Recent advances of nanoparticles on bone tissue engineering and bone cells.

With the development of biotechnology, biomaterials have been rapidly developed and shown great potential in bone regeneration therapy and bone tissue engineering. Nanoparticles have attracted the attention of researches and have applied in various fields especially in the biomedical field as the special physicochemical properties. Nanoparticles were found to regulate bone remodeling depending on their size, shape, composition, and charge. Therefore, in-depth research was necessary to provide the basic support to select the most suitable nanoparticles for bone relate diseases treatment. This article reviews the current development of nanoparticles in bone tissue engineering, focusing on drug delivery, gene delivery, and cell labeling. In addition, the research progress on the interaction of nanoparticles with bone cells, focusing on osteoblasts, osteoclasts, and bone marrow mesenchymal stem cells, and the underlying mechanism were also reviewed. Finally, the current challenges and future research directions are discussed. Thus, detailed study of nanoparticles may reveal new therapeutic strategies to improve the effectiveness of bone regeneration therapy or other bone diseases.

An integrated electrode material based on corn straw cellulose biochar with three-dimensional network porous structure for boosting electrochemical performance of lithium batteries.

In this work an integrated electrode material based on the VS4 nanoparticles grow on three-dimensional network porous biochar is put forward, forming a heterostructure that significantly boost the rate and cycle performance in lithium batteries. Biochar derives from two-steps treatment removing partial cellulose and hemicellulose, possessing three-dimensional network porous structure and naturally nitrogenous. The integrated electrode material constructs the continuous electrons transfer network, accommodates the volume expansion and traps the polar polysulfides efficiently. After 100 cycles at 1C, the integrated electrode with biochar shows the highest specific discharge capacity. Even at 2C, the three-dimensional electrode can display a high specific discharge capacity of 798.6 mAh·g-1. Thus, our study has pointed the innovations approach of constructing integrated electrode materials with porous structure biochar to enhance the electrochemical performance of lithium batteries.

Alleviation of taurine on liver injury of type 2 diabetic rats by improving antioxidant and anti-inflammatory capacity.

Type 2 diabetes mellitus (T2DM) is a serious metabolic disease characterized by insulin resistance and reduced insulin production, which causes abnormally elevated blood glucose. It has been reported that T2DM can enhance oxidative stress and inflammatory responses, and stimulate a variety of complications including liver injury. Studies have shown that taurine has antioxidant and anti-inflammatory effects that can not only ameliorate diabetes but also alleviate liver injury caused by various diseases. However, its effect on liver injury in T2DM is not clear. In our study, a high-fat diet and intraperitoneal injection of streptozotocin (STZ) was used to induce liver injury in T2DM rats, and taurine was given as a treatment. Through the use of HE staining on paraffin sections, ELISA, and qRT-PCR, the effects of taurine on liver pathological alterations, antioxidant capacity, and inflammatory response were investigated. We found that: hepatic transaminase levels of rats were reduced significantly following taurine administration; histopathological observations revealed that the morphology of rat hepatocytes was close to normal, and the number of inflammatory cells around liver vessels was significantly reduced; antioxidant-related indicators were significantly increased, including SOD, CAT, GSH-Px and T-AOC, while related factors of the Nrf2 signalling pathway and its downstream HO-1, NQO1 and γ-GCS were significantly increased; the expression of the JAK2-STAT1 signalling pathway, TLR4/NF-κB signalling pathway and NLRP3 inflammatory vesicle-related factors were significantly reduced. Our results suggest taurine can alleviate T2DM-induced liver injury by improving the antioxidant capacity of the liver and inhibiting macrophage M1-type polarization and the inflammatory response mediated thereby.

Multimodal fusion of liquid biopsy and CT enhances differential diagnosis of early-stage lung adenocarcinoma.

This research explores the potential of multimodal fusion for the differential diagnosis of early-stage lung adenocarcinoma (LUAD) (tumor sizes < 2 cm). It combines liquid biopsy biomarkers, specifically extracellular vesicle long RNA (evlRNA) and the computed tomography (CT) attributes. The fusion model achieves an impressive area under receiver operating characteristic curve (AUC) of 91.9% for the four-classification of adenocarcinoma, along with a benign-malignant AUC of 94.8% (sensitivity: 89.1%, specificity: 94.3%). These outcomes outperform the diagnostic capabilities of the single-modal models and human experts. A comprehensive SHapley Additive exPlanations (SHAP) is provided to offer deep insights into model predictions. Our findings reveal the complementary interplay between evlRNA and image-based characteristics, underscoring the significance of integrating diverse modalities in diagnosing early-stage LUAD.

Design of a 0.15-2 GHz high-resolution vector-sum broadband phase shifter with controlled programmable attenuators for stochastic cooling system.

In this paper, a new 0.15-2 GHz broadband phase shifter with 9-bit phase resolution is presented. This broadband digital adjustable phase shifter is implemented by utilizing the signal vector summation technology. The phase shifter consists of one 90° hybrid coupler, two digital attenuators, two 180° hybrid couplers, two microwave switches, and one combiner. By adjusting the attenuation value of the two digital attenuators, the device creates a minimum 0.7° stepped phase shift. In addition, by switching the two microwave switches, a 360° range of phase shift is achieved while almost keeping the amplitude of the phase shifter constant. The measurement results show that the proposed phase shifter achieves a phase resolution of 0.7° while exhibiting an average insertion loss of 9 ± 2 dB and phase unbalance within ±10°. The calculated RMS phase error of the broadband phase shifter is below 5° from 0.15 to 2 GHz.

Taurine alleviates high-fat-high-glucose-induced pancreatic islet ß-cell oxidative stress and apoptosis in rat.

The effect of taurine (TAU) as a specific regulatory mediator on pancreatic function in obese rats induced by a high-fat-high-glucose (HFHG) diet was investigated. We fed male Sprague-Dawley rats under different conditions, namely the control, HFHG, TAU, and HFHG + TAU treatment groups for 4 months. Compared with the HFHG group, TAU supplementation significantly reduced malondialdehyde levels and increased superoxide dismutase, total antioxidant capacity, and glutathione levels in the rat pancreas. In addition, TAU significantly decreased the level of reactive oxygen species, and markedly increased the activity of heme oxygenase 1 (HO-1), Kelch-like ECH-associated protein 1 (KEAP-1), and nuclear factor erythrocyte-2-related factor 2 (Nrf2) in the rat pancreas. Notably, HFHG diet could induce pancreatic injury in the rats through the Nrf2/HO-1 signaling pathway and activate the mitochondrial channel-mediated apoptotic signaling pathway. The addition of TAU significantly improved the pancreatic tissue injury induced by the HFHG diet in the rats and reduced the protein expression of Caspase-3, Cleaved-caspase-3, Caspase-9 and Bcl-2 associated protein X (BAX), and increased the protein expression of B-cell lymphoma-2 (Bcl-2). In conclusion, this experiment confirmed that TAU could alleviate the oxidative stress and apoptosis induced by the HFHG diet in rat pancreatic ß-cells.

Cirsiliol induces autophagy and mitochondrial apoptosis through the AKT/FOXO1 axis and influences methotrexate resistance in osteosarcoma.

BACKGROUND: Osteosarcoma (OS) is the most common primary malignant bone tumor in children and adolescents, with poor outcomes for patients with metastatic disease or chemotherapy resistance. Cirsiliol is a recently found flavonoid with anti-tumor effects in various tumors. However, the effects of cirsiliol in the regulation of aggressive behaviors of OS remain unknown. METHODS: The effect of cirsiliol on the proliferation of OS cells was detected using a cell counting kit-8 (CCK-8) assay and 5-ethynyl-2'-deoxyuridine (EdU) staining, while cell apoptosis was detected using flow cytometry. Immunofluorescence was applied to visualize the expression level of the mitochondria, lysosomes and microtubule-associated protein light chain 3 (LC3). A computational molecular docking technique was used to predict the interaction between cirsiliol and the AKT protein. The impact of cirsiliol on resistance was investigated by comparing it between a methotrexate (MTX)-sensitive OS cell line, U2OS, and a MTX-resistant OS cell line, U2OS/MTX. Finally, in situ xenogeneic tumor models were used to validate the anti-tumor effect of cirsiliol in OS. RESULTS: Cirsiliol inhibited cell proliferation and induced apoptosis in both U2OS and U2OS/MTX300 OS cells. In addition, treatment with cirsiliol resulted in G2 phase arrest in U2OS/MTX300 and U2OS cells. Cell fluorescence probe staining results showed impaired mitochondria and increased autophagy in OS cells after treatment with cirsiliol. Mechanistically, it was found that cirsiliol targeted AKT by reducing the phosphorylation of AKT, which further activated the transcriptional activity of forkhead Box O transcription factor 1 (FOXO1), ultimately affecting the function of OS cells. Moreover, in situ tumorigenesis experiments showed that cirsiliol inhibited the tumorigenesis and progression of OS in vivo. CONCLUSIONS: Cirsiliol inhibits OS cell growth and induces cell apoptosis by reducing AKT phosphorylation and further promotes FOXO1 expression. These phenomena indicate that cirsiliol is a promising treatment option for OS.

Age-dependent gender differences in the diagnosis and treatment of osteoporosis during hospitalization in patients with fragility fractures.

BACKGROUND: There is a gender difference in the acceptance of osteoporosis diagnosis and treatment in patients after fragility fractures, but this difference is rarely assessed during hospitalization, and it is unclear whether these differences are age-dependent. This study aimed to evaluate the differences between male and female fragility fracture patients of different age groups who received the diagnosis and treatment of osteoporosis during hospitalization. METHODS: 31,265 fragility fracture patients aged ≥ 50 years from the Fragility Fracture Management Database in a high-volume orthopedic hospital from December 2019 to February 2023 were included in this study. We compared the differences in the rates of men and women with fragility fracture who received the measurement of bone mineral density (BMD) and bone metabolism biochemical markers (BMBMs) and treatment with anti-osteoporosis medications (AOMs), and follow-up to the internal medicine clinic within 3 months after discharge, across all age groups and across different age stages (50-59, 60-69, 70-79, and ≥ 80 years). RESULTS: The detection rates of female patients receiving BMD and BMBMs during hospitalization were 31.88% and 5.30%, respectively, compared with 22.23% and 2.69% for men. The rate of receiving any AOMs treatment was 44.63% for women and 31.60% for men. The follow-up rate of returning to the internal medicine clinic within 3 months after discharge was 9.79% for women compared to 3.00% for men. There was a significant difference between males compared to females (P < 0.0001). Analysis of patients by different age group revealed that differences in the diagnosis and treatment of osteoporosis were found only in patients under 80 years of age, while gender differences in the return to the internal medicine clinic for follow-up after discharge were present in all age groups. CONCLUSIONS: Gender differences present in osteoporosis management in patients with fragility fracture during hospitalization, especially for patients under 80 years of age. This finding suggests that orthopedic surgeons neglect to manage osteoporosis in male patients with fragility fracture during hospitalization.

Deep learning parametric response mapping from inspiratory chest CT scans: a new approach for small airway disease screening.

OBJECTIVES: Parametric response mapping (PRM) enables the evaluation of small airway disease (SAD) at the voxel level, but requires both inspiratory and expiratory chest CT scans. We hypothesize that deep learning PRM from inspiratory chest CT scans can effectively evaluate SAD in individuals with normal spirometry. METHODS: We included 537 participants with normal spirometry, a history of smoking or secondhand smoke exposure, and divided them into training, tuning, and test sets. A cascaded generative adversarial network generated expiratory CT from inspiratory CT, followed by a UNet-like network predicting PRM using real inspiratory CT and generated expiratory CT. The performance of the prediction is evaluated using SSIM, RMSE and dice coefficients. Pearson correlation evaluated the correlation between predicted and ground truth PRM. ROC curves evaluated predicted PRMfSAD (the volume percentage of functional small airway disease, fSAD) performance in stratifying SAD. RESULTS: Our method can generate expiratory CT of good quality (SSIM 0.86, RMSE 80.13 HU). The predicted PRM dice coefficients for normal lung, emphysema, and fSAD regions are 0.85, 0.63, and 0.51, respectively. The volume percentages of emphysema and fSAD showed good correlation between predicted and ground truth PRM (|r| were 0.97 and 0.64, respectively, p < 0.05). Predicted PRMfSAD showed good SAD stratification performance with ground truth PRMfSAD at thresholds of 15%, 20% and 25% (AUCs were 0.84, 0.78, and 0.84, respectively, p < 0.001). CONCLUSION: Our deep learning method generates high-quality PRM using inspiratory chest CT and effectively stratifies SAD in individuals with normal spirometry.

Effect of bedside health education for elderly patients with fragility fracture by specialist physicians on the diagnosis and treatment of osteoporosis during hospitalization and the visiting rate to osteoporosis clinic after discharge in a high-volume orthopedic hospital.

We retrospectively analyzed 12,999 elderly patients with fragility fracture and found that the detection rate of bone mineral density (BMD) and bone turnover markers (BTMs), the treatment rate of osteoporosis, and the visiting rate to the osteoporosis specialist clinic after discharge are significantly enhanced in fragility fracture patients after receiving health education on osteoporosis-related knowledge during hospitalization. PURPOSE: To observe the effect of health education on the diagnosis and treatment of osteoporosis during hospitalization and the rate of come back to osteoporosis clinic after discharge in elderly patients with fragility fracture. METHODS: A retrospective analysis was performed on 12,999 elderly patients with fragility fracture admitted to Xi'an Honghui Hospital from March 2021 to December 2022. The patients were divided into the health education group and the non-health education group according to whether they received health education on osteoporosis-related knowledge during hospitalization. The diagnosis and treatment of osteoporosis during hospitalization and the outpatient treatment of osteoporosis after discharge were compared between the two groups. RESULTS: Among the 7784 patients in the health education group, 4551 (58.47%) received BMD test, 798 (10.25%) received BTMs test, 3990 (51.26%) received anti-osteoporosis medications (AOMs) treatment, and 1232 (15.83%) came back to the osteoporosis specialist clinic after discharge. Among the 5215 patients in the non-health education group, 681 (13.06%) received BMD test, 6 (0.12%) received BTMs test, 2071 (39.71%) received AOMs treatment, and 440 (8.44%) came back to the osteoporosis specialist clinic within one month after discharge. CONCLUSION: The education of osteoporosis-related knowledge for patients with fragility fracture contribute to enhance the detection rate of BMD and BTMs and the treatment rate of osteoporosis during hospitalization, and increase the rate of coming back to the osteoporosis clinic after discharge.

Deep learning-based solid component measuring enabled interpretable prediction of tumor invasiveness for lung adenocarcinoma.

BACKGROUND: The nature of the solid component of subsolid nodules (SSNs) can indicate tumor pathological invasiveness. However, preoperative solid component assessment still lacks a reference standard. METHODS: In this retrospective study, an AI algorithm was proposed for measuring the solid components ratio in SSNs, which was used to assess the diameter ratio (1D), area ratio (2D), and volume ratio (3D). The radiologist measured each SSN's consolidation to tumor ratio (CTR) twice, four weeks apart. The area under the receiver-operating characteristic (ROC) curve (AUC) was calculated for each method used to discriminate an Invasive Adenocarcinoma (IA) from a non-IA. The AUC and the time cost of each measurement were compared. Furthermore, we examined the consistency of measurements made by the radiologist on two separate occasions. RESULTS: A total of 379 patients (the primary dataset n = 278, the validation dataset n = 101) were included. In the primary dataset, compared to the manual approach (AUC: 0.697), the AI algorithm (AUC: 0.811) had better predictive performance (P =.0027) in measuring solid components ratio in 3D. Algorithm measurement in 3D had an AUC no inferior to 1D (AUC: 0.806) and 2D (AUC: 0.796). In the validation dataset, the AI 3D method also achieved superior diagnostic performance compared to the radiologist (AUC: 0.803 vs 0.682, P =.046). The two measurements of the CTR in the primary dataset, taken 4 weeks apart, have 7.9 % cases in poor consistency. The measurement time cost by the radiologist is about 60 times that of the AI algorithm (P <.001). CONCLUSION: The 3D measurement of solid components using AI, is an effective and objective approach to predict the pathological invasiveness of SSNs. It can be a preoperative interpretable indicator of pathological invasiveness in patients with lung adenocarcinoma.

Measurement and optimization of the beam coupling impedance of a novel 3D-printed titanium alloy cage inside the thin-wall vacuum chamber.

Dipole magnet vacuum chambers are among the most critical and costly components of rapid-cycling accelerator facilities. Alternative approaches to traditional ceramic chambers have been explored for the implementation of fast-ramping dipole-magnet vacuum chambers, including thin-wall metallic beam pipe chambers strengthened with transverse ribs and ceramic rings inside thin-walled chambers. Here, we report a novel 3D-printed titanium alloy cage inside the thin-wall vacuum chamber, which is designed for high-intensity heavy ion accelerator facility (HIAF) to reduce manufacturing difficulty and cost, shorten the production cycle, and improve the quality. Comprehensive studies were undertaken to characterize the impedance of the 3D-printed titanium alloy cage inside the thin-wall vacuum chamber. The beam-coupling impedance and eddy currents of the new thin-wall vacuum chamber were studied mostly numerically. Strategies for further reducing the beam-coupling impedance were explored. In addition, impedance bench measurements using the "half wavelength" resonant method were conducted to identify the longitudinal and transverse impedances of the 3D-printed titanium alloy cage inside the thin-wall vacuum chamber prototype experimentally. The simulated and measured results for the impedance were consistent. Furthermore, a campaign for resonance-check measurements on the 3D-printed titanium alloy ring loaded inside a thin-wall vacuum chamber prototype was launched. This novel thin-wall vacuum chamber structure is now entering the fabrication stage and will soon be ready for installation in the Booster Ring (BRing).

Stochastic cooling notch filter developments for the high-precision spectrometer ring in the high intensity heavy-ion accelerator facility project.

Stochastic cooling of the high-precision spectrometer ring (SRing) at the High Intensity Heavy-Ion Accelerator Facility (HIAF) project in China, which is used mainly for experiments with radioactive fragment beams, is applied to speed up the cooling process of a stored ion beam. In this article, a new coaxial-type notch filter with an amplitude equalizer in the long branch and an optical-type notch filter with phase-stabilized optical fiber are discussed and evaluated for the SRing stochastic cooling system. Both prototypes of coaxial and optical notch filters are fabricated and tested. The minimum notch depth of coaxial and optical notch filters reaches to 26 and 40 dB, respectively. The performance of both coaxial notch filter and optical fiber notch filter is presented in this work. These developments will be used not only for the longitudinal stochastic cooling system but also have potential for the beam feedback system.

Multi-site, Multi-domain Airway Tree Modeling.

Open international challenges are becoming the de facto standard for assessing computer vision and image analysis algorithms. In recent years, new methods have extended the reach of pulmonary airway segmentation that is closer to the limit of image resolution. Since EXACT'09 pulmonary airway segmentation, limited effort has been directed to the quantitative comparison of newly emerged algorithms driven by the maturity of deep learning based approaches and extensive clinical efforts for resolving finer details of distal airways for early intervention of pulmonary diseases. Thus far, public annotated datasets are extremely limited, hindering the development of data-driven methods and detailed performance evaluation of new algorithms. To provide a benchmark for the medical imaging community, we organized the Multi-site, Multi-domain Airway Tree Modeling (ATM'22), which was held as an official challenge event during the MICCAI 2022 conference. ATM'22 provides large-scale CT scans with detailed pulmonary airway annotation, including 500 CT scans (300 for training, 50 for validation, and 150 for testing). The dataset was collected from different sites and it further included a portion of noisy COVID-19 CTs with ground-glass opacity and consolidation. Twenty-three teams participated in the entire phase of the challenge and the algorithms for the top ten teams are reviewed in this paper. Both quantitative and qualitative results revealed that deep learning models embedded with the topological continuity enhancement achieved superior performance in general. ATM'22 challenge holds as an open-call design, the training data and the gold standard evaluation are available upon successful registration via its homepage (https://atm22.grand-challenge.org/).

RibSeg v2: A Large-scale Benchmark for Rib Labeling and Anatomical Centerline Extraction.

Automatic rib labeling and anatomical centerline extraction are common prerequisites for various clinical applications. Prior studies either use in-house datasets that are inaccessible to communities, or focus on rib segmentation that neglects the clinical significance of rib labeling. To address these issues, we extend our prior dataset (RibSeg) on the binary rib segmentation task to a comprehensive benchmark, named RibSeg v2, with 660 CT scans (15,466 individual ribs in total) and annotations manually inspected by experts for rib labeling and anatomical centerline extraction. Based on the RibSeg v2, we develop a pipeline including deep learning-based methods for rib labeling, and a skeletonization-based method for centerline extraction. To improve computational efficiency, we propose a sparse point cloud representation of CT scans and compare it with standard dense voxel grids. Moreover, we design and analyze evaluation metrics to address the key challenges of each task. Our dataset, code, and model are available online to facilitate open research at https://github.com/M3DV/RibSeg.

Prognostic impact of deep learning-based quantification in clinical stage 0-I lung adenocarcinoma.

OBJECTIVES: To evaluate the performance of automatic deep learning (DL) algorithm for size, mass, and volume measurements in predicting prognosis of lung adenocarcinoma (LUAD) and compared with manual measurements. METHODS: A total of 542 patients with clinical stage 0-I peripheral LUAD and with preoperative CT data of 1-mm slice thickness were included. Maximal solid size on axial image (MSSA) was evaluated by two chest radiologists. MSSA, volume of solid component (SV), and mass of solid component (SM) were evaluated by DL. Consolidation-to-tumor ratios (CTRs) were calculated. For ground glass nodules (GGNs), solid parts were extracted with different density level thresholds. The prognosis prediction efficacy of DL was compared with that of manual measurements. Multivariate Cox proportional hazards model was used to find independent risk factors. RESULTS: The prognosis prediction efficacy of T-staging (TS) measured by radiologists was inferior to that of DL. For GGNs, MSSA-based CTR measured by radiologists (RMSSA%) could not stratify RFS and OS risk, whereas measured by DL using 0HU (2D-AIMSSA0HU%) could by using different cutoffs. SM and SV measured by DL using 0 HU (AISM0HU% and AISV0HU%) could effectively stratify the survival risk regardless of different cutoffs and were superior to 2D-AIMSSA0HU%. AISM0HU% and AISV0HU% were independent risk factors. CONCLUSION: DL algorithm can replace human for more accurate T-staging of LUAD. For GGNs, 2D-AIMSSA0HU% could predict prognosis rather than RMSSA%. The prediction efficacy of AISM0HU% and AISV0HU% was more accurate than of 2D-AIMSSA0HU% and both were independent risk factors. CLINICAL RELEVANCE STATEMENT: Deep learning algorithm could replace human for size measurements and could better stratify prognosis than manual measurements in patients with lung adenocarcinoma. KEY POINTS: • Deep learning (DL) algorithm could replace human for size measurements and could better stratify prognosis than manual measurements in patients with lung adenocarcinoma (LUAD). • For GGNs, maximal solid size on axial image (MSSA)-based consolidation-to-tumor ratio (CTR) measured by DL using 0 HU could stratify survival risk than that measured by radiologists. • The prediction efficacy of mass- and volume-based CTRs measured by DL using 0 HU was more accurate than of MSSA-based CTR and both were independent risk factors.

Green and chemical-free pretreatment of corn straw using cold isostatic pressure for methane production.

In this study, the effect of cold isostatic pressure (CIP) pretreatment on the physicochemical properties and subsequent anaerobic digestion (AD) performance of corn straw (CS) was explored. The CS was subjected to CIP pretreatment by pressures of 200, 400 and 600 MPa, respectively, while AD was carried out at medium temperature (35 ± 2 °C). The results showed that CIP pretreatment disrupted the dense structure of the CS and altered the crystallinity index and surface hydrophobicity of the CS, thereby affecting the AD process. The presence of CIP pretreatment increased the initial reducing sugar concentration by 0.11-0.27 g/L and increased the maximum volatile fatty acids content by 112.82-436.64 mg/L, which facilitated the process of acidification and hydrolysis of the AD. It was also observed that the CIP pretreatment maintained the pH in the range of 6.37-7.30, maintaining the stability of the overall system. Moreover, the cumulative methane production in the CIP pretreatment group increased by 27.17 %-64.90 % compared to the control group. Analysis of the microbial results showed that CIP pretreatment increased the abundance of cellulose degrading bacteria Ruminofilibacter from 21.50 % to 27.53 % and acetoclastic methanogen Methanosaeta from 45.48 % to 56.92 %, thus facilitating the hydrolysis and methanogenic stages. The energy conversion analysis showed that CIP is a green and non-polluting pretreatment strategy for the efficient AD of CS to methane.

Photosynthetic response mechanism to polybrominated diphenyl ether exposure in Chlorella pyrenoidosa.

Polybrominated diphenyl ether (PBDE) contamination is common in aquatic environments and can severely damage aquatic organisms. However, there is a lack of information on the response and self-adaptation mechanisms of these organisms. Chlorella pyrenoidosa was treated with 2,2',4,4'-tetrabromodiphenyl ether (BDE47), causing significant growth inhibition, pigment reduction, oxidative stress, and chloroplast atrophy. Photosynthetic damage contributed to inhibition, as indicated by Fv/Fm, Chl a fluorescence induction, photosynthetic oxygen evolution activity, and photosystem subunit stoichiometry. Here, Chl a fluorescence induction and quinone electron acceptor (QA-) reoxidation kinetics showed that the PSII donor and acceptor sides were insensitive to BDE47. Quantitative analyses of D1 and PsaD proteins illustrated that PSII and PSI complexes were the main primary targets of photosynthesis inhibition by BDE47. Significant modulation of PSII complex might have been caused by the potential binding of BDE47 on D1 protein, and molecular docking was performed to investigate this. Increased activation of antioxidant defense systems and photosystem repair as a function of exposure time indicated a positive resistance to BDE47. After a 5-day exposure, 23 % of BDE47 was metabolized. Our findings suggest that C. pyrenoidosa has potential as a bioremediator for wastewater-borne PBDEs and can improve our understanding of ecological risks to microalgae.

Effects of cold isostatic press pretreatment of rice straw on microstructure and efficiency of anaerobic digestion for methane production.

In this study, rice straw was pretreated using by cold isostatic pressure to disrupt its microstructure and improve the performance of anaerobic digestion, and the optimal process parameters were optimized using the response surface methodology. The results showed that cold isostatic pressure pretreatment under optimal conditions (pressure of 400 MPa and holding time of 9 min) was effective in disrupting the structure of rice straw and improving its biodegradability. The cumulative methane production of the cold isostatic pressure pretreatment group increased by 76% compared to the untreated group. In addition, microbial community analysis showed that the relative abundance of Firmicutes, Halobacterota, DMER64 and Methanosaeta was higher in groups pretreated with cold isostatic pressure than in untreated groups. This study demonstrated the potential of pretreatment of rice straw with cold isostatic pressure to increase methane production during anaerobic digestion.

Deep learning predicts malignancy and metastasis of solid pulmonary nodules from CT scans.

In the clinic, it is difficult to distinguish the malignancy and aggressiveness of solid pulmonary nodules (PNs). Incorrect assessments may lead to delayed diagnosis and an increased risk of complications. We developed and validated a deep learning-based model for the prediction of malignancy as well as local or distant metastasis in solid PNs based on CT images of primary lesions during initial diagnosis. In this study, we reviewed the data from multiple patients with solid PNs at our institution from 1 January 2019 to 30 April 2022. The patients were divided into three groups: benign, Ia-stage lung cancer, and T1-stage lung cancer with metastasis. Each cohort was further split into training and testing groups. The deep learning system predicted the malignancy and metastasis status of solid PNs based on CT images, and then we compared the malignancy prediction results among four different levels of clinicians. Experiments confirmed that human-computer collaboration can further enhance diagnostic accuracy. We made a held-out testing set of 134 cases, with 689 cases in total. Our convolutional neural network model reached an area under the ROC (AUC) of 80.37% for malignancy prediction and an AUC of 86.44% for metastasis prediction. In observer studies involving four clinicians, the proposed deep learning method outperformed a junior respiratory clinician and a 5-year respiratory clinician by considerable margins; it was on par with a senior respiratory clinician and was only slightly inferior to a senior radiologist. Our human-computer collaboration experiment showed that by simply adding binary human diagnosis into model prediction probabilities, model AUC scores improved to 81.80-88.70% when combined with three out of four clinicians. In summary, the deep learning method can accurately diagnose the malignancy of solid PNs, improve its performance when collaborating with human experts, predict local or distant metastasis in patients with T1-stage lung cancer, and facilitate the application of precision medicine.