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Abstract Exploring lanthanide light upconversion (UC) has emerged as a promising strategy to enhance the near-infrared (NIR) responsive region of silicon solar cells (SSCs). However, its practical application under normal sunlight conditions has been hindered by the narrow NIR excitation bandwidth and the low UC efficiency of conventional materials. Here, we report the design of an efficient multiband UC system based on Ln3+/Yb3+-doped core-shell upconversion nanoparticles (Ln/Yb-UCNPs, Ln3+ = Ho3+, Er3+, Tm3+). In our design, Ln3+ ions are incorporated into distinct layers of Ln/Yb-UCNPs to function as near-infrared (NIR) absorbers across different spectral ranges. This design achieves broad multiband absorption withtin the 1100 to 2200 nm range, with an aggregated bandwidth of ~500 nm. We have identified a synthetic electron pumping (SEP) effect involving Yb3+ ions, facilitated by the synergistic interplay of energy transfer and cross-relaxation between Yb3+ and other ions Ln3+ (Ho3+, Er3+, Tm3+). This SEP effect enhances the UC efficiency of the nanomaterials by effectively transferring electrons from the low-excited states of Ln3+ to the excited state of Yb3+, resulting in intense Yb3+ luminescence at ~980 nm within the optimal response region for SSCs, thus markedly improving their overall performance. The SSCs integrated with Ln/Yb-UCNPs with multiband excitation demonstrate the largest reported NIR response range up to 2200 nm, while enabling the highest improvement in absolute photovoltaic efficiency reported, with an increase of 0.87% (resulting in a total efficiency of 19.37%) under standard AM 1.5 G irradiation. Our work tackles the bottlenecks in UCNP-coupled SSCs and introduces a viable approach to extend the NIR response of SSCs.

Abstract Background Cognitive impairment presents in both adolescent-onset(ado-OP) and adult-onset psychosis(adu-OP). Age and neurodevelopmental factors likely contribute to cognitive differences. This study aimed to characterize cognitive functions in ado-OP compared to adu-OP in a clinical population with drug-naive first-episode psychosis(FEP). Methods A total of 788 drug-naive patients with FEP and 774 sex- and age-matched healthy controls(HCs) were included. Participants were divided into four groups by whether they were under or over 21 years of age: adolescent-onset FEP(ado-FEP, n = 380), adult-onset FEP(adu-FEP, n = 408), adolescent HC(ado-HC, n = 334), and adult HC(adu-HC, n = 440). Comprehensive cognitive assessments were performed using the MATRICS Cognitive Consensus Battery(MCCB), covers six cognitive domains: speed of processing, attention/vigilance, working memory, verbal learning, visual learning, reasoning, and problem-solving. Data analyses were conducted using correlation analyses and binary logistic regression. Results The patterns of cognitive domain differences between ado-FEP and adu-FEP were found to be similar to those between ado-HC and adu-HC, whereas cognitive impairments appeared to be more pronounced in patients with adu-OP than ado-OP. The mazes subtest had the maximum effect size(ES) in the FEP(ES = 0.37) and HC(ES = 0.30) groups when comparing the adolescent and adult groups. Cognitive subtests were mostly significantly correlated with negative symptoms, especially for adolescents with FEP, in which all the subtests were significantly correlated with negative symptoms in the ado-FEP group. Better performance in the domains of spatial cognition and problem-solving abilities was more likely in the ado-FEP group than in the adu-FEP group. Conclusions These findings suggest cognitive differences between adolescents and adults but similar patterns of affected domains in HCs and patients with FEP. Therefore, the development of targeted cognitive interventions tailored to the specific needs of different age groups appears warranted.

Abstract Background The effects of antipsychotic (AP) medications on cognitive functions in individuals at clinical high-risk (CHR) of psychosis are poorly understood. This study compared the effects of AP treatment on cognitive improvement in CHR adolescents and adults. Methods A total of 327 CHR participants, with an age range of 13 to 45 years, who underwent baseline neuropsychological assessments and a 1-year clinical follow-up were included. Participants with CHR were categorized into four groups based on their age: adolescents (aged

Abstract Photodynamic therapy (PDT) fundamentally relies on local generation of PDT precursor states in added photosensitizers (PS), particularly triplet and photo-radical states. Monitoring these states in situ can provide important feedback but is difficult in practice. The states are strongly influenced by local oxygenation, pH and redox conditions, often varying significantly at PDT treatment sites. To overcome this problem, we followed local PDT precursor state populations of PS compounds, via their fluorescence intensity response to systematically varied excitation light modulation. Thereby, we could demonstrate local monitoring of PDT precursor states of methylene blue (MB) and IRdye700DX (IR700), and determined their transitions rates under different oxygenation, pH and redox conditions. By fiber-optics, using one fiber for both excitation and fluorescence detection, the triplet and photo-radical state kinetics of locally applied MB and IR700 could then be monitored in a tissue sample. Finally, potassium iodide and ascorbate were added as possible PDT adjuvants, enhancing intersystem crossing and photoreduction, respectively, and their effects on the PDT precursor states of MB and IR700 could be locally monitored. Taken together, the presented procedure overcomes current methodological limitations and can offer feedback, guiding both excitation and PDT adjuvant application, and thereby more efficient and targeted PDT treatments.

IntroductionWhile the attention to personality disorders (PD) and childhood maltreatment (CM) has grown in recent years, there remains limited understanding of the prevalence and distinctions of PD and CM in clinical populations of Chinese adolescents in comparison to adults.MethodsA total of 1,417 participants were consecutively sampled from patients diagnosed with either psychotic or non-psychotic disorders in the psychiatric and psycho-counseling clinics at Shanghai Mental Health Center. The participants were categorized into two groups based on their age: adolescents (aged 15-21 years) and adults (aged 22-35 years). PDs were evaluated using a self-reported personality diagnostic questionnaire and a structured clinical interview, while CMs were assessed using the Chinese version of the Child Trauma Questionnaire Short Form.ResultsWhen comparing self-reported PD traits and CM between adolescents and adults, differences emerge. Adolescents, particularly in the psychotic disorder group, exhibit more pronounced schizotypal PD traits (p=0.029), and this pattern extends to non-psychotic disorders (p

Abstract This study aimed to construct a multi-segment lumbar finite element model (FEM) of PTED surgery to analyze the changes in stress and ROM after visible trephine-based foraminoplasty. The CT scans of a 35-year-old healthy male were used to develop a multi-segment lumbar FEM with Mimic, Geomagic Studio, Hypermesh and MSC.Patran. Different foraminoplasty was performed on the model, and these were grouped into normal group (A), the ventral resection group (B), the apex resection group (C), the ventral + apex + isthmus resection group (D), and the SAP + isthmus + lateral recess resection group (E). A vertical load of 500N and a torque of 10N·M were applied to the upper surface of the L3 vertebral body to simulate the biomechanical characteristics under the motion of flexion, extension, lateral bending, and rotation. The von Mises stress maps of the intervertebral f, vertebral body, facet joints, and the ROM of the L3-S1 intervertebral disk were calculated and analyzed. The changes of peak stress on the vertebral body for each group were not significant in the same motion state. Significant stress differences were observed in the L4/5 intervertebral disks, while no obvious stress changes were observed for the L3/4 and L5/S1 intervertebral disks. The stress of the L3/4 and L5/S1 facet joints decreased after L4/5 foraminoplasty, while the stress of L4/5 facet joints displayed an overall increasing trend. Significant asymmetrical stress changes of bilateral facet joints were observed in all three segments, particularly during bilateral rotation movements. The ROM of L3-S1 gradually increased from Group A to Group E, especially during flexion, left lateral bending, and right rotation, with the highest elevation observed for the L45 ROM. Our FEM indicated that enlarged resection and exposure of the articular surface could lead to significant asymmetrical stress changes in the bilateral facet joints and ROM instability of the surgical and adjacent segments. These findings suggested that unnecessary and excessive resection should be avoided in PTED to reduce the incidence of low back pain and the risk of postsurgical degeneration.

Abstract Background Colposcopy is indispensable for the diagnosis of cervical lesions. However, its diagnosis accuracy for high‐grade squamous intraepithelial lesion (HSIL) is at about 50%, and the accuracy is largely dependent on the skill and experience of colposcopists. The advancement in computational power made it possible for the application of artificial intelligence (AI) to clinical problems. Here, we explored the feasibility and accuracy of the application of AI on precancerous and cancerous cervical colposcopic image recognition and classification. Methods The images were collected from 6002 colposcopy examinations of normal control, low‐grade squamous intraepithelial lesion (LSIL), and HSIL. For each patient, the original, Schiller test, and acetic‐acid images were all collected. We built a new neural network classification model based on the hybrid algorithm. EfficientNet‐b0 was used as the backbone network for the image feature extraction, and GRU(Gate Recurrent Unit)was applied for feature fusion of the three modes examinations (original, acetic acid, and Schiller test). Results The connected network classifier achieved an accuracy of 90.61% in distinguishing HSIL from normal and LSIL. Furthermore, the model was applied to “Trichotomy”, which reached an accuracy of 91.18% in distinguishing the HSIL, LSIL and normal control at the same time. Conclusion Our results revealed that as shown by the high accuracy of AI in the classification of colposcopic images, AI exhibited great potential to be an effective tool for the accurate diagnosis of cervical disease and for early therapeutic intervention in cervical precancer.

Abstract Numerous studies on short‐term load forecasting (STLF) have used feature extraction methods to increase the model's accuracy by incorporating multidimensional features containing time, weather and distance information. However, less attention has been paid to the input data size and output dimensions in STLF. To address these two issues, an STLF model is proposed based on output dimensions using only load data. First, the load data's long‐term behavior (trend and seasonality) is extracted through the long short‐term memory network (LSTM), followed by convolution to obtain the load data's non‐stationarity. Then, using the self‐attention mechanism (SAM), the crucial input load information is emphasized in the forecasting process. The calculation example shows that the proposed algorithm outperforms LSTM, LSTM‐based SAM, and CNN‐GRU‐based SAM by more than 10% in eight different buildings, demonstrating its suitability for forecasting with only load data. Additionally, compared to earlier research utilizing two well‐known public data sets, the MAPE is optimized by 2.2% and 5%, respectively. Also, the method has good prediction accuracy for a wide variety of time granularities and load aggregation levels, so it can be applied to various load forecasting scenarios and has good reference significance for load forecasting instrumentation.

Abstract Immunological/inflammatory factors are implicated in the development of psychosis. Complement is a key driver of inflammation; however, it remains unknown which factor is better at predicting the onset of psychosis. This study aimed to compare the alteration and predictive performance of inflammation and complement in individuals at clinical high risk (CHR). We enrolled 49 individuals at CHR and 26 healthy controls (HCs). Twenty-five patients at CHR had converted to psychosis (converter) by the 3-year follow-up. Inflammatory cytokines, including interleukin (IL)-1β, 6, 8, 10, tumor necrosis factor-alpha (TNF-alpha), macrophage colony-stimulating factor levels, and complement proteins (C1q, C2, C3, C3b, C4, C4b, C5, C5a, factor B, D, I, H) were measured by enzyme-linked immunosorbent assay at baseline. Except for TNF- alpha, none of the inflammatory cytokines reached a significant level in either the comparison of CHR individuals and HC or between CHR-converters and non-converters. The C5, C3, D, I, and H levels were significantly lower (C5, p = 0.006; C3, p = 0.009; D, p = 0.026; I, p = 0.016; H, p = 0.019) in the CHR group than in the HC group. Compared to non-converters, converters had significantly lower levels of C5 (p = 0.012) and C5a (p = 0.007). None of the inflammatory factors, but many complement factors, showed significant correlations with changes in general function and symptoms. None of the inflammatory markers, except for C5a and C5, were significant in the discrimination of conversion outcomes in CHR individuals. Our results suggest that altered complement levels in the CHR population are more associated with conversion to psychosis than inflammatory factors. Therefore, an activated complement system may precede the first-episode of psychosis and contribute to neurological pathogenesis at the CHR stage.

BackgroundPrevious research has demonstrated the negative impact of the coronavirus disease-2019 (COVID-19) pandemic on mental health.AimsTo examine changes in the Chinese psychiatric emergency department (PED) visits for mental health crises that occurred during the pandemic.MethodsBefore and during the COVID-19 pandemic, PED visit counts from the largest psychiatric hospital in China between 2018 and 2020 were investigated. Electronic medical records of 2020 PED visits were extracted during the COVID-19 pandemic period and compared for the same period of 2018 and 2019.ResultsOverall, PED visits per year increased from 1,767 in 2018 to 2210 (an increase of 25.1%) in 2019 and 2,648 (an increase of 49.9%) in 2020. Compared with 2 years before the epidemic, during the COVID-19 pandemic, the proportion of PED visits among patients with stress disorders, sleep disorders, and anxiety disorders increased significantly. In terms of the distribution of demographic characteristics, age shows a younger trend, while the gender difference is not significant.ConclusionThese findings suggest that PED care-seeking increases during the COVID-19 pandemic, highlighting the need to integrate mental health services for patients with stress, sleep, anxiety, and obsessive-compulsive disorders during public health crises.

Wear debris-induced osteolysis, especially titanium (Ti) particles-induced osteolysis, is the most common cause of arthroplasty failure with no effective therapy. Previous studies have suggested that inflammation and impaired osteogenesis are associated with Ti particles -induced osteolysis. Selenium (Se) is an essential trace element in the human body, which forms selenomethionine (Se-Met) in nature, and selenoproteins has strong anti-inflammatory and antioxidant stress effects. In this study, the effects of Se-Met on Ti particles-induced osteolysis were observed and the potential mechanism was explored. We found that exogenous Se-Met relieved osteolysis induced by Ti particles in two animal models and MC3T3-E1 cells. We found that the addition of Se-Met effectively inhibited Ti particle-induced inflammation by regulating reactive oxygen species-dependent (ROS-dependent) NOD-like receptor protein 3 (NLRP3) inflammasome activation. These therapeutic effects were abrogated in MC3T3-E1 cells that had received a β-catenin antagonist, suggesting that Se-Met alleviates inflammatory osteolysis via the β-catenin signaling pathway. Collectively, these findings indicated that Se-Met may serve as a potential therapeutic agent for treating Ti particle-induced osteolysis.

Corrosion is one of the critical factors leading to the failure of steel structures. Ultrasonic C-scans are widely used to identify corrosion damage. Limited by the range of C-scans, multiple C-scans are usually required to cover the whole component. Thus, stitching multiple C-scans into a panoramic image of the area under detection is necessary for interpreting non-destructive testing (NDT) data. In this paper, an image mosaic method for ultrasonic C-scan based on scale invariant feature transform (SIFT) is proposed. Firstly, to improve the success rate of registration, the difference in the probe starting position in two scans is used to filter the matching pairs of feature points obtained by SIFT. Secondly, dynamic programming methods are used to search for the optimal seam path. Finally, the pixels in the overlapping area are fused by fade-in and fade-out fusion along the seam line. The improved method has a higher success rate of registration and lower image distortion than the conventional method in the mosaic of ultrasonic C-scan images. Experimental results show that the proposed method can stitch multiple C-scan images of a testing block containing artificial defects into a panorama image effectively.

Objectives To conduct a comprehensive analysis of demographic information, medical history, and blood pressure (BP) and heart rate (HR) variability during hospitalisation so as to establish a predictive model for preoperative in-hospital mortality of patients with acute aortic dissection (AD) by using machine learning techniques.Design Retrospective cohort study.Setting Data were collected from the electronic records and the databases of Shanghai Ninth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine and the First Affiliated Hospital of Anhui Medical University between 2004 and 2018.Participants 380 inpatients diagnosed with acute AD were included in the study.Primary outcome Preoperative in-hospital mortality rate.Results A total of 55 patients (14.47%) died in the hospital before surgery. The results of the areas under the receiver operating characteristic curves, decision curve analysis and calibration curves indicated that the eXtreme Gradient Boosting (XGBoost) model had the highest accuracy and robustness. According to the SHapley Additive exPlanations analysis of the XGBoost model, Stanford type A, maximum aortic diameter >5.5 cm, high variability in HR, high variability in diastolic BP and involvement of the aortic arch had the greatest impact on the occurrence of in-hospital deaths before surgery. Moreover, the predictive model can accurately predict the preoperative in-hospital mortality rate at the individual level.Conclusion In the current study, we successfully constructed machine learning models to predict the preoperative in-hospital mortality of patients with acute AD, which can help identify high-risk patients and optimise the clinical decision-making. Further applications in clinical practice require the validation of these models using a large-sample, prospective database.Trial registration number ChiCTR1900025818.

The authors introduce stimulated-emission induced excitation depletion (STExD) nanoscopy using a single pair of low-power, near-infrared, continue-wave lasers. Emission of multichromatic probes is inhibited by cascade amplified depletion in lanthanide upconversion systems induced by manipulating their common sensitizer.

Abstract The current concept of clinical high-risk(CHR) of psychosis relies heavily on “below-threshold” (i.e. attenuated or limited and intermittent) psychotic positive phenomena as predictors of the risk for future progression to “above-threshold” positive symptoms (aka “transition” or “conversion”). Positive symptoms, even at attenuated levels are often treated with antipsychotics (AP) to achieve clinical stabilization and mitigate the psychopathological severity. The goal of this study is to contextually examine clinicians’ decision to prescribe AP, CHR individuals’ decision to take AP and psychosis conversion risk in relation to prodromal symptoms profiles. CHR individuals (n = 600) were recruited and followed up for 2 years between 2016 and 2021. CHR individuals were referred to the participating the naturalistic follow-up study, which research procedure was independent of the routine clinical treatment. Clinical factors from the Structured Interview for Prodromal Syndromes (SIPS) and global assessment of function (GAF) were profiled via exploratory factor analysis (EFA), then the extracted factor structure was used to investigate the relationship of prodromal psychopathology with clinicians’ decisions to AP-prescription, CHR individuals’ decisions to AP-taking and conversion to psychosis. A total of 427(71.2%) CHR individuals were prescribed AP at baseline, 532(88.7%) completed the 2-year follow-up, 377(377/532, 70.9%) were taken AP at least for 2 weeks during the follow-up. EFA identified six factors (Factor-1-Negative symptoms, Factor-2-Global functions, Factor-3-Disorganized communication & behavior, Factor-4-General symptoms, Factor-5-Odd thoughts, and Factor-6-Distorted cognition & perception). Positive symptoms (Factor-5 and 6) and global functions (Factor-2) factors were significant predictors for clinicians’ decisions to AP-prescription and CHR individuals’ decisions to assume AP, whereas negative symptoms (Factor-1) and global functions (Factor-2) factors predicted conversion. While decisions to AP-prescription, decisions to AP-taking were associated to the same factors (positive symptoms and global functions), only one of those was predictive of conversion, i.e. global functions. The other predictor of conversion, i.e. negative symptoms, did not seem to be contemplated both on the clinician and patients’ sides. Overall, the findings indicated that a realignment in the understanding of AP usage is warranted.

Electroencephalography (EEG) signals can be used as a neuroimaging indicator to analyze brain-related diseases and mental states, such as schizophrenia, which is a common and serious mental disorder. However, the main limiting factor of using EEG data to support clinical schizophrenia diagnosis lies in the inadequacy of both objective characteristics and effective data analysis techniques. Random matrix theory (RMT) and its linear eigenvalue statistics (LES) can provide an effective mathematical modeling method for exploring the statistical properties of non-stationary nonlinear systems, such as EEG signals. To obtain an accurate classification and diagnosis of schizophrenia, this paper proposes a LES-based deep learning network scheme in which a series of random matrixes, consisting of EEG data sliding window sampling and their eigenvalues, are employed as features for deep learning. Due to the fact that the performance of the LES-based scheme is sensitive to the LES’s test function, the proposed LES-based deep learning network is embedded with two ways of combining LES’s test functions with learning techniques: the first is to have the LES’s test function assigned, while, using the second way, the optimal LES’s test function should be solved in a functional optimization problem. In this paper, various test functions and different optimal learning methods were coupled in experiments. Our results revealed a binary classification accuracy of nearly 90% in distinguishing between healthy controls (HC) and patients experiencing the first episode of schizophrenia (FES). Additionally, we achieved a ternary classification accuracy of approximately 70% by including clinical high risk for psychosis (CHR). The LES-embedded approach yielded notably higher classification accuracy compared to conventional machine learning methods and standard convolutional neural networks. As the performance of schizophrenia classification is strongly influenced by test functions, a functional optimization problem was proposed to identify an optimized test function, and an approximated parameter optimization problem was introduced to limit the search area of suitable basis functions. Furthermore, the parameterization test function optimization problem and the deep learning network were coupled to be synchronously optimized during the training process. The proposal approach achieved higher classification accuracy rates of 96.87% between HC and FES, with an additional 89.06% accuracy when CHR was included. The experimental studies demonstrated that the proposed LES-based method was significantly effective for schizophrenic EEG data classification.

BackgroundThe coronavirus disease 2019 (COVID-19) pandemic has had a significant and far-reaching impact on mental health. The psychiatric emergency department (PED) is pivotal in the management of acute and severe mental illnesses, especially anxiety-and stress-related disorders.AimsThis study aimed to evaluate whether changes in the frequency or patients’ demographics of visiting the PED occurred during the COVID-19 pandemic among individuals with anxiety and stress-related disorders.MethodsThis cross-sectional study used data on PED visit counts from the largest psychiatric hospital in China between 2018 and 2020 (before and during the COVID-19 pandemic). Data from 2020, representing the COVID-19 pandemic period, were extracted from electronic medical records and compared using descriptive statistics for the same periods in 2018 and 2019.ResultsThe number of PED visits related to anxiety and stress disorders per year increased from 83 in 2018 to 136 (63.9% increase) in 2019 and 239 (188.0% increase) in 2020. Compared to that in 2018 and 2019, the proportion of PED visits in 2020 among patients with anxiety and stress disorders increased significantly. Patients with anxiety-and stress-related disorders during PED visits in 2020 were younger than those in 2018 and 2019 (three-year groups: F = 9.124, df = 2, p

ObjectivesHypertension is a predominant risk factor for aortic dissection (AD), and blood pressure (BP) control plays a vital role in the management of AD. However, the correlation between BP change and the prognosis for AD remains unclear. This study aims to demonstrate the impact of BP change patterns on AD prognosis.MethodsThis retrospective study included AD patients at two institutions (Shanghai Ninth People's Hospital Affiliated with Shanghai Jiao Tong University School of Medicine and the Vascular Department of the First Affiliated Hospital of Anhui Medical University) between 2004 and 2018. The systolic BP (SBP) change patterns of these patients were analyzed by functional data analysis (FDA). The relationship between BP change patterns and the risk of adverse events (AEs) was assessed using survival analysis.ResultsA total of 458 patients with AD were eligible for analysis. The logistic regression analysis indicated that compared with that in patients with low SBP variation (SBPV), the incidence of AEs in patients with high SBPV was significantly higher (35.84 vs. 20.35%, OR 2.19, P < 0.001). The patients were divided into four categories (accelerating rise, accelerating drop, decelerating rise, and decelerating drop) based on their SBP patterns after FDA fitting. The results of Kaplan–Meier analysis showed that at the 15- and 20-min time points, the incidence of AEs in the decelerating-drop group was significantly lower than that in the accelerating-rise group (OR 0.19, P = 0.031 and OR 0.23, P = 0.050). However, at the 25- and 30-min time points, the difference between these four groups was not significant (OR 0.26, P = 0.08 and OR 0.29, P = 0.10).ConclusionsThis study classified AD patients into four groups according to the SBP change patterns the first 30 min following admission, of which those with accelerating rises in SBP are at the highest risk of AEs, while those with decelerating drops have the best prognosis in the first 24 h after admission. Clinical practitioners may benefit from analyzing patterns of in-hospital SBP.

Abstract Background As important players in cell-to-cell communication, exosomes (exo) are believed to play a similar role in promoting fracture healing. This study investigated whether exosomes derived from bone marrow mesenchymal stem cells (BMMSC-Exos) could improve fracture healing of nonunion. Methods BMMSC-Exos were isolated and transplanted into the fracture site in a rat model of femoral nonunion (Exo group) every week. Moreover, equal volumes of phosphate-buffered saline (PBS) and exosome-depleted conditioned medium (CM-Exo) were injected into the femoral fracture sites of the rats in the control and CM-Exo groups. Bone healing processes were recorded and evaluated by radiographic methods on weeks 8, 14 and 20 after surgery. Osteogenesis and angiogenesis at the fracture sites were evaluated by radiographic and histological methods on postoperative week 20. The expression levels of osteogenesis- or angiogenesis-related genes were evaluated in vitro by western blotting and immunohistochemistry. The ability to internalize exosomes was assessed using the PKH26 assay. Altered proliferation and migration of human umbilical vein endothelial cells (HUVECs) and mouse embryo osteoblast precursor cells (MC3TE-E1s) treated with BMMSC-Exos were determined by utilizing EdU incorporation, immunofluorescence staining, and scratch wound assay. The angiogenesis ability of HUVECs was evaluated through tube formation assays. Finally, to explore the effect of exosomes in osteogenesis via the BMP-2/Smad1/RUNX2 signalling pathway, the BMP-2 inhibitors noggin and LDN193189 were utilized, and their subsequent effects were observed. Results BMMSC-Exos were observed to be spherical with a diameter of approximately 122 nm. CD9, CD63 and CD81 were expressed. Transplantation of BMMSC-Exos obviously enhanced osteogenesis, angiogenesis and bone healing processes in a rat model of femoral nonunion. BMMSC-Exos were taken up by HUVECs and MC3T3-E1 in vitro, and their proliferation and migration were also improved. Finally, experiments with BMP2 inhibitors confirmed that the BMP-2/Smad1/RUNX2 signalling pathway played an important role in the pro-osteogenesis induced by BMMSC-Exos and enhanced fracture healing of nonunion. Conclusions Our findings suggest that transplantation of BMMSC-Exos exerts a critical effect on the treatment of nonunion by promoting osteogenesis and angiogenesis. This promoting effect might be ascribed to the activation of the BMP-2/Smad1/RUNX2 and the HIF-1α/VEGF signalling pathways.

Background Impaired sensitivity of the skin flush response to niacin is one of the most replicated findings in patients with schizophrenia. However, prior studies have usually focused on postonset psychosis, and little is known about the clinical high-risk (CHR) phase of niacin sensitivity in psychosis.Aims To profile and compare the niacin flush response among CHR individuals (converters and non-converters), patients with first-episode schizophrenia (FES) and healthy controls (HCs).Methods Sensitivity to four concentrations (0.1–0.0001 M) of aqueous methylnicotinate was tested in 105 CHR individuals, 57 patients with FES and 52 HCs. CHR individuals were further grouped as converters and non-converters according to the 2-year follow-up outcomes. Skin flush response scores were rated on a 4-point scale.Results Of the 105 CHR individuals, 21 individuals were lost during the study, leaving 84 CHR individuals; 16 (19.0%) converted to full psychosis at 2 years of follow-up. Flush response scores identified in the CHR samples were characterised as modest degree levels, intermediate between those of HC individuals and patients with FES. The flush responses in the CHR group mimicked the responses observed in the FES group at higher concentrations (0.01 M, 0.1 M) and longer time points (15 min, 20 min); however, these became comparable with the responses in the HC group at the shorter time points and at lower concentrations. The converters exhibited lower mean flush response scores than the non-converters.Conclusions Attenuated niacin-induced flushing emerged during the early phase of psychosis. New devices should be developed and verified for objective quantification of skin responses in the CHR population.

Cnidium lactone is effective in the maintenance of bone mass in various osteoporosis models; however, the precise molecular mechanisms are not understood. In this study, we investigated the effects and underlying mechanisms of action of cnidium lactone on receptor activator of NF-κB ligand (RANKL)-induced osteoclastogenesis. Cnidium lactone dose-dependently inhibited osteoclast differentiation and formation, decreased the bone-resorbing activity of osteoclasts, and downregulated the expression of osteoclast differentiation marker genes. Cnidium lactone treatment considerably reduced RANKL-induced p38 MAPK and PI3K-Akt signal activity in RAW264.7 cells. The cnidium lactone-induced osteoclastogenesis was significantly attenuated by inhibition of p38 and PI3K through pretreatment with SB203580 and LY294002, respectively. Furthermore, cnidium lactone inhibited the expression of c-Fos and NFATc-1 with dose-dependently and enhanced by SB203580 and LY294002. In conclusion, cnidium lactone inhibits osteoclast differentiation through p38 MAPK and PI3K-Akt signalling pathway/c-Fos/NFATc1 signalling pathway.

This paper proposes a new deep learning framework for the location of broken insulators (in particular the self-blast glass insulator) in aerial images. We address the broken insulators location problem in a low signal-noise-ratio (SNR) setting. We deal with two modules: 1) object detection based on Faster R-CNN, and 2) classification of pixels based on U-net. For the first time, our paper combines the above two modules. This combination is motivated as follows: Faster R-CNN is used to improve SNR, while the U-net is used for classification of pixels. A diverse aerial image set measured by a power grid in China is tested to validate the proposed approach. Furthermore, a comparison is made among different methods and the result shows that our approach is accurate in real time.

Upconversion nanoparticles, which do not suffer from the photophysical artifacts that limit fluorescent molecules, offer an exciting opportunity for biological super-resolution imaging. Here, Zhan et al. develop an efficient STED mechanism using optimized lanthanide upconversion nanoparticles, enabling cytoskeleton nanoscopic imaging.

Severe spinal cord injury (SCI) is caused by external mechanical injury, resulting in unrecoverable neurological injury. Recent studies have shown that exosomes derived from bone marrow mesenchymal stem cells (BMSCs-Exos) might be valuable paracrine molecules in the treatment of SCI. In this study, we designed SCI models in vivo and in vitro and then investigated the possible mechanism of successful repair by BMSCs-Exos. In vivo, we established one Sham group and two SCI model groups. The Basso, Beattie, Bresnahan (BBB) scores showed that BMSCs-Exos could effectively promote the recovery of spinal cord function. The results of the Nissl staining, immunohistochemistry, and TUNEL/NeuN/DAPI double staining showed that BMSCs-Exos inhibited neuronal apoptosis. Western blot analysis showed that the protein expression level of Bcl-2 was significantly increased in the BMSCs-Exos group compared with the PBS group, while the protein expression levels of Bax, cleaved caspase-3, and cleaved caspase-9 were significantly decreased. The results of western bolt and qRT-PCR demonstrated that BMSCs-Exos could activate the Wnt/β-catenin signaling pathway effectively. In vitro, we found that inhibition of the Wnt/β-catenin signaling pathway could promote neuronal apoptosis following lipopolysaccharide (LPS) induction. These results demonstrated that BMSCs-Exos may be a promising therapeutic for SCI by activating the Wnt/β-catenin signaling pathway.

Lanthanide-doped upconversion nanoparticles (UCNPs) are promising bioimaging nanoprobes due to their excellent photostability. As one of the most commonly used lanthanide activators, Tm3+ ions have perfect ladder-type electron configuration and can be directly excited by bio-friendly near-infrared-II (NIR-II) wavelengths. Here, the emission characteristics of Tm3+-doped nanoparticles under laser excitations of different near-infrared-II wavelengths were systematically investigated. The 1064 nm, 1150 nm, and 1208 nm lasers are proposed to be three excitation strategies with different response spectra of Tm3+ ions. In particular, we found that 1150 nm laser excitation enables intense three-photon 475 nm emission, which is nearly 100 times stronger than that excited by 1064 nm excitation. We further optimized the luminescence brightness after investigating the luminescence quenching mechanism of bare NaYF4: Tm (1.75%) core. After growing an inert shell, a ten-fold increase of emission intensity was achieved. Combining the advantages of NIR-II wavelength and the higher-order nonlinear excitation, a promising facile excitation strategy was developed for the application of thulium-doped upconversion nanoparticles in nanoparticles imaging and cancer cell microscopic imaging.

Enzyme inhibitors from natural products are becoming an attractive target for drug discovery and development; however, separating enzyme inhibitors from natural-product extracts is highly complex. In this study, we developed a strategy based on tyrosinase-site blocking ultrafiltration integrated with HPLC-QTOF-MS/MS and optimized molecular docking to screen tyrosinase inhibitors from Puerariae lobatae Radix extract. Under optimized ultrafiltration parameters, we previously used kojic acid, a known tyrosinase inhibitor, to block the tyrosinase active site in order to eliminate false-positive results. Using this strategy, puerarin, mirificin, daidzin and genistinc were successfully identified as potential ligands, and after systematic evaluation by several docking programs, the rank of the identified compounds predicted by computational docking was puerarin > mirificin > kojic acid > daidzin ≈ genistin, which agreed with the results of tyrosinase-inhibition assays. Structure-activity relationships indicated that C-glycosides showed better tyrosinase inhibition as compared with O-glycosides, with reduced inhibition achieved through the addition of glycosyl, which provides ideas about the screen of leading compounds and structural modification.