propose a new methodology that allows for the immediate application of deep learning models on raw EEG data without using the frequency properties of EEG. This proposed deep learning-based stroke disease prediction model was developed and trained with data collected from real-time EEG sensors. We implemented and compared different deep-learning models (LSTM, Bidirectional LSTM, CNN-LSTM, and CNN-Bidirectional LSTM) that are specialized in time series data classification and prediction. The experimental results confirmed that the raw EEG data, when wielded by the CNN-bidirectional LSTM model, can predict stroke with 94.0% accuracy with low FPR (6.0%) and FNR (5.7%), thus showing high confidence in our system. These experimental results demonstrate the feasibility of non-invasive methods that can easily measure brain waves alone to predict and monitor stroke diseases in real time during daily life. These findings are expected to lead to significant improvements for early stroke detection with reduced cost and discomfort compared to other measuring techniques.

Efficacy of a povidone-iodine foam dressing (Betafoam) on diabetic foot ulcer.

This study aimed to assess the efficacy of a new povidone-iodine (PVP-I) foam dressing (Betafoam) vs foam dressing (Medifoam) for the management of diabetic foot ulcers. This study was conducted between March 2016 and September 2017 at 10 sites in Korea. A total of 71 patients (aged ≥19 years) with type 1/2 diabetes and early-phase diabetic foot ulcers (Wagener classification grade 1/2) were randomised to treatment with PVP-I foam dressing or foam dressing for 8 weeks. Wound healing, wound infection, patient satisfaction, and adverse events (AEs) were assessed. The PVP-I foam and foam dressing groups were comparable in the proportion of patients with complete wound healing within 8 weeks (44.4% vs 42.3%, P = .9191), mean (±SD) number of days to complete healing (31.00 ± 15.07 vs 33.27 ± 12.60 days; P = .6541), and infection rates (11.1% vs 11.4%; P = 1.0000). Median satisfaction score (scored from 0 to 10) at the final visit was also comparable between groups (10 vs 9, P = .2889). There was no significant difference in AE incidence (27.8% vs 17.1%, P = .2836), and none of the reported AEs had a causal relationship with the dressings. The results of this study suggest that PVP-I foam dressing has wound-healing efficacy comparable with foam dressing, with no notable safety concerns. This study was funded by Mundipharma Korea Ltd and registered at ClinicalTrials.gov (identifier NCT02732886).

Rotational and Varus Instability in Chronic Lateral Ankle Instability: In Vivo 3D Biomechanical Analysis.

We retrospectively evaluated the altered biomechanics of the talus in 15 adult patients (7 males, 8 females) with chronic lateral ankle instability when the ankle joint moved actively from full dorsiflexion to full plantarflexion under a non-weight bearing condition. CT images were taken for the unstable ankle and the contralateral normal (control) ankle. Three-dimensional surface models of both ankle joints were reconstructed from the CT data, and we used a computer simulation program to compare both ankle motions of inversion/eversion in the coronal plane, plantarflexion/dorsiflexion in the sagittal plane, and internal rotation/external rotation in the axial plane. This evaluation method provides in vivo, dynamic, and 3D results of ankle motion. In the ankles with chronic lateral instability and the controls, the average talar rotational movement of inversion (+)/eversion (-) was 19.0° and 15.5° and the internal rotation (+)/external rotation (-) was 30.4° and 20.7°, respectively. Paired t-tests revealed significant differences in the amount of inversion (+)/eversion (-) (p=0.012) and internal rotation (+)/external rotation (-) (p<0.001) between unstable and normal ankle joints. The difference of mean rotational movement in internal rotation (9.7°) was greater than that of inversion (3.5°). Rotational instability should be considered when evaluating chronic lateral ankle instability.

Three-dimensional scapular dyskinesis in hook-plated acromioclavicular dislocation including hook motion.

BACKGROUND: The purpose of this study is to analyze the 3-dimensional scapular dyskinesis and the kinematics of a hook plate relative to the acromion after hook-plated acromioclavicular dislocation in vivo. Reported complications of acromioclavicular reduction using a hook plate include subacromial erosion and impingement. However, there are few reports of the 3-dimensional kinematics of the hook and scapula after the aforementioned surgical procedure. METHODS: We studied 15 cases of acromioclavicular dislocation treated with a hook plate and 15 contralateral normal shoulders using computed tomography in the neutral and full forward flexion positions. Three-dimensional motion of the scapula relative to the thorax during arm elevation was analyzed using a computer simulation program. We also measured the distance from the tip of the hook plate to the greater tuberosity, as well as the angular motion of the plate tip in the subacromial space. RESULTS: Decreased posterior tilting (22° ± 10° vs 31° ± 8°) in the sagittal plane and increased external rotation (19° ± 9° vs 7° ± 5°) in the axial plane were evident in the affected shoulders. The mean values of translation of the hook plate and angular motion against the acromion were 4.0 ± 1.6 mm and 15° ± 8°, respectively. The minimum value of the distance from the hook plate to the humeral head tuberosity was 6.9 mm during arm elevation. CONCLUSIONS: Acromioclavicular reduction using a hook plate may cause scapular dyskinesis. Translational and angular motion of the hook plate against the acromion could lead to subacromial erosion. However, the hook does not seem to impinge directly on the humeral head.

In Vivo Analysis of Three-Dimensional Dynamic Scapular Dyskinesis in Scapular or Clavicular Fractures.

The three-dimensional (3D) kinematics of the scapula were analyzed in vivo in 10 patients with scapular and 10 patients with clavicular fracture. Both the injured shoulder and normal contralateral shoulder were evaluated by computed tomography in the neutral and fully elevated positions. 3D rotational and translational movements of the scapula relative to the thorax during arm elevation were analyzed. A computer simulation program was used to compare rotational elevation/depression in the coronal plane, anterior/posterior tilting in the sagittal plane and protraction/retraction in the axial plane between the normal and affected sides. Anterior/ posterior translational movement along the X-axis, upward/downward movement along the Y-axis, and lateral/ medial movement along the Z-axis in the Euler space during forward elevation were also compared. In scapular fracture, rotational elevation of the scapula decreased in the coronal plane and posterior tilting of the scapula increased in the sagittal plane. Anterior and superior translation were higher in scapular fracture than in the corresponding normal sides. However, no significant abnormal rotational and translational kinematic changes were observed during elevation in clavicular fracture. In vivo 3D computerized motion analysis was useful for evaluating scapular dyskinesis. Scapular fracture can cause scapular dyskinesis, but not all clavicular fractures alter scapular motion biomechanics.

miR-34a inhibits differentiation of human adipose tissue-derived stem cells by regulating cell cycle and senescence induction.

MicroRNAs (miRNAs) are critical in the maintenance, differentiation, and lineage commitment of stem cells. Stem cells have the unique property to differentiate into tissue-specific cell types (lineage commitment) during cell division (self-renewal). In this study, we investigated whether miR-34a, a cell cycle-regulating microRNA, could control the stem cell properties of adipose tissue-derived stem cells (ADSCs). First, we found that the expression level of miR-34a was increased as the cell passage number was increased. This finding, however, was inversely correlated with our finding that the overexpression of miR-34a induced the decrease of cell proliferation. In addition, miR-34a overexpression decreased the expression of various cell cycle regulators such as CDKs (-2, -4, -6) and cyclins (-E, -D), but not p21 and p53. The cell cycle analysis showed accumulation of dividing cells at S phase by miR-34a, which was reversible by co-treatment with anti-miR-34a. The potential of adipogenesis and osteogenesis of ADSCs was also decreased by miR-34a overexpression, which was recovered by co-treatment with anti-miR-34a. The surface expression of stem cell markers including CD44 was also down-regulated by miR-34a overexpression as similar to that elicited by cell cycle inhibitors. miR-34a also caused a significant decrease in mRNA expression of stem cell transcription factors as well as STAT-3 expression and phosphorylation. Cytokine profiling revealed that miR-34a significantly modulated IL-6 and -8 production, which was strongly related to cellular senescence. These data suggest the importance of miR-34a for the fate of ADSCs toward senescence rather than differentiation.