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Abstract Background Zinc oxide nanoparticle (ZnO NP) is one of the metal nanomaterials with extensive use in many fields such as feed additive and textile, which is an emerging threat to human health due to widely distributed in the environment. Thus, there is an urgent need to understand the toxic effects associated with ZnO NPs. Although previous studies have found accumulation of ZnO NPs in testis, the molecular mechanism of ZnO NPs dominated a decline in male fertility have not been elucidated. Results We reported that ZnO NPs exposure caused testicular dysfunction and identified spermatocytes as the primary damaged site induced by ZnO NPs. ZnO NPs led to the dysfunction of spermatocytes, including impaired cell proliferation and mitochondrial damage. In addition, we found that ZnO NPs induced ferroptosis of spermatocytes through the increase of intracellular chelatable iron content and lipid peroxidation level. Moreover, the transcriptome analysis of testis indicated that ZnO NPs weakened the expression of miR-342-5p, which can target Erc1 to block the NF-κB pathway. Eventually, ferroptosis of spermatocytes was ameliorated by suppressing the expression of Erc1. Conclusions The present study reveals a novel mechanism in that miR-342-5p targeted Erc1 to activate NF-κB signaling pathway is required for ZnO NPs-induced ferroptosis, and provide potential targets for further research on the prevention and treatment of male reproductive disorders related to ZnO NPs. Graphical Abstract

Objective When urban rail transit trains operate on elevated tracks, crosswinds have a significant impact on the stability and safety of vehicle operation. It is essential to conduct a more in-depth study on the dynamics performance of metro vehicles under crosswind action. Method Three classical crosswind load models (classical constant steady-state wind load model, transient Chinese hat wind load model, and non-steady-state stochastic wind load model) in MATLAB software are employed to simulate dynamic wind fields. In SIMPACK software, a detailed rigid-body dynamics model of a single-car formation train is established. The three wind load models are applied as external excitations to the vehicle. Comparative analysis is conducted on the influence of different wind load models, wind speeds, and vehicle speeds on the operational stability of metro vehicles when operating on straight tracks. From a safety design perspective, the non-steady-state stochastic wind load model is selected to analyze the stability and safety of the vehicle during curve negotiation, exploring the safety domain of vehicle speeds under different wind speeds. Result & Conclusion Under crosswind action, both metro vehicle speed and wind speed significantly influence vehicle operational stability and safety. During train high-speed running, wind speed has a more pronounced impact on the lateral vibration of the vehicle. Among the three crosswind load models, the non-steady-state stochastic wind load model has the most significant impact on vehicle stability. Based on the analysis of vehicle stability and safety, speed limits for vehicles are determined for different route geometries (straight or curve tracks) and wind speeds.

Applying a quantum cascade laser as a light source in a benchtop infrared spectrometer has enabled a new strategy for microplastic analysis. This strategy uses discrete frequency infrared images to recognize particles from a reflecting substrate prior to chemical identification, thereby improving analysis throughput.This research work established the performance of the approach using an Agilent Laser Direct Infrared chemical imaging system (LDIR). Over 90 % of fluorescently labelled polyethylene particles (> 20 µm) spiked in the environmental matrix were correctly identified at scanning wavenumbers and sensitivity settings. When the high-throughput setting was used, the discrete frequency infrared image-guided microplastic analysis demonstrated good selectivity. With around 30 % of particles in the sample scanned, the technique could recognize around 86 % percent of fluorescently labelled polyethylene particles introduced.The MirrIR low-e slide and gold-coated filter are suitable reflecting substrates for this particle analysis strategy. When detecting small microplastic particles with sizes between 5 and 20 µm, around 94 % spiked polymethyl methacrylate particles could be found on the MirrIR low-e slide. The analysis also showed good accuracy and precision in a three-day test. The texture on the gold-coated filter surface could interfere with the small particle analysis.Overall, this novel discrete frequency infrared image-guided particle analysis shows good accuracy, high precision, and respectable selectivity, which are all critical characteristics for microplastic analysis.

Summary: Background: Toripalimab, a novel PD-1 antibody, is approved for treatment of multiple solid tumors; however, its neoadjuvant use with chemotherapy for triple-negative breast cancer (TNBC) remains unevaluated. Additionally, induction chemotherapy followed by de-escalation of neoadjuvant immunotherapy remains underexplored. Therefore, we conducted a phase II trial investigating a novel neoadjuvant chemoimmunotherapy regimen including de-escalation of immunotherapy for early-stage TNBC. Methods: Chemotherapy and anti-PD-1 therapy were sequentially administered in a neoadjuvant setting to female patients with histologically confirmed stage II–III TNBC between June 9, 2020, and March 24, 2022. Patients received neoadjuvant therapy with four cycles of epirubicin-cyclophosphamide every 2 weeks, followed by toripalimab (240 mg) every 3 weeks plus nab-paclitaxel weekly for 12 weeks. The primary endpoint was total pathological complete response (tpCR; ypT0/is ypN0). Key secondary endpoints included breast pCR (bpCR; ypT0/is), event-free survival and biomarker analysis. Safety was also assessed. This study was registered with ClinicalTrials.gov (NCT04418154). Findings: Among 70 enrolled patients (median age, 51 years; 62.9% stage III), 66 completed treatment without progression and subsequently underwent surgery. The percentages of patients with a tpCR and bpCR were 39 of 70 (55.7%, 95% confidence interval [CI]: 43.3–67.6) and 41 of 70 (58.6%, 95% CI 46.2–70.2), respectively. Sixteen (22.9%) patients experienced grade ≥3 adverse events (AEs), frequently neutropenia (12, 17.1%) and leukopenia (11, 15.7%). The most common immune-related AE was hypothyroidism (5, 7.1%, all grade 1–2). Interpretation: Including 12 weeks of toripalimab in neoadjuvant chemotherapy conferred encouraging activity and manageable toxicity in patients with early TNBC, and this regimen warrants further investigation. Funding: National Natural Science Foundation of China, Junshi Biosciences, and Jiangsu Hengrui Pharmaceuticals.

Abstract Conventional food production is restricted by energy conversion efficiency of natural photosynthesis and demand for natural resources. Solar-driven artificial food synthesis from CO2 provides an intriguing approach to overcome the limitations of natural photosynthesis while promoting carbon-neutral economy, however, it remains very challenging. Here, we report the design of a hybrid electrocatalytic−biocatalytic flow system, coupling photovoltaics-powered electrocatalysis (CO2 to formate) with five-enzyme cascade platform (formate to sugar) engineered via genetic mutation and bioinformatics, which achieves conversion of CO2 to C6 sugar (L-sorbose) with a solar-to-food energy conversion efficiency of 3.5%, outperforming natural photosynthesis by over three-fold. This flow system can in principle be programmed by coupling with diverse enzymes toward production of multifarious food from CO2. This work opens a promising avenue for artificial food synthesis from CO2 under confined environments.

Accurate identification of fungal species is crucial for mycological research, relying significantly on experienced taxonomists’ ability to recognize fungal morphology. With the dwindling number of taxonomists, developing a rapid, accurate, and automated fungal identification method is essential, promising to markedly decrease the time and resources needed for such tasks. To address the challenges in the automatic identification of nematode-trapping fungi (NTF, belong to Orbiliaceae, Orbiliomycetes, a group of filamentous fungi that catch nematodes in soil), specifically focusing on their conidia which are often aggregated and morphologically similar, we developed an enhanced method. Our approach integrates an efficient channel attention (ECA) mechanism within the backbone layer of YOLOv8l, improving the algorithm’s capability to detect global information effectively. This adjustment significantly optimizes the detection and differentiation of limited features and small-sized conidia among aggregated samples. The experimental outcomes demonstrated that our method increases precision by 0.3%, recall by 1.8%, and mean average precision (mAP) by 0.4%, surpassing the performance of existing target detection algorithms. In addition, this method accurately identified the aggregated fungal conidia and distinguished morphologically similar spores. In conclusion, the system accurately detects and recognizes the conidia of NTF in mixed spore environments, thus providing crucial technical support for the automatic detection and early prediction of NTF.

In this study, an accurate tool is provided for the evaluation of the effect of joint motion effect on gait stability. This quantitative gait evaluation method relies exclusively on the analysis of data acquired using acceleration sensors. First, the acceleration signal of lower limb motion is collected dynamically in real-time through the acceleration sensor. Second, an algorithm based on improved dynamic time warping (DTW) is proposed and used to calculate the gait stability index of the lower limbs. Finally, the effects of different joint braces on gait stability are analyzed. The experimental results show that the joint brace at the ankle and the knee reduces the range of motions of both ankle and knee joints, and a certain impact is exerted on the gait stability. In comparison to the ankle joint brace, the knee joint brace inflicts increased disturbance on the gait stability. Compared to the joint motion of the braced side, which showed a large deviation, the joint motion of the unbraced side was more similar to that of the normal walking process. In this paper, the quantitative evaluation algorithm based on DTW makes the results more intuitive and has potential application value in the evaluation of lower limb dysfunction, clinical training and rehabilitation.

Abstract Programmable artificial photosynthetic cell is the ultimate goal for mimicking natural photosynthesis, offering tunable product selectivity via reductase selection toward device integration. However, this concept is limited by the capacity of regenerating the multiple cofactors that hold the key to various reductases. Here, we report the design of artificial photosynthetic cells using biotic–abiotic thylakoid–CdTe as hybrid energy modules. The rational integration of thylakoid with CdTe quantum dots substantially enhances the regeneration of bioactive NADPH, NADH and ATP cofactors without external supplements by promoting proton-coupled electron transfer. Particularly, this approach turns thylakoid highly active for NADH regeneration, providing a more versatile platform for programming artificial photosynthetic cells. Such artificial photosynthetic cells can be programmed by coupling with diverse reductases, such as formate dehydrogenase and remodeled nitrogenase for highly selective production of formate or methane, respectively. This work opens an avenue for customizing artificial photosynthetic cells toward multifarious demands for CO2 conversion.

The dry beach length determines the hydraulic boundary of tailings impoundments and significantly impacts the infiltration line, which is crucial for the tailings dam. A deep learning method utilizing satellite images is presented to recognize the dry beach area and accurately measure the length of dry beaches in tailing ponds. Firstly, satellite images of various tailing ponds were gathered and the collection was enlarged to create a dataset of satellite images of tailing ponds. Then, a deep learning method was created using YOLOv5-seg to identify the dry beach area of tailing ponds from satellite images. The mask of the dry beach region was segmented and contour extraction was then carried out. Finally, the beach crest line was fitted based on the extracted contour. The pixel distance between the beach crest line and the dry beach boundary was measured and then translated into real distance by ground resolution. This paper’s case study compared the calculated length of dry beach with the real length obtained by field monitoring. The results of the case study showed that the minimum error of the method was 2.10%, the maximum error was 3.46%, and the average error was 2.70%, indicating high precision for calculating dry beach length in tailing ponds.

BackgroundCircumcision as a common elective pediatric surgery worldwide is a stressful and anxiety-inducing experience for parents and children. Although current perioperative interventions proved effective, such as reducing preoperative anxiety, there are limited holistic solutions using mobile apps. ObjectiveThis paper aims to describe the development and primary evaluation of an intelligent customer-driven smartphone-based app program (ICory-Circumcision) to enhance health outcomes among children undergoing circumcision and their family caregivers. MethodsBased on the review of the literature and previous studies, Bandura’s self-efficacy theory was adopted as the conceptual framework. A multidisciplinary team was built to identify the content and develop the apps. Semistructured interviews were conducted to evaluate the ICory-Circumcision. ResultsThe ICory-Circumcision study was carried out from March 2019 to January 2020 and comprised 2 mobile apps, BuddyCare app and Triumf Health mobile game app. The former provides a day-by-day perioperative guide for parents whose children are undergoing circumcision, while the latter provides emotional support and distraction to children. In total, 6 participants were recruited to use the apps and interviewed to evaluate the program. In total, 4 main categories and 10 subcategories were generated from content analysis. ConclusionsICory-Circumcision seemed to lean toward being useful. Revisions to ICory-Circumcision are necessary to enhance its contents and features before advancing to the randomized controlled trial. Trial RegistrationClinicalTrials.gov NCT04174404; https://clinicaltrials.gov/ct2/show/NCT04174404

Abstract Chemokines secreted by dendritic cells (DCs) play a key role in the regulation of inflammation and autoimmunity through chemokine receptors. However, the role of chemokine receptor CXCR1 in inflammation-inducing experimental autoimmune encephalomyelitis (EAE) and acute respiratory distress syndrome (ARDS) remains largely enigmatic. Here we reported that compared with healthy controls, the level of CXCR1 was aberrantly increased in multiple sclerosis (MS) patients. Knockout of CXCR1 not only ameliorated disease severity in EAE mice but also suppressed the secretion of inflammatory factors (IL-6/IL-12p70) production. We observed the same results in EAE mice with DCs-specific deletion of CXCR1 and antibody neutralization of the ligand CXCL5. Mechanically, we demonstrated a positive feedback loop composed of CXCL5/CXCR1/HIF-1α direct regulating of IL-6/IL-12p70 production in DCs. Meanwhile, we found CXCR1 deficiency in DCs limited IL-6/IL-12p70 production and lung injury in LPS-induced ARDS, a disease model caused by inflammation. Overall, our study reveals CXCR1 governs DCs-mediated inflammation and autoimmune disorders and its potential as a therapeutic target for related diseases.

Natural dyes are widely available and mainly extracted from natural plants with green, safe, and sustainable characteristics. This study used Cinnamomum camphora fruit peel as the raw material under the premise of minimizing chemical reagents and employed the microwave-extraction method to extract the dyes from C. camphora fruit peel. The dye extraction conditions were optimized using response surface software via the Box-Behnken model to formulate a response surface test protocol. The results showed that optimizing the experimental conditions by the response surface method was reliable. The optimal extraction condition was found to be material-liquid ratio 1:20 (g: mL), microwave time 90 s, and microwave power 420 W. It is of practical application value to improve the extraction quantity of C. camphora fruit dye. At the same time, the infrared spectrum and HPLC-MS analysis of Cinnamomum camphora fruit dye were analyzed, and the stability of the dye was tested.

This study proposes an effective method for improving the determination accuracy of the viscoelastic constitutive model via the combination of uniaxial creep with stress relaxation and the use of creep data to correct stress relaxation curves, known as “stress relaxation and creep correction (SRCC).” A typical molding glass was selected as the study material to fit and correct the constitutive model using the new method, and glass was regarded as a thermorheologically simple material to describe the temperature-dependent viscoelasticity. Glass cylinders were used in uniaxial compression stress relaxation experiments, which were conducted on a molding machine, and the experimental results were fitted with the generalized Maxwell constitutive model. The constitutive model was corrected further by optimization of the long-term modulus based on the SRCC method, and friction coefficients were calibrated under different molding conditions to improve the simulation accuracy. To verify the accuracy of the corrected constitutive model, we constructed a two-dimensional axisymmetric numerical model of molding biconvex aspheric lens and extracted and compared the molding displacement curves with experimental data. The results indicate that more accurate guidance for the optimization of the glass molding process can be provided based on the corrected model.

Deep learning techniques, especially convolutional neural networks (CNNs), have dramatically boosted the performance of statistical downscaling. In this study, we propose a CNN-based 2 m air temperature downscaling model named Terrain-Guided Attention Network (TGAN), which aims at rebuilding 2 m air temperature distribution from 0.0625$^{\circ }$ to 0.01$^{\circ }$ over Southwest China. More concretely, TGAN utilizes two upsampling modules to progressively reconstruct the high-resolution temperature data from the low-resolution one. Then, to better recover the spatial detail of the low-resolution temperature data, an attentive-terrain block is proposed to introduce digital terrain model (DEM) information. It aggregates the temperature data and the corresponding-scale DEM information via the attention mechanism in a multiscale manner. Ultimately, the reconstruction module is employed to obtain the high-resolution temperature data. We use the 2019 data for training, and utilize the 2018 data to verify the effectiveness of the proposed TGAN. The experimental results showed that TGAN achieved the lowest root-mean-square error (1.12$\,^\circ$C) when incorporating DEM data by attentive-terrain blocks in a multiscale manner, followed by incorporating DEM data in a multiscale manner (TGAN-land, 1.31$\,^\circ$C) and only incorporating DEM data (SRCNN-land, 1.36$\,^\circ$C). Meanwhile, TGAN showed a competitive performance when compared with several advanced deep-learning-based super-resolution algorithms and reconstructed the texture details of 2 m air temperature fields more clearly. In general, among various deep learning approaches, TGAN achieves better downscaling results for 2 m air temperature reconstruction and provides a practical method and guidance for the back-calculation of high-resolution historical meteorological grid data.

BackgroundThe latissimus dorsi flap (LDF) is the most commonly used autologous flap for breast reconstruction (BR) in China. We conducted this study to explore the current status of BR using LDF with/without implants.MethodsThis study was a single-center retrospective study that included breast tumor patients who underwent LDF breast reconstruction at Fudan University Shanghai Cancer Center (FUSCC) between 2000 and 2021.ResultsWe analyzed 4918 patients who underwent postmastectomy BR, including 1730 patients (35.2%) with autologous flaps. LDF was used for BR in 1093 (22.2%) patients, and an abdominal flap was used in 637 (13.0%) patients. The proportion of LDFs used in autologous BR patients decreased each year and dropped to approximately 65.0% after 2013 due to the increased use of abdominal flaps. Among these patients, 609 underwent extended LDF (ELDF) BR, 455 underwent LDF BR with implants, and 30 received a LDF as a salvage flap due to previous flap or implant failure. Patients who underwent ELDF reconstruction were older and had a higher BMI than those who received a LDF with implants. There was no significant difference in the mean postoperative hospital stay, neoadjuvant chemotherapy rates, or adjuvant radiotherapy rates between the two groups. Major complications requiring surgical intervention occurred in 25 patients (2.29%). There was no significant difference in the incidence of major complications between the two groups (P=0.542).ConclusionsLDF breast reconstruction is a well-developed and safe procedure. The duration of postoperative hospitalization nor the incidence of major complications was affected by implant use.

Purpose: Changes in biological features and functional status make management decisions in older women with primary breast cancer complicated. We aimed to provide an overview of the clinicopathological characteristics and survival outcomes of older breast cancer patients based on the current treatment strategies. Methods: Female patients diagnosed with primary invasive breast cancer at Fudan University Shanghai Cancer Centre from 2008 to 2016 were included. Patients were divided into a younger group (

As the core of the heating, ventilation, and air-conditioning (HVAC) control system, the sensor directly determines whether the HVAC control system is running normally. Poor natural and artificial environments can cause sensors to simultaneously fault, called multi-source sensor faults. Compared to the other methods, the Gaussian mixture model (GMM) has a better effect on high-dimensional data classification. In this paper, an improved ensemble empirical mode decomposition hard threshold denoising (EEMD-HTD) is first proposed to denoise the collected data and highlight each sensor’s fault characteristics. Secondly, the single-source and multi-source faults of the sensors are analyzed by the GMM at different points. The above fault diagnosis processes are collectively referred to as EEMD-HTD-GMM. Experimental results show that the EEMD-HTD-GMM has a higher diagnostic ability and distinguishes between the fault types that cause GMM classification confusion.

Abstract Background The medical damage litigations after knee arthroplasty are on the rise year by year. However, few studies examined the litigation after knee arthroplasty. This study analyzed the litigation of medical damage after knee replacement in the past ten years based on a Chinese database. It synthesized the focus of the dispute in these cases to provide a reference for doctors to reduce the risk of litigation. Methods Retrospectively analyzed were medical damage litigations after total knee arthroplasty in the past ten years (June 2011–June 2021) from the "Wolters Kluwer Legal Information Database". The data collected included the characteristics of patients, causes of litigation, the results of litigation and the amount of compensation. Results A total of 110 litigation cases were analyzed, including 40 male patients (36.3%) and 70 female patients (63.6%). The top cause of litigation was infection (43.6%). The most common factor leading to the doctor losing the case was "complications caused by operational error" (P < 0.05). Complications, such as amputation, postoperative ischemic stroke and infection, were more likely to result in higher compensation. Conclusions The prevention of infection and the avoidance of operational errors are very important in avoiding medical litigations. Moreover, avoiding disabling complications or a protracted course of disease could significantly reduce the amount of compensation. In addition, full and reasonable communication, paying full attention to the reaction of patients, and timely diagnosis could also effectively minimize the risk of litigation and loss.

Objective: Sentinel lymph node biopsy (SLNB) is currently the standard of care in clinically node negative (cN0) breast cancer. The present study aimed to evaluate the negative predictive value (NPV) of 18F-FDG dedicated lymph node positron emission tomography (LymphPET) in cN0 patients. Methods: This was a prospective phase II trial divided into 2 stages (NCT04072653). In the first stage, cN0 patients underwent axillary LymphPET followed by SLNB. In the second stage, SLNB was omitted in patients with a negative preoperative axillary assessment after integration of LymphPET. Here, we report the results of the first stage. The primary outcome was the NPV of LymphPET to detect macrometastasis of lymph nodes (LN-macro). Results: A total of 189 patients with invasive breast cancer underwent LymphPET followed by surgery with definitive pathological reports. Forty patients had LN-macro, and 16 patients had only lymph node micrometastasis. Of the 131 patients with a negative LymphPET result, 16 patients had LN-macro, and the NPV was 87.8%. After combined axillary imaging evaluation with ultrasound and LymphPET, 100 patients were found to be both LymphPET and ultrasound negative, 9 patients had LN-macro, and the NPV was 91%. Conclusions: LymphPET can be used to screen patients to potentially avoid SLNB, with an NPV > 90%. The second stage of the SOAPET trial is ongoing to confirm the safety of omission of SLNB according to preoperational axillary evaluation integrating LymphPET.

Purpose: Neoadjuvant therapy (NAT) is considered the standard of care for patients with HER2-positive breast cancer (BC). However, there is no proven survival benefit of NAT compared to adjuvant therapy for the survival of patients with early-stage HER2-positive BC. This study aimed to compare the prognosis of HER2-positive BC patients treated with NAT to that of patients treated with adjuvant therapy. Methods: This was a single-center real-world retrospective study. This study analyzed the disease-free survival (DFS) and overall survival (OS) of 538 HER2-positive BC patients treated with neoadjuvant therapy and 2684 patients treated with adjuvant therapy at Fudan University Shanghai Cancer Center (FUSCC) between 2012 and 2016. Patients with a clinical tumor size (cT) ≤5 cm or >5 cm were matched using the propensity score matching (PSM) method to prevent selection bias. Results: After PSM, among patients with cT ≤ 5 cm, there was no significant difference in DFS (P = 0.08) or OS (P = 0.11) between the two groups. The analysis of survival outcomes of patients treated with neoadjuvant and adjuvant therapy in the different chemotherapy subgroups yielded consistent results. According to multivariate analysis, lymph node status and response to NAT showed independent prognostic value for OS and DFS. Among patients with cT > 5 cm, the DFS (P = 0.25) and OS (P = 0.57) of patients treated with NAT were similar to those of patients treated with adjuvant therapy after PSM. Conclusion: We confirmed the equivalent effects of adjuvant therapy and NAT in HER2-positive BC patients. Neoadjuvant therapy should be used for patients with HER2-positive BC.

Aluminium composites have been extensively developed for automotive applications due to their high specific strength. However, particle agglomeration, high porosity content, and weak reinforcement/matrix interface bond are prone to occur in the casting process. These arise during the introduction of the reinforcement, mould filling, and solidification process, especially when the particle sizes are approaching the nanoscale and are detrimental, with respect to the mechanical properties. By applying high pressure in conjunction with high cooling rates, an improved distribution of the reinforcing particles can be expected, as the high pressure improves the filling capacity of the composite melt, in which the fluidity is generally decreased by the added heterogeneous particles. The fine grain structure obtained under the high cooling rate is also beneficial for the distribution of the reinforcing nanoparticles during solidification. In this study, SiC nano-reinforced AlSi9Cu3 composites were developed by employing an Al–Cu-SiCnp master alloy, stir mixing, ultrasonication and HPDC technology. The findings showed a good distribution of individual SiC particles, resulting in a reduction of ∼40% in the α-Al grain size and near 10% increment in the yield strength, which was attributed to grain refinement, CTE strengthening and Orowan strengthening. Compared to commercial AlSi9Cu3 HPDC alloys, the developed AlSi9Cu3-1wt% SiCnp composite provided an improved YS of ∼187 MPa and a UTS of ∼350 MPa in the as-cast state and the milestone high YS and UTS of ∼370 MPa and ∼468 MPa under a T6 condition, respectively.

The laser-assisted diamond turning (LADT) method can effectively improve the machinability of hard and brittle materials based on the laser heating effect, resulting in prolonged diamond tool life and better surface integrity. However, due to the incomplete absorption of laser beam energy within the workpiece cutting zone, simultaneous heating of the tool holder occurs, resulting in a structural thermal expansion that affects the workpiece form accuracy. In this article, the form accuracy of a LADT-machined workpiece was systematically studied. Accurate calculations of the tool shank and tool holder thermal fields and thermal expansion were performed using thermodynamic coupled finite element analysis. In addition, the LADT tool path was precisely pre-compensated by taking into account the structure expansion. The experimental results demonstrate that the form accuracy can be significantly improved with a pre-compensated tool path, which provides crucial technical support for achieving a high-precision finish on optical elements using the LADT method.

Abstract DC/DC converters are ubiquitous in renewable energies such as photovoltaic power systems. A novel and general approach is proposed that consists of three matching principles, which enables one to assign a best set of energy storage elements to a DC/DC converter to meet both desirable transients and small ripples, facilitating the design of a controlled DC/DC system. First, the principle of specification matching introduces a ‘virtual’ lower order model to meet the prescribed specifications. Second, the rule of order matching augments the ‘virtual’ model to a higher order, the same as that of the actual DC/DC circuit if necessary. Third, the principle of parameter matching helps derive desirable element parameters through solving an optimization nonlinear function with constraints. These principles are realized through a specific design method for boost DC/DC converters of a photovoltaic power system. Given some specifications, a numerical optimal design is executed to verify the effectiveness of the specific design tool as well as the proposed theory. A photovoltaic power system with the designed boost DC/DC converter is implemented in both the simulated environment and the physical environment.The experimental results show that both transient responses and steady state responses (or ripples) meet the given specifications. The proposed theory can either work alone in simple designs or assist in advanced control designs for a better performance.

BackgroundNeoadjuvant chemotherapy (NAC) has evolved significantly and has been widely accepted for downstaging disease in early-stage and locally advanced breast cancer patients. Since the optimal surgical intervention for patients receiving NAC remains controversial, we aim to investigate the survival outcome of patients treated with different surgical management.MethodsA retrospective, nested case-control study was conducted in patients with invasive breast cancer that underwent NAC at Fudan University Shanghai Cancer Center from January 2010 to June 2019. Based on surgical intervention, patients were divided into mastectomy and breast conservation groups. Patients were matched on age at diagnosis, menopausal status, the year of the surgery, post neoadjuvant therapy pathological tumor (ypT) stage, post neoadjuvant therapy pathological node (ypN) stage, molecular subtypes, and axillary surgery by propensity score matching.ResultsA total of 2080 patients were enrolled in this study. Among them, 1819 (87.5%) patients were categorized as mastectomy group, and 261 (12.5%) patients were classed as breast conservation group. Over 9-years of research, the proportion of breast conservation steadily increased in patients after NAC. Data showed that younger (P

Abstract Background Mortality and complications remain high after acute type A aortic dissection (ATAAD) open surgery and are associated with coagulation dysfunction. Platelets play an important role in the process of coagulation. This study explored the relationship between postoperative platelet counts and 3-year mortality after operation in patients with ATAAD undergoing open aortic repair surgery. Methods Patients with ATAAD who underwent Total Arch Replacement and Frozen Elephant Trunk in Fuwai Hospital from 2011 to 2015 were selected for this study. The perioperative data were collected and sorted through the electronic clinical case system. Multivariate Logistic regression was used to analyze the risk factors for death within three years after surgery. Results A total of 495 patients were included in the analysis. After correction for confounding factors, decreased postoperative platelet count remained an independent factor that was associated with lower mortality (OR = 0.918, 95% CI 0.853–0.988, P = 0.023). Conclusions The study indicated that decreased postoperative platelet count may lead to increased 3-year mortality, in patients with ATAAD who underwent open aortic repair surgery.

Breast cancer as the most common cancer in women has become the leading cause of cancer death for women. Although many inflammatory factors increase the risk of breast cancer, there are very few studies on the mechanisms by which inflammation affects the initiation and progression of breast cancer. Here, we profiled and compared the transcriptome of normal tissues, inflammatory breast tissues, benign breast tumors, and malignant breast tumors. To find key regulatory factors, a protein interaction network between characteristic modules in inflammatory lesions and ER-negative (ER−) breast cancer was constructed and inflammation-cancer interface genes were identified. We found that the transcriptional profile of inflammatory breast tissues was similar with ER− malignant tumors, featured with low ER expression levels and similar immune signaling pathway activation. Through comprehensive protein network analysis, we identified the interface genes and chemokine signaling pathway that have the potential to promote inflammatory cancer transformation. These interface genes could be used as a risk factor to provide a certain basis for the clinical early detection and treatment of breast cancer. This is the first study to explore the association between breast inflammatory lesions and breast cancer at the transcriptome level. Our inflammation data and research results provide a basis for future inflammation-cancer transformation analysis.

The reform of tertiary education teaching promotes teachers to adjust timely teaching plans based on students’ learning feedback in order to improve teaching performance. Thefore, learning score prediction is a key issue in process of the reform of tertiary education teaching. With the development of information and management technologies, a lot of teaching data are generated as the scale of online and offline education expands. However, a teacher or educator does not have a comprehensive dataset in practice, which challenges his/her ability to predict the students’ learning performance from the individual’s viewpoint. How to overcome the drawbacks of small samples is an open issue. To this end, it is desirable that an effective artificial intelligent tool is designed to help teachers or educators predict students’ scores well. We propose a boosting and decision-tree-regression-based score prediction (BDTR-SP) model, which relies on an ensemble learning structure with base learners of decision tree regression (DTR) to improve the prediction accuracy. Experiments on small samples are conducted to examine the important features that affect students’ scores. The results show that the proposed model has advantages over its peer in terms of prediction correctness. Moreover, the predicted results are consistent with the actual facts implied in the original dataset. The proposed BDTR-SP method aids teachers and students to predict students’ performance in the on-going courses in order to adjust the teaching and learning strategies, plans and practices in advance, enhancing the teaching and learning quality. Therefore, the integration of information technology and artificial intelligence into teaching and learning practices is able to push forward the reform of tertiary education teaching.

Recurrent bacterial infection is a major problem that threatens the tissue repair process. However, most current therapeutic strategies fail to deal with management of the overlap dynamics of bacterial killing and tissue repair. Here, in accord with the different responses of eukaryotic and prokaryotic cells to electric potential, we developed high performance photoelectric BiOCl nanosheets that dynamically switch between conditions that favor either tissue regrowth or antibacterial microenvironments due to light stimulated and bi-modal switching of their surface electrical polarization. In vitro assays demonstrate that, under light illumination, the mannitol modified BiOCl nanosheets show high relative surface potential and achieve robust antibacterial performance. Conversely, under dark conditions, the nanosheets exhibit relatively low surface potential and promote Bone Marrow Stem Cell (BMSCs) proliferation. In vivo studies indicate that BiOCl nanosheets with light switch capabilities promote the significant regeneration of infected skin wounds. This work offers a new insight into treating recurrent bacterial infections with photoelectric biomaterials for light controlled selection of alternative electrical microenvironments, thereby benefiting the capability for either antisepsis or repair of damaged tissues.

IntroductionWhile tranexamic acid (TXA) is widely used in patients with acute type A aortic dissection (ATAAD) who undergo surgical repair to reduce blood loss and transfusion requirement, the optimal dosage of TXA is unknown in these patients.Materials and MethodsThis was a retrospective cohort study that compared high-dose (>50 mg/kg) and low-dose TXA (≤50 mg/kg) in patients with ATAAD who underwent surgical repair. Propensity score matching (PSM) was performed between the two groups and results were analyzed in matched cases. The primary outcome was postoperative blood loss within 3 days after surgery. The secondary outcomes were total blood loss after surgery and perioperative blood transfusion, and safety outcomes were also assessed.ResultsThrough medical record screening, 529 patients were identified. After PSM, 196 patients in the high-dose group and 196 patients in the low-dose group were matched and included in the final analysis. Postoperative blood loss in 3 days after surgery was 940 mL (710–1,010 mL) in the low-dose group and 695 mL (620–860 mL) in the high-dose group. The difference was statistically significant (P

PurposeTo evaluate the efficacy of the Sanyin formula (SYF) plus conventional standard chemotherapy in operable triple-negative breast cancer (TNBC) patients, a randomized controlled trial was implemented at 5 hospitals and cancer centers in China between May 23, 2016, and October 31, 2019.Materials and MethodsFemale patients aged 18 to 80 years with operable TNBC after definitive surgery were screened and enrolled. The exclusion criteria included metastatic disease, other tumors, or locally advanced disease. Patients were randomly divided into groups SYF plus conventional standard chemotherapy and placebo plus conventional standard chemotherapy at a ratio of 1:1. The primary endpoint of the investigation was disease-free survival (DFS), and secondary endpoints included overall survival (OS) and toxicity.ResultsA total of 252 operable female TNBC patients were randomized to receive SYF plus conventional standard chemotherapy (N = 127) or a placebo plus conventional standard chemotherapy (N = 125). At a median follow-up of 51 months, 5-year DFS time was longer in those assigned to SYF plus conventional standard chemotherapy compared with placebo plus conventional standard chemotherapy (94.2%vs 85.5%, hazard ratio [HR] = 0.40; 95%CI, 0.17-0.97; P = 0.034). The absolute benefit for 5-year DFS was 8.7% in the SYF plus conventional standard chemotherapy group. No statistically significant difference was observed in OS between the two groups (P = 0.23). Patients with negative node status benefited more from SYF plus conventional standard chemotherapy treatment (HR = 0.21, P-interaction = 0.013) in accordance with the exploratory subgroup analyses of DFS.ConclusionsThe results of the present study suggest that the traditional Chinese medicine SYF plus conventional chemotherapy regimens is an effective alternative adjuvant chemotherapy strategy for female operable TNBC patients.Clinical Trial Registrationhttps://www.chictr.org.cn/searchproj.aspx, identifier ChiCTR-IPR-16008590.

BackgroundElective colorectal cancer (CRC) surgeries offer enhanced surgical outcomes but demand high self-efficacy in prehabilitation and competency in self-care and disease management postsurgery. Conventional strategies to meet perioperative needs have not been pragmatic, and there remains a pressing need for novel technologies that could improve health outcomes. ObjectiveThe aim of this paper was to describe the development of a smartphone-based interactive CRC self-management enhancement psychosocial program (iCanManage) in order to improve health outcomes among patients who undergo elective CRC surgeries and their family caregivers. MethodsA multidisciplinary international team comprising physicians, specialist nurses, a psychologist, software engineers, academic researchers, cancer survivors, patient ambassadors, and ostomy care medical equipment suppliers was formed to facilitate the development of this patient-centric digital solution. The process occurred in several stages: (1) review of current practice through clinic visits and on-site observations; (2) review of literature and findings from preliminary studies; (3) content development grounded in an underpinning theory; (4) integration of support services; and (5) optimizing user experience through improving interface aesthetics and customization. In our study, 5 participants with CRC performed preliminary assessments on the quality of the developed solution using the 20-item user version of the Mobile App Rating Scale (uMARS), which had good psychometric properties. ResultsBased on the collected uMARS data, the smartphone app was rated highly for functionality, aesthetics, information quality, and perceived impact, and moderately for engagement and subjective quality. Several limiting factors such as poor agility in the adoption of digital technology and low eHealth literacy were identified despite efforts to promote engagement and ensure ease of use of the mobile app. To overcome such barriers, additional app-training sessions, an instruction manual, and regular telephone calls will be incorporated into the iCanManage program during the trial period. ConclusionsThis form of multidisciplinary collaboration is advantageous as it can potentially streamline existing care paths and allow the delivery of more holistic care to the CRC population during the perioperative period. Should the program be found to be effective and sustainable, hospitals adopting this digital solution may achieve better resource allocation and reduce overall health care costs in the long run. Trial RegistrationClinicalTrials.gov NCT04159363; https://clinicaltrials.gov/ct2/show/NCT04159363

Diabetes remains a great threat to human beings’ health and its world prevalence is projected to reach 9.9% by 2045. At present, the detection methods used are often invasive, cumbersome and time-consuming, thus increasing the burden on patients. In this paper, we propose a novel noninvasive and low-cost biosensor capable of detecting glucose in human sweat using enzyme-based electrodes for point-of-care uses. Specifically, an electrochemical method is applied for detection and the electrodes are covered with multilayered films including ferrocene-polyaniline (F-P), multi-walled carbon nanotubes (MWCNTs) and glucose oxidase (GOx) on Cu substrates (GOx/MWCNTs/F-P/Cu). The coated layers enhance the immobilization of GOx, increase the conductivity of the anode and improve the electrochemical properties of the electrode. Compared with the Cu electrode and the F-P/Cu electrode, a maximum peak current is obtained when the MWCNTs/F-P/Cu electrode is applied. We also study its current response by cyclic voltammetry (CV) at different concentrations (0–2.0 mM) of glucose solution. The best current response is obtained at 0.25 V using chronoamperometry. The effective working lifetime of an electrode is up to 8 days. Finally, to demonstrate the capability of the electrode, a portable, miniaturized and integrated detection device based on the GOx/MWCNTs/F-P/Cu electrode is developed. The results exhibit a short response time of 5 s and a correlation coefficient R2 of 0.9847 between the response current of sweat with blood glucose concentration. The LOD is of 0.081 mM and the reproducibility achieved in terms of RSD is 3.55%. The sweat glucose sensor is noninvasive and point-of-care, which shows great development potential in the health examination and monitoring field.

The prognostic value of intensive postoperative bone scan (BS) screening, which is performed in asymptomatic patients with breast cancer (BC) after surgery, remained unclear. Patients diagnosed with BC with bone metastasis (BM) from five medical centers in China during the years 2005–2013 were retrospectively collected. Propensity score matching (PSM) was performed to balance the baseline characteristics. The survival outcomes were overall survival (OS) and overall survival after BM (OSABM). Among 1059 eligible patients, 304 underwent intensive postoperative BS while 755 did not. During a median follow-up of 6.67 years (95%CI 6.45, 7.21), intensive postoperative BS prolonged the median OS by 1.63 years (Log-Rank p = 0.006) and OSABM by 0.66 years (Log-Rank p = 0.002). Intensive postoperative BS was an independent prognostic factor for both OS (adjusted HR 0.77, 95%CI 0.64, 0.93, adjusted p = 0.006) and OSABM (adjusted HR 0.71, 95%CI 0.60, 0.86, adjusted p < 0.001). The prognostic value of intensive postoperative BS was consistently favorable for OS among clinical high-risk patients, including those with ages younger than 50, stage II, histology grade G3 and ER-Her2- subtype. This multicenter real-world study showed that intensive postoperative BS screening improved survival for BC patients with BM and should probably be recommended for postoperative surveillance, especially for patients at clinical high-risk.

The development of the smartphone and computer vision technique provides customers with a convenient approach to identify tea species, as well as qualities. However, the prediction model may not behave robustly due to changes in illumination conditions. Fluorescence imaging can induce the fluorescence signal from typical components, and thus may improve the prediction accuracy. In this paper, a tea classification method based on fluorescence imaging and convolutional neural networks (CNN) is proposed. Ultra-violet (UV) LEDs with a central wavelength of 370 nm were utilized to induce the fluorescence of tea samples so that the fluorescence images could be captured. Five kinds of tea were included and pre-processed. Two CNN-based classification models, e.g., the VGG16 and ResNet-34, were utilized for model training. Images captured under the conventional fluorescent lamp were also tested for comparison. The results show that the accuracy of the classification model based on fluorescence images is better than those based on the white-light illumination images, and the performance of the VGG16 model is better than the ResNet-34 model in our case. The classification accuracy of fluorescence images reached 97.5%, which proves that the LED-induced fluorescence imaging technique is promising to use in our daily life.

Due to the lack of body stability of emergency rescue vehicles, their attitude stability is insufficient and they are unable to realize working while driving, resulting in low rescue efficiency. Aiming at the water tower fire truck, which is equipped with an active suspension system, the vehicle attitude stability is studied. First, combined with the active suspension system and spray system, a 13-DOF integrated dynamic model for the water tower fire truck is established. Using the model-assisted active disturbance rejection control method, the controllers are designed for the vertical displacement, pitch angle, and roll angle of the vehicle attitude. Then, the computer simulation is carried out to verify the effectiveness of this control method. Finally, the water spray obstacle crossing experiment is carried out with a JP32G water tower fire truck. The results show that when the vehicle runs over the triangular obstacle on one side and two sides in the integrated spray-active suspension mode, the peak–peak values of body pitch angle and roll angle are reduced by 10.9% and 23.2%, and 23.7% and 16.3%, respectively, compared with the passive hydro pneumatic suspension.

Explainable artificial intelligence has been extensively studied recently. However, the research of interpretable methods in the agricultural field has not been systematically studied. We studied the interpretability of deep learning models in different agricultural classification tasks based on the fruit leaves dataset. The purpose is to explore whether the classification model is more inclined to extract the appearance characteristics of leaves or the texture characteristics of leaf lesions during the feature extraction process. The dataset was arranged into three experiments with different categories. In each experiment, the VGG, GoogLeNet, and ResNet models were used and the ResNet-attention model was applied with three interpretable methods. The results show that the ResNet model has the highest accuracy rate in the three experiments, which are 99.11%, 99.4%, and 99.89%, respectively. It is also found that the attention module could improve the feature extraction of the model, and clarify the focus of the model in different experiments when extracting features. These results will help agricultural practitioners better apply deep learning models to solve more practical problems.

Purpose of the Report: Accurate clinical axillary evaluation plays an important role in the diagnosis and treatment planning for early-stage breast cancer (BC). This study aimed to develop a scalable, non-invasive and robust machine learning model for predicting of the pathological node status using dedicated-PET integrating the clinical characteristics in early-stage BC. Materials and Methods: A total of 420 BC patients confirmed by postoperative pathology were retrospectively analyzed. 18F-fluorodeoxyglucose (18F-FDG) Mammi-PET, ultrasound, physical examination, Lymph-PET, and clinical characteristics were analyzed. The least absolute shrinkage and selection operator (LASSO) regression analysis were used in developing prediction models. The characteristic curve (ROC) of the area under receiver-operator (AUC) and DeLong test were used to evaluate and compare the performance of the models. The clinical utility of the models was determined via decision curve analysis (DCA). Then, a nomogram was developed based on the model with the best predictive efficiency and clinical utility and was validated using the calibration plots. Results: A total of 290 patients were enrolled in this study. The AUC of the integrated model diagnosed performance was 0.94 (95% confidence interval (CI), 0.91–0.97) in the training set (n = 203) and 0.93 (95% CI, 0.88–0.99) in the validation set (n = 87) (both p < 0.05). In clinical N0 subgroup, the negative predictive value reached 96.88%, and in clinical N1 subgroup, the positive predictive value reached 92.73%. Conclusions: The use of a machine learning integrated model can greatly improve the true positive and true negative rate of identifying clinical axillary lymph node status in early-stage BC.

As it is high in value, extra virgin olive oil (EVOO) is frequently blended with inferior vegetable oils. This study presents an optical method for determining the adulteration level of EVOO with soybean oil as well as peanut oil using LED-induced fluorescence spectroscopy. Eight LEDs with central wavelengths from ultra-violet (UV) to blue are tested to induce the fluorescence spectra of EVOO, peanut oil, and soybean oil, and the UV LED of 372 nm is selected for further detection. Samples are prepared by mixing olive oil with different volume fractions of peanut or soybean oil, and their fluorescence spectra are collected. Different pre-processing and regression methods are utilized to build the prediction model, and good linearity is obtained between the predicted and actual adulteration concentration. This result, accompanied by the non-destruction and no pre-treatment characteristics, proves that it is feasible to use LED-induced fluorescence spectroscopy as a way to investigate the EVOO adulteration level, and paves the way for building a hand-hold device that can be applied to real market conditions in the future.