A 3D and Explainable Artificial Intelligence Model for Evaluation of Chronic Otitis Media Based on Temporal Bone Computed Tomography: Model Development, Validation, and Clinical Application.

BACKGROUND: Temporal bone computed tomography (CT) helps diagnose chronic otitis media (COM). However, its interpretation requires training and expertise. Artificial intelligence (AI) can help clinicians evaluate COM through CT scans, but existing models lack transparency and may not fully leverage multidimensional diagnostic information. OBJECTIVE: We aimed to develop an explainable AI system based on 3D convolutional neural networks (CNNs) for automatic CT-based evaluation of COM. METHODS: Temporal bone CT scans were retrospectively obtained from patients operated for COM between December 2015 and July 2021 at 2 independent institutes. A region of interest encompassing the middle ear was automatically segmented, and 3D CNNs were subsequently trained to identify pathological ears and cholesteatoma. An ablation study was performed to refine model architecture. Benchmark tests were conducted against a baseline 2D model and 7 clinical experts. Model performance was measured through cross-validation and external validation. Heat maps, generated using Gradient-Weighted Class Activation Mapping, were used to highlight critical decision-making regions. Finally, the AI system was assessed with a prospective cohort to aid clinicians in preoperative COM assessment. RESULTS: Internal and external data sets contained 1661 and 108 patients (3153 and 211 eligible ears), respectively. The 3D model exhibited decent performance with mean areas under the receiver operating characteristic curves of 0.96 (SD 0.01) and 0.93 (SD 0.01), and mean accuracies of 0.878 (SD 0.017) and 0.843 (SD 0.015), respectively, for detecting pathological ears on the 2 data sets. Similar outcomes were observed for cholesteatoma identification (mean area under the receiver operating characteristic curve 0.85, SD 0.03 and 0.83, SD 0.05; mean accuracies 0.783, SD 0.04 and 0.813, SD 0.033, respectively). The proposed 3D model achieved a commendable balance between performance and network size relative to alternative models. It significantly outperformed the 2D approach in detecting COM (P≤.05) and exhibited a substantial gain in identifying cholesteatoma (P<.001). The model also demonstrated superior diagnostic capabilities over resident fellows and the attending otologist (P<.05), rivaling all senior clinicians in both tasks. The generated heat maps properly highlighted the middle ear and mastoid regions, aligning with human knowledge in interpreting temporal bone CT. The resulting AI system achieved an accuracy of 81.8% in generating preoperative diagnoses for 121 patients and contributed to clinical decision-making in 90.1% cases. CONCLUSIONS: We present a 3D CNN model trained to detect pathological changes and identify cholesteatoma via temporal bone CT scans. In both tasks, this model significantly outperforms the baseline 2D approach, achieving levels comparable with or surpassing those of human experts. The model also exhibits decent generalizability and enhanced comprehensibility. This AI system facilitates automatic COM assessment and shows promising viability in real-world clinical settings. These findings underscore AI's potential as a valuable aid for clinicians in COM evaluation. TRIAL REGISTRATION: Chinese Clinical Trial Registry ChiCTR2000036300; https://www.chictr.org.cn/showprojEN.html?proj=58685.

Synthesis of 3-sulfonylisoindolin-1-ones from olefinic amides and sodium sulfinates via electrooxidative tandem cyclization.

Sulfonyl groups are motifs that are widely found in biologically active compounds and drug molecules, many isolated natural products as well as pharmaceuticals contain sulfonyl groups. Herein, we present the synthesis of sulfonyl-substituted isoindolones by a electrochemical oxidative radical cascade cycloaddition reaction of olefinic amides with sodium sulfite under oxidant- and catalyst-free conditions. Various olefinic amides and sodium sulfinates were compatible and gave the desired products in yields up to 99%.

High-density, highly sensitive sensor array of spiky carbon nanospheres for strain field mapping.

While accurate mapping of strain distribution is crucial for assessing stress concentration and estimating fatigue life in engineering applications, conventional strain sensor arrays face a great challenge in balancing sensitivity and sensing density for effective strain mapping. In this study, we present a Fowler-Nordheim tunneling effect of monodispersed spiky carbon nanosphere array on polydimethylsiloxane as strain sensor arrays to achieve a sensitivity up to 70,000, a sensing density of 100 pixel cm-2, and logarithmic linearity over 99% within a wide strain range of 0% to 60%. The highly ordered assembly of spiky carbon nanospheres in each unit also ensures high inter-unit consistency (standard deviation ≤3.82%). Furthermore, this sensor array can conformally cover diverse surfaces, enabling accurate acquisition of strain distributions. The sensing array offers a convenient approach for mapping strain fields in various applications such as flexible electronics, soft robotics, biomechanics, and structure health monitoring.

Understanding asymmetric switching times in accumulation mode organic electrochemical transistors.

Understanding the factors underpinning device switching times is crucial for the implementation of organic electrochemical transistors in neuromorphic computing, bioelectronics and real-time sensing applications. Existing models of device operation cannot explain the experimental observations that turn-off times are generally much faster than turn-on times in accumulation mode organic electrochemical transistors. Here, using operando optical microscopy, we image the local doping level of the transistor channel and show that turn-on occurs in two stages-propagation of a doping front, followed by uniform doping-while turn-off occurs in one stage. We attribute the faster turn-off to a combination of engineering as well as physical and chemical factors including channel geometry, differences in doping and dedoping kinetics and the phenomena of carrier-density-dependent mobility. We show that ion transport limits the operation speed in our devices. Our study provides insights into the kinetics of organic electrochemical transistors and guidelines for engineering faster organic electrochemical transistors.

Human periodontal ligament stem cell sheets activated by graphene oxide quantum dots repair periodontal bone defects by promoting mitochondrial dynamics dependent osteogenic differentiation.

BACKGROUND: Bone defects in the maxillofacial region restrict the integrity of dental function, posing challenges in clinical treatment. Bone tissue engineering (BTE) with stem cell implants is an effective method. Nanobiomaterials can effectively enhance the resistance of implanted stem cells to the harsh microenvironment of bone defect areas by promoting cell differentiation. Graphene oxide quantum dots (GOQDs) are zero-dimensional nanoscale derivatives of graphene oxide with excellent biological activity. In the present study, we aimed to explore the effects of GOQDs prepared by two methods (Y-GOQDs and B-GOQDs) on the osteogenic differentiation of human periodontal ligament stem cells (hPDLSCs), as well as the effect of gelatin methacryloyl (GelMA)-encapsulated GOQD-induced hPDLSC sheets on the repair of mandibular periodontal defects in rats. We also explored the molecular biological mechanism through which GOQD promotes bone differentiation. RESULTS: There were significant differences in oxygen-containing functional groups, particle size and morphology between Y-GOQDs and B-GOQDs. Y-GOQDs promoted the osteogenic differentiation of hPDLSCs more effectively than did B-GOQDs. In addition, GelMA hydrogel-encapsulated Y-GOQD-induced hPDLSC cell sheet fragments not only exhibited good growth and osteogenic differentiation in vitro but also promoted the repair of mandibular periodontal bone defects in vivo. Furthermore, the greater effectiveness of Y-GOQDs than B-GOQDs in promoting osteogenic differentiation is due to the regulation of hPDLSC mitochondrial dynamics, namely, the promotion of fusion and inhibition of fission. CONCLUSIONS: Overall, Y-GOQDs are more effective than B-GOQDs at promoting the osteogenic differentiation of hPDLSCs by regulating mitochondrial dynamics, which ultimately contributes to bone regeneration via the aid of the GelMA hydrogels in vivo.

Chain Length Dependence of Electron Transport in an n-Type Conjugated Polymer with a Rigid-Rod Chain Topology.

Most currently known n-type conjugated polymers have a semiflexible chain topology, and their charge carrier mobilities are known to peak at modest chain lengths of below 40-60 repeat units. Herein, we show that the field-effect electron mobility of a model n-type conjugated polymer that has a rigid-rod chain topology grows continuously without saturation, even at a chain length exceeding 250 repeat units. We found the mechanism underlying the novel chain length-dependent electron transport to originate from the reduced structural disorder and energetic disorder with the increasing degree of polymerization inherent to the rigid-rod chain topology. Furthermore, we demonstrate a unique chain length-dependent decay of threshold voltage, which is rationalized by decreased trap densities and trap depths with respect to the degree of polymerization. Our findings provide new insights into the role of polymer chain topology in electron transport and demonstrate the promise of rigid-rod chain architectures for the design of future high-mobility conjugated polymers.

The ATF4-regulated LncRNA MALAT1 promotes odontoblastic differentiation of human dental pulp stem cells via histone demethylase JMJD3: An in vitro study.

AIM: This study aimed to investigate the upstream regulators and specific mechanisms of metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) in the odontoblastic differentiation of human dental pulp stem cells (hDPSCs). METHODOLOGY: Human dental pulp stem cells were isolated and cultured, followed by conducting loss- or gain-of-function experiments on ATF4 and loss experiments on MALAT1 to elucidate their respective biological functions in odontoblastic differentiation. Chromatin immunoprecipitation assays and RNA immunoprecipitation were performed to uncover the interaction between ATF4-MALAT1 and MALAT1-JMJD3, respectively. The odontoblastic differentiation was estimated by the mRNA and protein of DSPP and DMP1, as well as alkaline phosphatase staining. RESULTS: Expression of MALAT1 was upregulated in the hDPSCs cultured in an odontoblastic medium, and MALAT1 downregulation suppressed the odontoblastic differentiation of the hDPSCs. Subsequent experiments confirmed that ATF4 promoted odontoblastic differentiation and induced MALAT1 expression by binding to the MALAT1 promoter region. Further experiments revealed that nuclear MALAT1 interacted with JMJD3. MALAT1 knockdown decreased the JMJD3 protein level and demethylase activity, and it enhanced H3K27me3 occupancy of the promoter region of DSPP and DMP1, resulting in the inhibition of DSPP and DMP1 transcription. Importantly, JMJD3 overexpression significantly attenuated the inhibition of odontoblastic differentiation induced by MALAT1 knockdown. CONCLUSIONS: ATF4-regulated MALAT1 plays a positive regulatory role in odontoblastic differentiation of hDPSCs through JMJD3-mediated H3K27me3 modifications of the DSPP and DMP1 promoters.

The complete chloroplast genome of Geum longifolium (Maxim.) Smedmark 2006 (Rosaceae: Colurieae) and its phylogenomic implications.

Geum longifolium (Maxim.) Smedmark 2006 belongs to the family Rosaceae, subfamily Rosoideae, tribe Colurieae. Geum longifolium is endemic to China and its whole herb is used in Chinese medicine. Here, the first complete chloroplast (cp) genome of G. longifolium was assembled and annotated based on genome skimming, and its phylogenetic position was investigated using phylogenomic evidence. The cp genome size of G. longifolium was 155,884 bp with the total GC content of 36.7%. Its cp genome presented a typical tetrad structure, composed of a large single copy (LSC) region (85,338 bp), a small single copy (SSC) region (18,358 bp), and a pair of inverted repeat (IR) regions (26,094 bp). The cp genome encoded 129 genes, including 84 protein-coding genes, 37 tRNA genes, and eight rRNA genes. Phylogenetic analysis indicated that G. longifolium was sister to G. elatum Wall. ex G.Don 1832 in current taxa sampling. This study can enrich the chloroplast genomic resource of Geum and lay the foundation for future phylogenetic studies on Geum.

Detecting Bell Correlations in Multipartite Non-Gaussian Spin States.

We expand the toolbox for studying Bell correlations in multipartite systems by introducing permutationally invariant Bell inequalities (PIBIs) involving few-body correlators. First, we present around twenty families of PIBIs with up to three- or four-body correlators, that are valid for an arbitrary number of particles. Compared to known inequalities, these show higher noise robustness, or the capability to detect Bell correlations in highly non-Gaussian spin states. We then focus on finding PIBIs that are of practical experimental implementation, in the sense that the associated operators require collective spin measurements along only a few directions. To this end, we formulate this search problem as a semidefinite program that embeds the constraints required to look for PIBIs of the desired form.

Hydration of a Side-Chain-Free n-Type Semiconducting Ladder Polymer Driven by Electrochemical Doping.

We study the organic electrochemical transistor (OECT) performance of the ladder polymer poly(benzimidazobenzophenanthroline) (BBL) in an attempt to better understand how an apparently hydrophobic side-chain-free polymer is able to operate as an OECT with favorable redox kinetics in an aqueous environment. We examine two BBLs of different molecular masses from different sources. Regardless of molecular mass, both BBLs show significant film swelling during the initial reduction step. By combining electrochemical quartz crystal microbalance gravimetry, in-operando atomic force microscopy, and both ex-situ and in-operando grazing incidence wide-angle X-ray scattering (GIWAXS), we provide a detailed structural picture of the electrochemical charge injection process in BBL in the absence of any hydrophilic side-chains. Compared with ex-situ measurements, in-operando GIWAXS shows both more swelling upon electrochemical doping than has previously been recognized and less contraction upon dedoping. The data show that BBL films undergo an irreversible hydration driven by the initial electrochemical doping cycle with significant water retention and lamellar expansion that persists across subsequent oxidation/reduction cycles. This swelling creates a hydrophilic environment that facilitates the subsequent fast hydrated ion transport in the absence of the hydrophilic side-chains used in many other polymer systems. Due to its rigid ladder backbone and absence of hydrophilic side-chains, the primary BBL water uptake does not significantly degrade the crystalline order, and the original dehydrated, unswelled state can be recovered after drying. The combination of doping induced hydrophilicity and robust crystalline order leads to efficient ionic transport and good stability.

Osteoblast Jmjd3 regulates osteoclastogenesis via EphB4 and RANKL signalling.

BACKGROUND: Osteoblasts suppress osteoclastogenesis during the reversal phase of bone remodelling and the mechanism needs to be further investigated. Here, we investigated the role of histone demethylase Jumonji domain-containing 3 (Jmjd3) in osteoblasts on regulating osteoclastogenesis. METHODS: Jmjd3 expression was silenced in osteoblasts. Osteoblasts and osteoclasts were co-cultured in direct or indirect contact ways, and osteoclastogenesis was determined by tartrate-resistant acid phosphatase (TRAP) staining and Western blotting. Additionally, Ephrin receptor B4 (EphB4) and receptor activator of nuclear factor-kappa Β ligand (RANKL) expression were quantified in osteoblasts via real-time PCR, Western blotting, and enzyme-linked immunosorbent assay. Subsequently, EphB4 was overexpressed in osteoblasts and RANKL expression and osteoclastogenesis was quantified. RESULTS: Osteoclastogenesis and marker protein expression levels was promoted when osteoclasts were co-cultured with Jmjd3-silenced osteoblasts. Silencing of Jmjd3 expression in osteoblasts decreased EphB4 expression, owing to suppression of demethylation of H3K27me3 on the promoter region of EphB4. Whereas RANKL expression was upregulated in Jmjd3-silenced osteoblasts. Overexpression of EphB4 in osteoblasts inhibited osteoclastogenesis and RANKL expression. CONCLUSION: Jmjd3 in osteoblasts is a crucial regulator of osteoblast-to-osteoclast communication through EphB4-EphrinB2, RANKL-RANK and EphB4-RANKL signalling axes, suggesting the pivotal role of Jmjd3 in bone remodelling process in bone destruction disease such as chronic apical periodontitis.

Flexible Electronic Skin for Monitoring of Grasping State During Robotic Manipulation.

Electronic skin for robotic tactile sensing has been studied extensively over the past years, yet practical applications of electronic skin for the grasping state monitoring during robotic manipulation are still limited. In this study, we present the fabrication and implementation of electronic skin sensor arrays for the detection of unstable grasping. The piezoresistive sensor arrays have the advantages of facile fabrication, fast response, and high reliability. With the tactile data from the sensor array, we propose two quantitative indicators, correlation coefficient and wavelet coefficient, to identify grasping with variable forces and slippage. Those two indicators reflect both time and frequency domain characteristics in the contact forces from the sensor array and can be obtained without large amount of calculation. We demonstrate the utility of this method under various conditions, the results indicate grasping with variable forces, and slippage can be distinguished by this method. The flexible sensor arrays are adopted for tactile sensing on a bionic hand, and the effectiveness of this method in detecting various grasping states has been verified. The electronic skin sensor array and the grasping state monitoring method are promising for applications in robotic dexterous manipulation.

Deep learning in automatic detection of dysphonia: Comparing acoustic features and developing a generalizable framework.

BACKGROUND: Auditory-perceptual assessment of voice is a subjective procedure. Artificial intelligence with deep learning (DL) may improve the consistency and accessibility of this task. It is unclear how a DL model performs on different acoustic features. AIMS: To develop a generalizable DL framework for identifying dysphonia using a multidimensional acoustic feature. METHODS & PROCEDURES: Recordings of sustained phonations of /a/ and /i/ were retrospectively collected from a clinical database. Subjects contained 238 dysphonic and 223 vocally healthy speakers of Chinese Mandarin. All audio clips were split into multiple 1.5-s segments and normalized to the same loudness level. Mel frequency cepstral coefficients and mel-spectrogram were extracted from these standardized segments. Each set of features was used in a convolutional neural network (CNN) to perform a binary classification task. The best feature was obtained through a five-fold cross-validation on a random selection of 80% data. The resultant DL framework was tested on the remaining 20% data and a public German voice database. The performance of the DL framework was compared with those of two baseline machine-learning models. OUTCOMES & RESULTS: The mel-spectrogram yielded the best model performance, with a mean area under the receiver operating characteristic curve of 0.972 and an accuracy of 92% in classifying audio segments. The resultant DL framework significantly outperformed both baseline models in detecting dysphonic subjects on both test sets. The best outcomes were achieved when classifications were made based on all segments of both vowels, with 95% accuracy, 92% recall, 98% precision and 98% specificity on the Chinese test set, and 92%, 95%, 90% and 89%, respectively, on the German set. CONCLUSIONS & IMPLICATIONS: This study demonstrates the feasibility of DL for automatic detection of dysphonia. The mel-spectrogram is a preferred acoustic feature for the task. This framework may be used for vocal health screening and facilitate automatic perceptual evaluation of voice in the era of big data. WHAT THIS PAPER ADDS: What is already known on this subject Auditory-perceptual assessment is the current gold standard in clinical evaluation of voice quality, but its value may be limited by the rater's reliability and accessibility. DL is a new method of artificial intelligence that can overcome these disadvantages and promote automatic voice assessment. This study explored the feasibility of a DL approach for automatic detection of dysphonia, along with a quantitative comparison of two common sets of acoustic features. What this study adds to existing knowledge A CNN model is excellent at decoding multidimensional acoustic features, outperforming the baseline parameter-based models in identifying dysphonic voices. The first 13 mel-frequency cepstral coefficients (MFCCs) are sufficient for this task. The mel-spectrogram results in greater performance, indicating the acoustic features are presented in a more favourable way than the MFCCs to the CNN model. What are the potential or actual clinical implications of this work? DL is a feasible method for the detection of dysphonia. The current DL framework may be used for remote vocal health screening or documenting voice recovery after treatment. In future, DL models may potentially be used to perform auditory-perceptual tasks in an automatic, efficient, reliable and low-cost manner.

Flexible Capacitive Sensing and Ultrasound Calibration for Skeletal Muscle Deformations.

Skeletal muscles are critical to human-limb motion dynamics and energetics, where their mechanical states are seldom explored in vitro due to practical limitations of sensing technologies. This article aims to capture mechanical deformations of muscle contraction using wearable flexible sensors, which is justified with model calibration and experimental validation. The capacitive sensor is designed with the composite of conductive fabric electrodes and the porous dielectric layer to increase the pressure sensitivity and prevent lateral expansions. In this way, the compressive displacement of muscle deformation is captured in the muscle-sensor coupling model in terms of sensor deformation and parameters of pretension, material, and shape properties. The sensing model is calibrated in a linear form using ultrasound medical imaging. The sensor is capable of measuring muscle strain of 70% with an error of <3.6% and temperature disturbance of <5.6%. After 10K cycles of compression, the drift is only 3.3%. Immediate application of the proposed method is illustrated by gait pattern identification, where the K-nearest neighbor prediction accuracy of squats, level walking, stair ascent/descent, and ramp ascent is over 97% with a standard deviation below 2.6% compared to that of 94.61 ± 4.24% for ramp descent, and the response time is 14.37 ± 0.52 ms. The wearable sensing method is valid for muscle deformation monitoring and gait pattern identification, and it provides an alternative approach to capture mechanical motions of muscles, which is anticipated to contribute to understand locomotion biomechanics in terms of muscle forces and metabolic landscapes.

DNA-catalysed alternative RNA splicing.

We report DNA-catalysed alternative RNA splicing in vitro. Using modular DNA catalysts with RNA endonuclease and RNA ligase activities, we show that DNA can modulate RNA structure and activity. Furthermore, we illustrate that such DNA-catalysed reactions can yield, from a common precursor, different splicing isoforms with distinct functions.

Vestigial-Like 3 Plays an Important Role in Osteoblast Differentiation by Regulating the Expression of Osteogenic Transcription Factors and BMP Signaling.

Our previous gene profiling analysis showed that the transcription cofactor vestigial-like 3 (VGLL3) gene expression was upregulated by mechanical tension in the mouse cranial suture, coinciding with accelerated osteoblast differentiation. Therefore, we hypothesized that VGLL3 plays a significant role in osteogenic differentiation. To clarify the function of VGLL3 in osteoblasts, we examined its expression characteristics in mouse bone tissue and the osteoblastic cell line MC3T3-E1. We further examined the effects of Vgll3 knockdown on osteoblast differentiation and bone morphogenetic protein (BMP) signaling. In the mouse cranial suture, where membranous ossification occurs, VGLL3 was immunohistochemically detected mostly in the nucleus of osteoblasts, preosteoblasts, and fibroblastic cells. VGLL3 expression in MC3T3-E1 cells was transient and peaked at a relatively early stage of differentiation. RNA sequencing revealed that downregulated genes in Vgll3-knockdown cells were enriched in gene ontology terms associated with osteoblast differentiation. Interestingly, most of the upregulated genes were related to cell division. Targeted Vgll3 knockdown markedly suppressed the expression of major osteogenic transcription factors (Runx2, Sp7/osterix, and Dlx5) and osteoblast differentiation. It also attenuated BMP signaling; moreover, exogenous BMP2 partially restore osteogenic transcription factors' expression in Vgll3-knockdown cells. Furthermore, overexpression of Vgll3 increased the expression of osteogenic transcription factors. These results suggest that VGLL3 plays a critical role in promoting osteoblast differentiation and that part of the process is mediated by BMP signaling. Further elucidation of VGLL3 function will increase our understanding of osteogenesis and skeletal disease etiology.

Development of Novel Aptamer-Based Targeted Chemotherapy for Bladder Cancer.

Bladder cancer is common worldwide, with most patients presenting with nonmuscle invasive disease. Multiple intravesical recurrences lead to reduced quality of life and high costs for patients with this form of bladder cancer. Intravesical chemotherapy aimed at reducing recurrence is the standard-of-care but has significant side effects from nonspecific cytotoxicity to normal urothelium. Importantly, toxicity limits doses that can be administered. Thus, tumor-specific drug targeting could reduce toxicity and enhance effectiveness by allowing higher doses. Here, using cell internalization systematic evolution of ligands by exponential enrichment (SELEX), we identify a novel bladder cancer-specific, chemically modified nucleic acid aptamer that can be preferentially internalized into tumor cells but not normal urothelial cells. The 35-nucleotide B1 aptamer is internalized into bladder cancer cells through clathrin-mediated endocytosis and macropinocytosis. As proof of principle, a B1-guided DNA nanotrain delivery vehicle for epirubicin was constructed as a targeted intravesical chemotherapy. The B1-nanotrain-epirubicin construct exhibited selective cytotoxicity towards bladder cancer cells and outperformed epirubicin in murine orthotopic xenograft models of human bladder cancer. This aptamer-based delivery system makes targeted chemotherapy possible for bladder cancer, providing a compelling rationale for clinical development. SIGNIFICANCE: These findings identify a bladder cancer-specific aptamer that can be used for targeted delivery of chemotherapy, potentially reducing toxicity and enhancing therapeutic efficacy.

Monitoring the delicate operations of surgical robots via ultra-sensitive ionic electronic skin.

The arrival of surgical robots in high-end medical equipment is a landmark, and the realization of tactile sensation a major challenge in this important cutting-edge research field. Aiming to address this issue, we present ultra-sensitive ionic electronic skin in the form of flexible capacitive pressure sensors, which incorporate multistage bionic microstructures in ion gels for the purpose of monitoring the delicate operations of surgical robots. Significantly, the ionic skin exhibits an ultra-high sensitivity of 9484.3 kPa-1 (<15 kPa), and the sensitivity remains higher than 235 kPa-1 in the wide range of 15-155 kPa. The device has also achieved a detection limit as low as 0.12 Pa or, equivalently, 0.31 mg, fast response within 24 ms, and high robustness (loading/unloading for 5000 cycles without fatigue). The sensor facilitates the challenging task of tele-operated robotic threading, which exceeds the human tactile perception limit when threading a needle. We have also confirmed that ionic skin can be used in robot-assisted invasive surgery, such as incision/resection of tissues and suturing of wounds, providing tactile information to surgeons to improve operation success rates. The flexible ionic skin is capable of conforming to the various shapes of robotic manipulators, thus has great promise for applications in robotic dexterous manipulation, prosthetics and human-machine interfaces.

Developing an Image-Based Deep Learning Framework for Automatic Scoring of the Pentagon Drawing Test.

BACKGROUND: The Pentagon Drawing Test (PDT) is a common assessment for visuospatial function. Evaluating the PDT by artificial intelligence can improve efficiency and reliability in the big data era. This study aimed to develop a deep learning (DL) framework for automatic scoring of the PDT based on image data. METHODS: A total of 823 PDT photos were retrospectively collected and preprocessed into black-and-white, square-shape images. Stratified fivefold cross-validation was applied for training and testing. Two strategies based on convolutional neural networks were compared. The first strategy was to perform an image classification task using supervised transfer learning. The second strategy was designed with an object detection model for recognizing the geometric shapes in the figure, followed by a predetermined algorithm to score based on their classes and positions. RESULTS: On average, the first framework demonstrated 62%accuracy, 62%recall, 65%precision, 63%specificity, and 0.72 area under the receiver operating characteristic curve. This performance was substantially outperformed by the second framework, with averages of 94%, 95%, 93%, 93%, and 0.95, respectively. CONCLUSION: An image-based DL framework based on the object detection approach may be clinically applicable for automatic scoring of the PDT with high efficiency and reliability. With a limited sample size, transfer learning should be used with caution if the new images are distinct from the previous training data. Partitioning the problem-solving workflow into multiple simple tasks should facilitate model selection, improve performance, and allow comprehensible logic of the DL framework.

[Effect of inhibition of Porphyromonas endodontalis on osteoblast differentiation].

PURPOSE: Porphyromonas endodontalis (P.e) is the dominant bacterium in the infected canal of pulpal and periapical disease.Lipopolysaccharides (LPS) in the outer membrane of the cell wall is an important toxicity factor of P.e. In this study, the effect of P.e-LPS on osteoblast differentiation was studied, and the pathogenic mechanism of P.e-LPS in periapical bone resorption disease was explored. METHODS: Porphyromonas endodontalis was cultured under anaerobic conditions. P.e-LPS was extracted by thermophenol water method, and then the extracted LPS was qualitatively analyzed by gel limulireagent method. Preosteoblast cell line MC3T3-E1 were induced to differentiate into osteoblasts by osteoblast differentiation medium (50 µg/mL ascorbic acid，6 mmol/L beta-glycerphosphate). Expressions of osteogenic differentiation genes including distal-less homeobox 5（DLX5）, runt-related transcription factor 2(Runx2), Osterix, bone sialoprotein (BSP), OCN(osteocalcin) and Collagen were detected by RT-PCR. The activity of alkaline phosphatase(ALP)， alizarin red staining and Von Kossa staining were used to determine the mineralization level of osteoblasts.The expression of TOLL-like receptor-4 (TLR-4), the receptor of P.e-LPS, was silenced by siRNA transfection. SPSS 11.0 software package was used for statistical analysis of the data. RESULTS: The mRNA expressions of osteogenic differentiation genes including DLX5, Runx2, Osterix, OCN, BSP, and Collagen were significantly decreased after treated with P.e-LPS (10 µg/mL) for 3 d, compared with the control group（P<0.05）.After treated with P.e-LPS (10 µg/mL) for 7 d or 14 d, respectively, ALP and alizarin red staining intensity was decreased. P.e-LPS was applied to the si-TLR-4 transfection group and the control group for 7,14 and 21 d, respectively. Compared with the control group, the expression level of osteogenic differentiation genes, ALP, alizarin red staining and Von Kossa staining intensity of si-TLR-4 group were significantly higher than those of the control group (P<0.05). CONCLUSIONS: P.e-LPS inhibits the differentiation of osteoblasts through TLR-4 receptor, thus participating in bone resorption process of periapical lesions.