Intra-articular sustained-release of pirfenidone as a disease-modifying treatment for early osteoarthritis.

Osteoarthritis (OA) is a major clinical challenge, and effective disease-modifying drugs for OA are still lacking due to the complicated pathology and scattered treatment targets. Effective early treatments are urgently needed to prevent OA progression. The excessive amount of transforming growth factor ß (TGFß) is one of the major causes of synovial fibrosis and subchondral bone sclerosis, and such pathogenic changes in early OA precede cartilage damage. Herein we report a novel strategy of intra-articular sustained-release of pirfenidone (PFD), a clinically-approved TGFß inhibitor, to achieve disease-modifying effects on early OA joints. We found that PFD effectively restored the mineralization in the presence of excessive amount of TGFß1 (as those levels found in patients' synovial fluid). A monthly injection strategy was then designed of using poly lactic-co-glycolic acid (PLGA) microparticles and hyaluronic acid (HA) solution to enable a sustained release of PFD (the "PLGA-PFD + HA" strategy). This strategy effectively regulated OA progression in destabilization of the medial meniscus (DMM)- induced OA mice model, including preventing subchondral bone loss in early OA and subchondral bone sclerosis in late OA, and reduced synovitis and pain with cartilage preservation effects. This finding suggests the promising clinical application of PFD as a novel disease-modifying OA drug.

Precision Identification of Locally Advanced Rectal Cancer in Denoised CT Scans Using EfficientNet and Voting System Algorithms.

BACKGROUND AND OBJECTIVE: Local advanced rectal cancer (LARC) poses significant treatment challenges due to its location and high recurrence rates. Accurate early detection is vital for treatment planning. With magnetic resonance imaging (MRI) being resource-intensive, this study explores using artificial intelligence (AI) to interpret computed tomography (CT) scans as an alternative, providing a quicker, more accessible diagnostic tool for LARC. METHODS: In this retrospective study, CT images of 1070 T3-4 rectal cancer patients from 2010 to 2022 were analyzed. AI models, trained on 739 cases, were validated using two test sets of 134 and 197 cases. By utilizing techniques such as nonlocal mean filtering, dynamic histogram equalization, and the EfficientNetB0 algorithm, we identified images featuring characteristics of a positive circumferential resection margin (CRM) for the diagnosis of locally advanced rectal cancer (LARC). Importantly, this study employs an innovative approach by using both hard and soft voting systems in the second stage to ascertain the LARC status of cases, thus emphasizing the novelty of the soft voting system for improved case identification accuracy. The local recurrence rates and overall survival of the cases predicted by our model were assessed to underscore its clinical value. RESULTS: The AI model exhibited high accuracy in identifying CRM-positive images, achieving an area under the curve (AUC) of 0.89 in the first test set and 0.86 in the second. In a patient-based analysis, the model reached AUCs of 0.84 and 0.79 using a hard voting system. Employing a soft voting system, the model attained AUCs of 0.93 and 0.88, respectively. Notably, AI-identified LARC cases exhibited a significantly higher five-year local recurrence rate and displayed a trend towards increased mortality across various thresholds. Furthermore, the model's capability to predict adverse clinical outcomes was superior to those of traditional assessments. CONCLUSION: AI can precisely identify CRM-positive LARC cases from CT images, signaling an increased local recurrence and mortality rate. Our study presents a swifter and more reliable method for detecting LARC compared to traditional CT or MRI techniques.

Effects of resistance training with elastic bands on bone mineral density, body composition, and osteosarcopenic obesity in elderly women: A meta-analysis.

Background: Elastic band exercises can improve bone density, muscle quality, and body fat in elderly patients with Sarcopenic Obesity Syndrome (SOS), a common diagnosis. Encouraging this exercise can bring significant benefits. Methods: We conducted a comprehensive search until April 1, 2023, covering UpToDate, PubMed, and Web of Science databases. The analysis focused on osteosarcopenic obesity and resistance training, involving four randomized controlled trials with 108 participants. After collecting key information, the methodological quality was assessed using the PEDro scale. Outcome quality was graded using the GRADE technique, and bias risk was evaluated using the Cochrane Bias Risk tool. Statistical analysis was performed using Review Manager 5.4. Results: After a 12-week elastic band resistance training regimen, the meta-analysis revealed significant improvement. The study focused on age-related osteoporosis and obesity in older women, evaluating parameters such as bone mineral density (BMD) (P < 0.001, I2 = 98 %, CI: 0.39-0.71), decreased body fat percentage (BFP) (CI: -262.55-260.11, P < 0.001, I2 = 100 %), and skeletal muscle mass index (SMI) (P < 0.001, I2 = 98 %, CI: 0.31-0.71). T-score (P < 0.001, I2 = 97 %, CI: -2.85-1.27), Time to Chair Rise (TCR) (P < 0.001, I2 = 100 %, CI: -24.28-23.44), and Gait Speed (GS) (P < 0.001, I2 = 100 %, CI: 9.84-9.88) were also evaluated. Conclusion: Following a 12-week elastic band resistance exercise regimen, older women showed notable improvements, particularly those with age-related osteoporosis and obesity.

Comparison of double chevron-cut and biplanar distal femoral osteotomy techniques: A biomechanical study.

OBJECTIVE: This study aimed to compare the stability and mechanical properties of the double chevron-cut (DCC) and biplanar (BP) distal femoral osteotomy (DFO) techniques, along with analyzing their respective contact surface areas. METHODS: Biomechanical testing was performed using sawbone and 3D modeling techniques to assess axial and torsional stability, torsional stiffness, and maximum torque of both osteotomy configurations. Additionally, 3D models of the sawbone femur were created to calculate and compare the contact surface area of the DCC, BP, and conventional single-plane DFO techniques. RESULTS: Axial stiffness and maximum strength did not significantly differ between the two osteotomy techniques. However, in terms of torsional properties, the DCC technique exhibited superior torsional stiffness compared to the BP group (27 ± 7.7 Nm/° vs. 4.5 ± 1.5 Nm/°, p = 0.008). Although the difference in maximum torque did not reach statistical significance (63 ± 10.6 vs. 56 ± 12.1, p = 0.87), it is noteworthy that the DCC group sawbone model exhibited fracture in the shaft region instead of at the osteotomy site. Therefore, the actual maximum torque of the DCC construct may not be accurately reflected by the numerical values obtained in this study. The contact surface area analysis revealed that the BP configuration had the largest contact surface area, 111% larger than that of the single-plane configuration. but 60% of it relied on the less reliable axial cut. Conversely, the DCC osteotomy offered a 31% larger contact surface area than the single-plane configuration, with both surfaces being weight-bearing. CONCLUSION: The DCC osteotomy exhibited superior mechanical stability, showing improved rotational stiffness and maximum torque when compared to the BP osteotomy. Although the BP osteotomy resulted in a larger contact surface area than the DCC osteotomy, both were larger than the conventional single-plane configuration. In clinical practice, both the DCC and BP techniques should be evaluated based on patient-specific characteristics and surgical goals.

Single-cell transcriptome analysis reveals the effectiveness of cytokine priming irrespective of heterogeneity in mesenchymal stromal cells.

BACKGROUND AIMS: Mesenchymal stromal cells (MSCs) are recognized as a potential cell-based therapy for regenerative medicine. Short-term inflammatory cytokine pre-stimulation (cytokine priming) is a promising approach to enhance regenerative efficacy of MSCs. However, it is unclear whether their intrinsic heterogenic nature causes an unequal response to cytokine priming, which might blunt the accessibility of clinical applications. METHODS: In this study, by analyzing the single-cell transcriptomic landscape of human bone marrow MSCs from a naïve to cytokine-primed state, we elucidated the potential mechanism of superior therapeutic potential in cytokine-primed MSCs. RESULTS: We found that cytokine-primed MSCs had a distinct transcriptome landscape. Although substantial heterogeneity was identified within the population in both naïve and primed states, cytokine priming enhanced the several characteristics of MSCs associated with therapeutic efficacy irrespective of heterogeneity. After cytokine-priming, all sub-clusters of MSCs possessed high levels of immunoregulatory molecules, trophic factors, stemness-related genes, anti-apoptosis markers and low levels of multi-lineage and senescence signatures, which are critical for their therapeutic potency. CONCLUSIONS: In conclusion, our results provide new insights into MSC heterogeneity under cytokine stimulation and suggest that cytokine priming reprogrammed MSCs independent of heterogeneity.

Mesenchymal stem cells alleviate dexamethasone-induced muscle atrophy in mice and the involvement of ERK1/2 signalling pathway.

BACKGROUND: High dosage of dexamethasone (Dex) is an effective treatment for multiple diseases; however, it is often associated with severe side effects including muscle atrophy, resulting in higher risk of falls and poorer life quality of patients. Cell therapy with mesenchymal stem cells (MSCs) holds promise for regenerative medicine. In this study, we aimed to investigate the therapeutic efficacy of systemic administration of adipose-derived mesenchymal stem cells (ADSCs) in mitigating the loss of muscle mass and strength in mouse model of DEX-induced muscle atrophy. METHODS: 3-month-old female C57BL/6 mice were treated with Dex (20 mg/kg body weight, i.p.) for 10 days to induce muscle atrophy, then subjected to intravenous injection of a single dose of ADSCs ([Formula: see text] cells/kg body weight) or vehicle control. The mice were killed 7 days after ADSCs treatment. Body compositions were measured by animal DXA, gastrocnemius muscle was isolated for ex vivo muscle functional test, histological assessment and Western blot, while tibialis anterior muscles were isolated for RNA-sequencing and qPCR. For in vitro study, C2C12 myoblast cells were cultured under myogenic differentiation medium for 5 days following 100 [Formula: see text]M Dex treatment with or without ADSC-conditioned medium for another 4 days. Samples were collected for qPCR analysis and Western blot analysis. Myotube morphology was measured by myosin heavy chain immunofluorescence staining. RESULTS: ADSC treatment significantly increased body lean mass (10-20%), muscle wet weight (15-30%) and cross-sectional area (CSA) (~ 33%) in DEX-induced muscle atrophy mice model and down-regulated muscle atrophy-associated genes expression (45-65%). Hindlimb grip strength (~ 37%) and forelimb ex vivo muscle contraction property were significantly improved (~ 57%) in the treatment group. Significant increase in type I fibres (~ 77%) was found after ADSC injection. RNA-sequencing results suggested that ERK1/2 signalling pathway might be playing important role underlying the beneficial effect of ADSC treatment, which was confirmed by ERK1/2 inhibitor both in vitro and in vivo. CONCLUSIONS: ADSCs restore the pathogenesis of Dex-induced muscle atrophy with an increased number of type I fibres, stronger muscle strength, faster recovery rate and more anti-fatigue ability via ERK1/2 signalling pathway. The inhibition of muscle atrophy-associated genes by ADSCs offered this treatment as an intervention option for muscle-associated diseases. Taken together, our findings suggested that adipose-derived mesenchymal stem cell therapy could be a new treatment option for patient with Dex-induced muscle atrophy.

Mechanotransduction of mesenchymal stem cells and hemodynamic implications.

Mesenchymal stem cells (MSCs) possess the capacity for self-renewal and multipotency. The traditional approach to manipulating MSC's fate choice predominantly relies on biochemical stimulation. Accumulating evidence also suggests the role of physical input in MSCs differentiation. Therefore, investigating mechanotransduction at the molecular level and related to tissue-specific cell functions sheds light on the responses secondary to mechanical forces. In this review, a new frontier aiming to optimize the cultural parameters was illustrated, i.e. spatial boundary condition, which recapitulates in vivo physiology and facilitates the investigations of cellular behavior. The concept of mechanical memory was additionally addressed to appreciate how MSCs store imprints from previous culture niches. Besides, different types of forces as physical stimuli were of interest based on the association with the respective signaling pathways and the differentiation outcome. The downstream mechanoreceptors and their corresponding effects were further pinpointed. The cardiovascular system or immune system may share similar mechanisms of mechanosensing and mechanotransduction; for example, resident stem cells in a vascular wall and recruited MSCs in the bloodstream experience mechanical forces such as stretch and fluid shear stress. In addition, baroreceptors or mechanosensors of endothelial cells detect changes in blood flow, pass over signals induced by mechanical stimuli and eventually maintain arterial pressure at the physiological level. These mechanosensitive receptors transduce pressure variation and regulate endothelial barrier functions. The exact signal transduction is considered context dependent but still elusive. In this review, we summarized the current evidence of how mechanical stimuli impact MSCs commitment and the underlying mechanisms. Future perspectives are anticipated to focus on the application of cardiovascular bioengineering and regenerative medicine.

Ginkgolide B facilitates muscle regeneration via rejuvenating osteocalcin-mediated bone-to-muscle modulation in aged mice.

BACKGROUND: The progressive deterioration of tissue-tissue crosstalk with aging causes a striking impairment of tissue homeostasis and functionality, particularly in the musculoskeletal system. Rejuvenation of the systemic and local milieu via interventions such as heterochronic parabiosis and exercise has been reported to improve musculoskeletal homeostasis in aged organisms. We have shown that Ginkgolide B (GB), a small molecule from Ginkgo biloba, improves bone homeostasis in aged mice by restoring local and systemic communication, implying a potential for maintaining skeletal muscle homeostasis and enhancing regeneration. In this study, we investigated the therapeutic efficacy of GB on skeletal muscle regeneration in aged mice. METHODS: Muscle injury models were established by barium chloride induction into the hind limb of 20-month-old mice (aged mice) and into C2C12-derived myotubes. Therapeutic efficacy of daily administrated GB (12 mg/kg body weight) and osteocalcin (50 µg/kg body weight) on muscle regeneration was assessed by histochemical staining, gene expression, flow cytometry, ex vivo muscle function test and rotarod test. RNA sequencing was used to explore the mechanism of GB on muscle regeneration, with subsequent in vitro and in vivo experiments validating these findings. RESULTS: GB administration in aged mice improved muscle regeneration (muscle mass, P = 0.0374; myofiber number/field, P = 0.0001; centre nucleus, embryonic myosin heavy chain-positive myofiber area, P = 0.0144), facilitated the recovery of muscle contractile properties (tetanic force, P = 0.0002; twitch force, P = 0.0005) and exercise performance (rotarod performance, P = 0.002), and reduced muscular fibrosis (collagen deposition, P < 0.0001) and inflammation (macrophage infiltration, P = 0.03). GB reversed the aging-related decrease in the expression of osteocalcin (P < 0.0001), an osteoblast-specific hormone, to promote muscle regeneration. Exogenous osteocalcin supplementation was sufficient to improve muscle regeneration (muscle mass, P = 0.0029; myofiber number/field, P < 0.0001), functional recovery (tetanic force, P = 0.0059; twitch force, P = 0.07; rotarod performance, P < 0.0001) and fibrosis (collagen deposition, P = 0.0316) in aged mice, without an increased risk of heterotopic ossification. CONCLUSIONS: GB treatment restored the bone-to-muscle endocrine axis to reverse aging-related declines in muscle regeneration and thus represents an innovative and practicable approach to managing muscle injuries. Our results revealed the critical and novel role of osteocalcin-GPRC6A-mediated bone-to-muscle communication in muscle regeneration, which provides a promising therapeutic avenue in functional muscle regeneration.

Application of a deep learning system in glaucoma screening and further classification with colour fundus photographs: a case control study.

BACKGROUND: To verify efficacy of automatic screening and classification of glaucoma with deep learning system. METHODS: A cross-sectional, retrospective study in a tertiary referral hospital. Patients with healthy optic disc, high-tension, or normal-tension glaucoma were enrolled. Complicated non-glaucomatous optic neuropathy was excluded. Colour and red-free fundus images were collected for development of DLS and comparison of their efficacy. The convolutional neural network with the pre-trained EfficientNet-b0 model was selected for machine learning. Glaucoma screening (Binary) and ternary classification with or without additional demographics (age, gender, high myopia) were evaluated, followed by creating confusion matrix and heatmaps. Area under receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, and F1 score were viewed as main outcome measures. RESULTS: Two hundred and twenty-two cases (421 eyes) were enrolled, with 1851 images in total (1207 normal and 644 glaucomatous disc). Train set and test set were comprised of 1539 and 312 images, respectively. If demographics were not provided, AUC, accuracy, precision, sensitivity, F1 score, and specificity of our deep learning system in eye-based glaucoma screening were 0.98, 0.91, 0.86, 0.86, 0.86, and 0.94 in test set. Same outcome measures in eye-based ternary classification without demographic data were 0.94, 0.87, 0.87, 0.87, 0.87, and 0.94 in our test set, respectively. Adding demographics has no significant impact on efficacy, but establishing a linkage between eyes and images is helpful for a better performance. Confusion matrix and heatmaps suggested that retinal lesions and quality of photographs could affect classification. Colour fundus images play a major role in glaucoma classification, compared to red-free fundus images. CONCLUSIONS: Promising results with high AUC and specificity were shown in distinguishing normal optic nerve from glaucomatous fundus images and doing further classification.

Functioning tailor-made 3D-printed vascular graft for hemodialysis.

BACKGROUND: The two ends of arteriovenous graft (AVG) are anastomosed to the upper limb vessels by surgery for hemodialysis therapy. However, the size of upper limb vessels varies to a large extent among different individuals. METHODS: According to the shape and size of neck vessels quantified from the preoperative computed tomography angiographic scan, the ethylene-vinyl acetate (EVA)-based AVG was produced in H-shape by the three-dimensional (3D) printer and then sterilized. This study investigated the function of this novel 3D-printed AVG in vitro and in vivo. RESULTS: This 3D-printed AVG can be implanted in the rabbit's common carotid artery and common jugular vein with ease and functions in vivo. The surgical procedure was quick, and no suture was required. The blood loss was minimal, and no hematoma was noted at least 1 week after the surgery. The blood flow velocity within the implanted AVG was 14.9 ± 3.7 cm/s. Additionally, the in vitro characterization experiments demonstrated that this EVA-based biomaterial is biocompatible and possesses a superior recovery property than ePTFE after hemodialysis needle cannulation. CONCLUSIONS: Through the 3D printing technology, the EVA-based AVG can be tailor-made to fit the specific vessel size. This kind of 3D-printed AVG is functioning in vivo, and our results realize personalized vascular implants. Further large-animal studies are warranted to examine the long-term patency.

Demystifying the long noncoding RNA landscape of small EVs derived from human mesenchymal stromal cells.

INTRODUCTION: The regenerative capacity of mesenchymal stromal cells or medicinal signaling cells (MSCs) is largely mediated by their secreted small extracellular vesicles (sEVs), and the therapeutic efficacy of sEVs can be enhanced by licensing approaches (e.g., cytokines, hypoxia, chemicals, and genetic modification). Noncoding RNAs within MSC-derived sEVs (MSC-sEVs) have been demonstrated to be responsible for tissue regeneration. However, unlike miRNA fingerprints, which have been explored, the landscape of long noncoding RNAs (lncRNAs) in MSC-sEVs remains to be described. OBJECTIVES: To characterize lncRNA signatures in sEVs of human adipose-derived MSCs with or without inflammatory cytokine licensing and depict MSC-sEV-specific and MSC-enriched lncRNA repertoires. METHODS: sEVs were isolated from MSCs with or without TNF-α and IFN-γ (20 ng/mL) stimulation. High-throughput lncRNA sequencing and an in silico approach were employed to analyze the profile of lncRNAs in sEVs and predict lncRNA-protein interactomes. RESULTS: sEVs derived from human MSCs and fibroblasts carried a unique landscape of lncRNAs distinct from the lncRNAs inside these cells. Compared with fibroblast-derived sEVs (F-sEVs), 194 MSC-sEV-specific and 8 upregulated lncRNAs in MSC-sEVs were considered "medicinal signaling lncRNAs"; inflammatory cytokines upregulated 27 lncRNAs in MSC-sEVs, which were considered "licensing-responsive lncRNAs". Based on lncRNA-protein interactome prediction and enrichment analysis, we found that the proteins interacting with medicinal signaling lncRNAs or licensing-responsive lncRNAs have a tight interaction network involved in chromatin remodeling, SWI/SNF superfamily type complexes, and histone binding. CONCLUSION: In summary, our study depicts the landscape of lncRNAs in MSC-sEVs and predicts their potential functions via the lncRNA-protein interactome. Elucidation of the lncRNA landscape of MSC-sEVs will facilitate defining the therapeutic potency of MSC-sEVs and the development of sEV-based therapeutics.

Application of a Deep Learning System in Pterygium Grading and Further Prediction of Recurrence with Slit Lamp Photographs.

BACKGROUND: The aim of this study was to evaluate the efficacy of a deep learning system in pterygium grading and recurrence prediction. METHODS: This was a single center, retrospective study. Slit-lamp photographs, from patients with or without pterygium, were collected to develop an algorithm. Demographic data, including age, gender, laterality, grading, and pterygium area, recurrence, and surgical methods were recorded. Complex ocular surface diseases and pseudopterygium were excluded. Performance of the algorithm was evaluated by sensitivity, specificity, F1 score, accuracy, and area under the receiver operating characteristic curve. Confusion matrices and heatmaps were created to help explain the results. RESULTS: A total of 237 eyes were enrolled, of which 176 eyes had pterygium and 61 were non-pterygium eyes. The training set and testing set were comprised of 189 and 48 photographs, respectively. In pterygium grading, sensitivity, specificity, F1 score, and accuracy were 80% to 91.67%, 91.67% to 100%, 81.82% to 94.34%, and 86.67% to 91.67%, respectively. In the prediction model, our results showed sensitivity, specificity, positive predictive value, and negative predictive values were 66.67%, 81.82%, 33.33%, and 94.74%, respectively. CONCLUSIONS: Deep learning systems can be useful in pterygium grading based on slit lamp photographs. When clinical parameters involved in the prediction of pterygium recurrence were included, the algorithm showed higher specificity and negative predictive value in prediction.

Artificial Intelligence-Assisted Diagnosis of Anterior Cruciate Ligament Tears From Magnetic Resonance Images: Algorithm Development and Validation Study.

BACKGROUND: Anterior cruciate ligament (ACL) injuries are common in sports and are critical knee injuries that require prompt diagnosis. Magnetic resonance imaging (MRI) is a strong, noninvasive tool for detecting ACL tears, which requires training to read accurately. Clinicians with different experiences in reading MR images require different information for the diagnosis of ACL tears. Artificial intelligence (AI) image processing could be a promising approach in the diagnosis of ACL tears. OBJECTIVE: This study sought to use AI to (1) diagnose ACL tears from complete MR images, (2) identify torn-ACL images from complete MR images with a diagnosis of ACL tears, and (3) differentiate intact-ACL and torn-ACL MR images from the selected MR images. METHODS: The sagittal MR images of torn ACL (n=1205) and intact ACL (n=1018) from 800 cases and the complete knee MR images of 200 cases (100 torn ACL and 100 intact ACL) from patients aged 20-40 years were retrospectively collected. An AI approach using a convolutional neural network was applied to build models for the objective. The MR images of 200 independent cases (100 torn ACL and 100 intact ACL) were used as the test set for the models. The MR images of 40 randomly selected cases from the test set were used to compare the reading accuracy of ACL tears between the trained model and clinicians with different levels of experience. RESULTS: The first model differentiated between torn-ACL, intact-ACL, and other images from complete MR images with an accuracy of 0.9946, and the sensitivity, specificity, precision, and F1-score were 0.9344, 0.9743, 0.8659, and 0.8980, respectively. The final accuracy for ACL-tear diagnosis was 0.96. The model showed a significantly higher reading accuracy than less experienced clinicians. The second model identified torn-ACL images from complete MR images with a diagnosis of ACL tear with an accuracy of 0.9943, and the sensitivity, specificity, precision, and F1-score were 0.9154, 0.9660, 0.8167, and 0.8632, respectively. The third model differentiated torn- and intact-ACL images with an accuracy of 0.9691, and the sensitivity, specificity, precision, and F1-score were 0.9827, 0.9519, 0.9632, and 0.9728, respectively. CONCLUSIONS: This study demonstrates the feasibility of using an AI approach to provide information to clinicians who need different information from MRI to diagnose ACL tears.

Mechanical and chemical cues synergistically promote human venous smooth muscle cell osteogenesis through integrin ß1-ERK1/2 signaling: A cell model of hemodialysis fistula calcification.

Arteriovenous fistula (AVF) is the vascular access of choice for renal replacement therapy. However, AVF is susceptible to calcification with a high prevalence of 40%-65% in chronic hemodialysis patients. Repeated needle puncture for hemodialysis cannulation results in intimal denudation of AVF. We hypothesized that exposure to blood shear stress in the medial layer promotes venous smooth muscle cell (SMC) osteogenesis. While previous studies of shear stress focused on arterial-type SMCs, SMCs isolated from the vein had not been investigated. This study established a venous cell model of AVF using the fluid shear device, combined with a high phosphate medium to mimic the uremic milieu. Osteogenic gene expression of venous SMCs upon mechanical and chemical cues was analyzed in addition to the activated cell signaling pathways. Our findings indicated that upon shear stress and high phosphate environment, mechanical stimulation (shear stress) had an additive effect in up-regulation of an early osteogenic marker, Runx2. We further identified that the integrin ß1-ERK1/2 signaling pathway was responsible for the molecular basis of venous SMC osteogenesis upon shear stress exposure. Mitochondrial biogenesis also took part in the early stage of this venopathy pathogenesis, evident by the up-regulated mitochondrial transcription factor A and mitochondrial DNA polymerase γ in venous SMCs. In conclusion, synergistic effects of fluid shear stress and high phosphate induce venous SMC osteogenesis via the ERK1/2 pathway through activating the mechanosensing integrin ß1 signaling. The present study identified a promising druggable target for reducing AVF calcification, which deserves further in vivo investigations.

Molecular Mechanisms of Mesenchymal Stem Cell-Based Therapy in Acute Kidney Injury.

Acute kidney injury (AKI) causes a lot of harm to human health but is treated by only supportive therapy in most cases. Recent evidence shows that mesenchymal stem cells (MSCs) benefit kidney regeneration through releasing paracrine factors and extracellular vesicles (EVs) to the recipient kidney cells and are considered to be promising cellular therapy for AKI. To develop more efficient, precise therapies for AKI, we review the therapeutic mechanism of MSCs and MSC-derived EVs in AKI and look for a better understanding of molecular signaling and cellular communication between donor MSCs and recipient kidney cells. We also review recent clinical trials of MSC-EVs in AKI. This review summarizes the molecular mechanisms of MSCs' therapeutic effects on kidney regeneration, expecting to comprehensively facilitate future clinical application for treating AKI.

Patient-Specific Instrument Guided Double Chevron-Cut Distal Femur Osteotomy.

The risk of non-union and prolonged periods of protected weight-bearing still remain unsolved issues after distal femur osteotomy (DFO). To improve the stability, we developed the double chevron-cut technique, which is a modified medial closing-wedge DFO guided by a patient-specific instrument. The purpose of this study was to investigate the feasibility and outcome of this operative approach. Twenty-five knees in twenty-three consecutive patients with genu valgum and lateral compartment osteoarthritis that received double chevron-cut DFO were included. The target of correction was 50% on the weight-bearing line (WBL) ratio. Patient-reported outcomes included the Oxford Knee Score (OKS) and the 2011 Knee Society Score (KSS). The mean of the WBL ratio was corrected from 78.7% ± 12.0% to 48.7% ± 2.9% postoperatively. The mean time to full weight bearing was 3.7 ± 1.4 weeks. Union of the osteotomy was achieved at 11.3 ± 2.8 weeks. At a mean follow-up of 17 months, the OKS improved from a mean of 27.6 ± 11.7 to 39.1 ± 7.5 (p = 0.03), and the KSS from a mean of 92.1 ± 13.0 to 143.9 ± 10.2 (p < 0.001). Three patients developed complications, including one case of peri-implant fracture, one of loss of fixation, and one of non-union. The double chevron-cut DFO followed by immediate weight-bearing as tolerated is effective in treating genu valgum deformity and associated lateral compartment osteoarthritis.

Machine Learning Analysis of Time-Dependent Features for Predicting Adverse Events During Hemodialysis Therapy: Model Development and Validation Study.

BACKGROUND: Hemodialysis (HD) therapy is an indispensable tool used in critical care management. Patients undergoing HD are at risk for intradialytic adverse events, ranging from muscle cramps to cardiac arrest. So far, there is no effective HD device-integrated algorithm to assist medical staff in response to these adverse events a step earlier during HD. OBJECTIVE: We aimed to develop machine learning algorithms to predict intradialytic adverse events in an unbiased manner. METHODS: Three-month dialysis and physiological time-series data were collected from all patients who underwent maintenance HD therapy at a tertiary care referral center. Dialysis data were collected automatically by HD devices, and physiological data were recorded by medical staff. Intradialytic adverse events were documented by medical staff according to patient complaints. Features extracted from the time series data sets by linear and differential analyses were used for machine learning to predict adverse events during HD. RESULTS: Time series dialysis data were collected during the 4-hour HD session in 108 patients who underwent maintenance HD therapy. There were a total of 4221 HD sessions, 406 of which involved at least one intradialytic adverse event. Models were built by classification algorithms and evaluated by four-fold cross-validation. The developed algorithm predicted overall intradialytic adverse events, with an area under the curve (AUC) of 0.83, sensitivity of 0.53, and specificity of 0.96. The algorithm also predicted muscle cramps, with an AUC of 0.85, and blood pressure elevation, with an AUC of 0.93. In addition, the model built based on ultrafiltration-unrelated features predicted all types of adverse events, with an AUC of 0.81, indicating that ultrafiltration-unrelated factors also contribute to the onset of adverse events. CONCLUSIONS: Our results demonstrated that algorithms combining linear and differential analyses with two-class classification machine learning can predict intradialytic adverse events in quasi-real time with high AUCs. Such a methodology implemented with local cloud computation and real-time optimization by personalized HD data could warn clinicians to take timely actions in advance.

The Crosstalk between Mesenchymal Stem Cells and Macrophages in Bone Regeneration: A Systematic Review.

Bone regeneration is a complex and well-coordinated process that involves crosstalk between immune cells and resident cells in the injury site. Transplantation of mesenchymal stem cells (MSCs) is a promising strategy to enhance bone regeneration. Growing evidence suggests that macrophages have a significant impact on osteogenesis during bone regeneration. However, the precise mechanisms by which macrophage subtypes influence bone regeneration and how MSCs communicate with macrophages have not yet been fully elucidated. In this systematic literature review, we gathered evidence regarding the crosstalk between MSCs and macrophages during bone regeneration. According to the PRISMA protocol, we extracted literature from PubMed and Embase databases by using "mesenchymal stem cells" and "macrophages" and "bone regeneration" as keywords. Thirty-three studies were selected for this review. MSCs isolated from both bone marrow and adipose tissue and both primary macrophages and macrophage cell lines were used in the selected studies. In conclusion, anti-inflammatory macrophages (M2) have significantly more potential to strengthen bone regeneration compared with naïve (M0) and classically activated macrophages (M1). Transplantation of MSCs induced M1-to-M2 transition and transformed the skeletal microenvironment to facilitate bone regeneration in bone fracture and bone defect models. This review highlights the complexity between MSCs and macrophages, providing more insight into the polarized macrophage behavior in this evolving field of osteoimmunology. The results may serve as a useful reference for definite success in MSC-based therapy based on the critical interaction with macrophages.

Deep Learning Application for Vocal Fold Disease Prediction Through Voice Recognition: Preliminary Development Study.

BACKGROUND: Dysphonia influences the quality of life by interfering with communication. However, a laryngoscopic examination is expensive and not readily accessible in primary care units. Experienced laryngologists are required to achieve an accurate diagnosis. OBJECTIVE: This study sought to detect various vocal fold diseases through pathological voice recognition using artificial intelligence. METHODS: We collected 189 normal voice samples and 552 samples of individuals with voice disorders, including vocal atrophy (n=224), unilateral vocal paralysis (n=50), organic vocal fold lesions (n=248), and adductor spasmodic dysphonia (n=30). The 741 samples were divided into 2 sets: 593 samples as the training set and 148 samples as the testing set. A convolutional neural network approach was applied to train the model, and findings were compared with those of human specialists. RESULTS: The convolutional neural network model achieved a sensitivity of 0.66, a specificity of 0.91, and an overall accuracy of 66.9% for distinguishing normal voice, vocal atrophy, unilateral vocal paralysis, organic vocal fold lesions, and adductor spasmodic dysphonia. Compared with the accuracy of human specialists, the overall accuracy rates were 60.1% and 56.1% for the 2 laryngologists and 51.4% and 43.2% for the 2 general ear, nose, and throat doctors. CONCLUSIONS: Voice alone could be used for common vocal fold disease recognition through a deep learning approach after training with our Mandarin pathological voice database. This approach involving artificial intelligence could be clinically useful for screening general vocal fold disease using the voice. The approach includes a quick survey and a general health examination. It can be applied during telemedicine in areas with primary care units lacking laryngoscopic abilities. It could support physicians when prescreening cases by allowing for invasive examinations to be performed only for cases involving problems with automatic recognition or listening and for professional analyses of other clinical examination results that reveal doubts about the presence of pathologies.