Assessment of pulmonary physiological changes caused by aging, cigarette smoking, and COPD with hyperpolarized 129Xe magnetic resonance.

OBJECTIVE: To comprehensively assess the impact of aging, cigarette smoking, and chronic obstructive pulmonary disease (COPD) on pulmonary physiology using 129Xe MR. METHODS: A total of 90 subjects were categorized into four groups, including healthy young (HY, n = 20), age-matched control (AMC, n = 20), asymptomatic smokers (AS, n = 28), and COPD patients (n = 22). 129Xe MR was utilized to obtain pulmonary physiological parameters, including ventilation defect percent (VDP), alveolar sleeve depth (h), apparent diffusion coefficient (ADC), total septal wall thickness (d), and ratio of xenon signal from red blood cells and interstitial tissue/plasma (RBC/TP). RESULTS: Significant differences were found in the measured VDP (p = 0.035), h (p = 0.003), and RBC/TP (p = 0.003) between the HY and AMC groups. Compared with the AMC group, higher VDP (p = 0.020) and d (p = 0.048) were found in the AS group; higher VDP (p < 0.001), d (p < 0.001) and ADC (p < 0.001), and lower h (p < 0.001) and RBC/TP (p < 0.001) were found in the COPD group. Moreover, significant differences were also found in the measured VDP (p < 0.001), h (p < 0.001), ADC (p < 0.001), d (p = 0.008), and RBC/TP (p = 0.032) between the AS and COPD groups. CONCLUSION: Our findings indicate that pulmonary structure and functional changes caused by aging, cigarette smoking, and COPD are various, and show a progressive deterioration with the accumulation of these risk factors, including cigarette smoking and COPD. CLINICAL RELEVANCE STATEMENT: Pathophysiological changes can be difficult to comprehensively understand due to limitations in common techniques and multifactorial etiologies. 129Xe MRI can demonstrate structural and functional changes caused by several common factors and can be used to better understand patients' underlying pathology. KEY POINTS: Standard techniques for assessing pathophysiological lung function changes, spirometry, and chest CT come with limitations. 129Xe MR demonstrated progressive deterioration with accumulation of the investigated risk factors, without these limitations. 129Xe MR can assess lung changes related to these risk factors to stage and evaluate the etiology of the disease.

Efficacy of digital zoning design for the resection of benign parotid gland tumor.

The objective of this study is to verify the role of digital modified parotid tumor zoning method in modified parotid incision. The data of patients with parotid benign tumors from November 2021 to December 2023 were collected. Through the use of digital technology for soft tissue reconstruction, the parotid tumor was divided into four areas according to the digital image marker points. We designed the surgical incision according to the parotid gland division, found that it was feasible to guide the incision selection by division, and summarized the common incision and division corresponding, zone I was I and V-shaped incision, zone II was V incision, zone III was V and C- shaped incision, and zone IV was C- shaped incision. We conclude that the digital modified parotid gland zoning method can provide a better distinction in the surgical incision, and provide a better cosmetic incision and prognosis.

Supramolecular prodrug of SN38 based on endogenous albumin and SN38 prodrug modified with semaglutide side chain to improve the tumor distribution.

To improve the biodistribution of the drug in the tumor, a supramolecular prodrug of SN38 was fabricated in situ between endogenous albumin and SN38 prodrug modified with semaglutide side chain. Firstly, SN38 was conjugated with semaglutide side chain and octadecanedioic acid via glycine linkers to obtain SI-Gly-SN38 and OA-Gly-SN38 prodrugs, respectively. Both SI-Gly-SN38 and OA-Gly-SN38 exhibited excellent stability in PBS for over 24 h. Due to the strong binding affinity of the semaglutide side chain with albumin, the plasma half-life of SI-Gly-SN38 was 2.7 times higher than that of OA-Gly-SN38. Furthermore, with addition of HSA, the fluorescence intensity of SI-Gly-SN38 was 4 times higher than that of OA-Gly-SN38, confirming its strong binding capability with HSA. MTT assay showed that the cytotoxicity of SI-Gly-SN38 and OA-Gly-SN38 was higher than that of Irinotecan. Even incubated with HSA, the SI-Gly-SN38 and OA-Gly-SN38 still maintained high cytotoxicity, indicating minimal influence of HSA on their cytotoxicity. In vivo pharmacokinetic studies demonstrated that the circulation half-life of SI-Gly-SN38 was twice that of OA-Gly-SN38. SI-Gly-SN38 exhibited significantly reduced accumulation in the lungs, being only 0.23 times that of OA-Gly-SN38. The release of free SN38 in the lungs from SI-Gly-SN38 was only 0.4 times that from OA-Gly-SN38 and Irinotecan. The SI-Gly-SN38 showed the highest accumulation in tumors. The tumor inhibition rate of SI-Gly-SN38 was 6.42% higher than that of OA-Gly-SN38, and 8.67% higher than that of Irinotecan, respectively. These results indicate that the supramolecular prodrug delivery system can be constructed between SI-Gly-SN38 and endogenous albumin, which improves drug biodistribution in vivo, enhances tumor accumulation, and plays a crucial role in tumor growth inhibition.

Carborane-FAPI conjugate: A potential FAP-targeted boron agent with improved boron content.

Boron neutron capture therapy (BNCT) has received extensive attention as an advanced binary radiotherapy method. However, BNCT still faces poor selectivity of boron agent and is insufficient boron content in tumor tissues. To improve the tumor-targeted ability and boron content, this research aims to design, synthesize and preliminary evaluate a new borane agent Carborane-FAPI, which coupling the o-carborane to the compound skeleton of a mature fibroblast activating protein (FAP) inhibitor (FAPI). FAP is a tumor-associated antigen. FAP expressed lowly in normal organs and highly expressed in tumors, so it is a potential target for diagnosis and treatment. Boronophenylalanine (BPA) is the most widely investigated BNCT drug in present. Compared with BPA, the boron content of a single molecule is increased and drug targeting is enhanced. The results show that Carboaren-FAPI has low toxicity to normal cells, and selective enrichment in tumor tissues. It is a promising boron drug that has the potential to be used in BNCT.

Preoperative nomogram for predicting spread through air spaces in clinical-stage IA non-small cell lung cancer using 18F-fluorodeoxyglucose positron emission tomography/computed tomography.

PURPOSE: This study aims to assess the predictive value of 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) radiological features and the maximum standardized uptake value (SUVmax) in determining the presence of spread through air spaces (STAS) in clinical-stage IA non-small cell lung cancer (NSCLC). METHODS: A retrospective analysis was conducted on 180 cases of NSCLC with postoperative pathological assessment of STAS status, spanning from September 2019 to September 2023. Of these, 116 cases from hospital one comprised the training set, while 64 cases from hospital two formed the testing set. The clinical information, tumor SUVmax, and 13 related CT features were analyzed. Subgroup analysis was carried out based on tumor density type. In the training set, univariable and multivariable logistic regression analyses were employed to identify the most significant variables. A multivariable logistic regression model was constructed and the corresponding nomogram was developed to predict STAS in NSCLC, and its diagnostic efficacy was evaluated in the testing set. RESULTS: SUVmax, consolidation-to-tumor ratio (CTR), and lobulation sign emerged as the best combination of variables for predicting STAS in NSCLC. Among these, SUVmax and CTR were identified as independent predictors for STAS prediction. The constructed prediction model demonstrated area under the curve (AUC) values of 0.796 and 0.821 in the training and testing sets, respectively. Subgroup analysis revealed a 2.69 times higher STAS-positive rate in solid nodules compared to part-solid nodules. SUVmax was an independent predictor for predicting STAS in solid nodular NSCLC, while CTR and an emphysema background were independent predictors for STAS in part-solid nodular NSCLC. CONCLUSION: Our nomogram based on preoperative 18F-FDG PET/CT radiological features and SUVmax effectively predicts STAS status in clinical-stage IA NSCLC. Furthermore, our study highlights that metabolic parameters and CT variables associated with STAS differ between solid and part-solid nodular NSCLC.

"Microflower-Templated" Janus Sheets: Synthesis and Application in Stabilizing Foams.

In this study, we proposed a method for fabricating Janus sheets using biological "microflowers" as a sacrificial template. The microflower-templated Janus sheets (MF-JNSs) were employed as a foam stabilizer in foam separation of the whey soybean protein (WSP). The MF-JNSs took inorganic hybrid microflowers (BSA@Cu3 (PO4)2-MF) as template, followed by the sequential attachment of protamine and silica to the surface of the BSA@Cu3(PO4)2-MF. Subsequently, the template was removed using ethylenediaminetetraacetic acid after the silicon dioxide was modified by 3-(methacryloyloxy) propyl trimethoxysilane. Upon template dissolution, the modified silica layer, lacking support from the core, fractured to form the MF-JNSs. This method omitted the step of treating the hollow ball by external force and obtained Janus sheets in one step, indicating that it was simple and feasible. The morphology, structure, and composition of the MF-JNSs were analyzed by SEM, TEM, AFM, XRD, and FT-IR. The MF-JNSs were found to delay the breakage time of the Pickering emulsion, demonstrating their emulsion stabilizing capability. Importantly, they significantly enhanced the foam half-life and foam height of soybean whey wastewater (SWW). Moreover, the recovery percentage and enrichment ratio of WSP, separated from SWW by foam separation, were improved to 81 ± 0.28 and 1.20 ± 0.05%, respectively.

Radiomics model based on MRI to differentiate spinal multiple myeloma from metastases: A two-center study.

Purpose: Spinal multiple myeloma (MM) and metastases are two common cancer types with similar imaging characteristics, for which differential diagnosis is needed to ensure precision therapy. The aim of this study is to establish radiomics models for effective differentiation between them. Methods: Enrolled in this study were 263 patients from two medical institutions, including 127 with spinal MM and 136 with spinal metastases. Of them, 210 patients from institution I were used as the internal training cohort and 53 patients from Institution II were used as the external validation cohort. Contrast-enhanced T1-weighted imaging (CET1) and T2-weighted imaging (T2WI) sequences were collected and reviewed. Based on the 1037 radiomics features extracted from both CET1 and T2WI images, Logistic Regression (LR), AdaBoost (AB), Support Vector Machines (SVM), Random Forest (RF), and multiple kernel learning based SVM (MKL-SVM) were constructed. Hyper-parameters were tuned by five-fold cross-validation. The diagnostic efficiency among different radiomics models was compared by accuracy (ACC), sensitivity (SEN), specificity (SPE), area under the ROC curve (AUC), YI, positive predictive value (PPV), negative predictive value (NPY), and F1-score. Results: Based on single-sequence, the RF model outperformed all other models. All models based on T2WI images performed better than those based on CET1. The efficiency of all models was boosted by incorporating CET1 and T2WI sequences, and the MKL-SVM model achieved the best performance with ACC, AUC, and F1-score of 0.862, 0.870, and 0.874, respectively. Conclusions: The radiomics models constructed based on MRI achieved satisfactory diagnostic performance for differentiation of spinal MM and metastases, demonstrating broad application prospects for individualized diagnosis and treatment.

Mg-MOF-74 Derived Defective Framework for Hydrogen Storage at Above-Ambient Temperature Assisted by Pt Catalyst.

Metal-organic frameworks (MOFs) are promising candidates for room-temperature hydrogen storage materials after modification, thanks to their ability to chemisorb hydrogen. However, the hydrogen adsorption strength of these modified MOFs remains insufficient to meet the capacity and safety requirements of hydrogen storage systems. To address this challenge, a highly defective framework material known as de-MgMOF is prepared by gently annealing Mg-MOF-74. This material retains some of the crystal properties of the original Mg-MOF-74 and exhibits exceptional hydrogen storage capacity at above-ambient temperatures. The MgO5 knots around linker vacancies in de-MgMOF can adsorb a significant amount of dissociated and nondissociated hydrogen, with adsorption enthalpies ranging from -22.7 to -43.6 kJ mol-1, indicating a strong chemisorption interaction. By leveraging a spillover catalyst of Pt, the material achieves a reversible hydrogen storage capacity of 2.55 wt.% at 160 °C and 81 bar. Additionally, this material offers rapid hydrogen uptake/release, stable cycling, and convenient storage capabilities. A comprehensive techno-economic analysis demonstrates that this material outperforms many other hydrogen storage materials at the system level for on-board applications.

Automatic detection of cerebral microbleeds using susceptibility weighted imaging and artificial intelligence.

Background: Efficiently and accurately detecting cerebral microbleeds (CMBs) is crucial for diagnosing dementia, stroke, and traumatic brain injury. Manual CMB detection, however, is time-consuming and error-prone. This study evaluates a novel artificial intelligence (AI) software designed for the automated detection of CMBs using susceptibility weighted imaging (SWI). Methods: The SWI data from 265 patients, 206 of whom had a history of stroke and others of whom presented a variety of other medical histories, including hypertension, diabetes, hyperlipidemia, cerebral hemorrhage, intracerebral vascular malformations, tumors, and inflammation, collected between January 2015 and December 2018, were analyzed. Two independent radiologists initially reviewed the images to identify and count the number of CMBs. Subsequently, the images were processed using an automatic CMB detection software. The generated reports were then reviewed by the radiologists. A final consensus between the two radiologists, obtained after a second review of the images, was used to compare results obtained from the initial manual detection and those of the automatic CMB detection software. The differences of detection sensitivity and precision for patients with or without CMBs and for individual CMBs between the radiologist and the automatic CMB detection software were compared using Pearson chi-squared tests. Results: A total of 1,738 CMBs were detected among 148 patients (71.4±10.7 years, 100 males) from the analyzed SWI data. While the radiologists identified 139 cases with CMBs, the automatic CMB detection software detected 145 cases. Nevertheless, there was no statistical difference in the sensitivity and specificity of the automatic CMB detection software compared to manual detection in determining patients with CMBs (P=0.656 and P=0.212, chi-square test). However, the radiologist identified 93 patients without CMBs, while the automatic CMB detection software detected 121 patients without CMBs, exhibiting a statistically significant difference (P=0.016, chi-square test). In terms of individual CMBs, the radiologists found 1,284, whereas the automatic CMB detection software detected 1,677 CMBs. The detection sensitivity for human versus the automatic CMB detection software were 75.5% and 96.5% respectively (P<0.001, chi-square test), while the precision rates were 92.2% and 86.0% (P<0.001, chi-square test), respectively. Notably, the radiologists were more likely to overlook CMBs when the number of CMBs was high (above 30). Conclusions: The automatic CMB detection software proved to be an effective tool for the detection and quantification of CMBs. It demonstrated higher sensitivity than the radiologists, especially in detecting minuscule CMBs and in cases with high CMB prevalence.

Comparison of deep-learning and radiomics-based machine-learning methods for the identification of chronic obstructive pulmonary disease on low-dose computed tomography images.

Background: Radiomics and artificial intelligence approaches have been developed to predict chronic obstructive pulmonary disease (COPD), but it is still unclear which approach has the best performance. Therefore, we established five prediction models that employed deep-learning (DL) and radiomics-based machine-learning (ML) approaches to identify COPD on low-dose computed tomography (LDCT) images and compared the relative performance of the different models to find the best model for identifying COPD. Methods: This retrospective analysis included 1,024 subjects (169 COPD patients and 855 control subjects) who underwent LDCT scans from August 2018 to July 2021. Five prediction models, including models that employed computed tomography (CT)-based radiomics features, chest CT images, quantitative lung density parameters, and demographic and clinical characteristics, were established to identify COPD by DL or ML approaches. Model 1 used CT-based radiomics features by ML method. Model 2 used a combination of CT-based radiomics features, lung density parameters, and demographic and clinical characteristics by ML method. Model 3 used CT images only by DL method. Model 4 used a combination of CT images, lung density parameters, and demographic and clinical characteristics by DL method. Model 5 used a combination of CT images, CT-based radiomics features, lung density parameters, and demographic and clinical characteristics by DL method. The accuracy, sensitivity, specificity, highest negative predictive values (NPVs), positive predictive values, and areas under the receiver operating characteristic (AUC) curve of the five prediction models were compared to examine their performance. The DeLong test was used to compare the AUCs of the different models. Results: In total, 107 radiomics features were extracted from each subject's CT images, 17 lung density parameters were acquired by quantitative measurement, and 18 selected demographic and clinical characteristics were recorded in this study. Model 2 had the highest AUC [0.73, 95% confidence interval (CI): 0.64-0.82], while model 3 had the lowest AUC (0.65, 95% CI: 0.55-0.75) in the test set. Model 2 also had the highest sensitivity (0.84), the highest accuracy (0.81), and the highest NPV (0.36). In the test set, based on the AUC results, Model 2 significantly outperformed Model 1 (P=0.03). Conclusions: The results showed that the identification ability of models that employ CT-based radiomics features combined with lung density parameters, and demographic and clinical characteristics using ML methods performed better than the chest CT image-based DL methods. ML methods are more suitable and beneficial for COPD identification.

Denitrification Characteristics of the Low-Temperature Tolerant Denitrification Strain Achromobacter spiritinus HS2 and Its Application.

The low-temperature environment significantly inhibits the growth and metabolism of denitrifying bacteria, leading to an excessive concentration of ammonia nitrogen and total nitrogen in sewage treatment plants during the cold season. In this study, an efficient denitrifying strain of heterotrophic nitrification-aerobic denitrification (HN-AD) bacteria named HS2 was isolated and screened from industrial sewage of a chemical factory in Inner Mongolia at 8 °C. The strain was confirmed to be Achromobacter spiritinus, a colorless rod-shaped bacterium. When cultured with sodium succinate as the carbon source, a carbon-to-nitrogen ratio of 20-30, a shaking rate of 150-180 r/min, and an initial pH of 6-10, the strain HS2 exhibited excellent nitrogen removal at 8 °C. Through the results of whole-genome sequencing, gene amplification, and gas product detection, the strain HS2 was determined to possess key enzyme genes in both nitrification and denitrification pathways, suggesting a HN-AD pathway of NH4+-N → NH2OH → NO2-N → NO → N2O → N2. At 8 °C, the strain HS2 could completely remove ammonia nitrogen from industrial sewage with an initial concentration of 127.23 mg/L. Microbial species diversity analysis of the final sewage confirmed Achromobacter sp. as the dominant genus, which indicated that the low-temperature denitrifying strain HS2 plays an important role in nitrogen removal in actual low-temperature sewage.

Correction: Exploring the Mpemba effect: a universal ice pressing enables porous ceramics.

Correction for 'Exploring the Mpemba effect: a universal ice pressing enables porous ceramics' by Xiaodan Yang et al., Mater. Horiz., 2024, DOI: https://doi.org/10.1039/d3mh01869e.

Super Resolution of Pulmonary Nodules Target Reconstruction Using a Two-Channel GAN Models.

RATIONALE AND OBJECTIVES: To develop a Dual generative-adversarial-network (GAN) Cascaded Network (DGCN) for generating super-resolution computed tomography (SRCT) images from normal-resolution CT (NRCT) images and evaluate the performance of DGCN in multi-center datasets. MATERIALS AND METHODS: This retrospective study included 278 patients with chest CT from two hospitals between January 2020 and June 2023, and each patient had all three NRCT (512×512 matrix CT images with a resolution of 0.70 mm, 0.70 mm,1.0 mm), high-resolution CT (HRCT, 1024×1024 matrix CT images with a resolution of 0.35 mm, 0.35 mm,1.0 mm), and ultra-high-resolution CT (UHRCT, 1024×1024 matrix CT images with a resolution of 0.17 mm, 0.17 mm, 0.5 mm) examinations. Initially, a deep chest CT super-resolution residual network (DCRN) was built to generate HRCT from NRCT. Subsequently, we employed the DCRN as a pre-trained model for the training of DGCN to further enhance resolution along all three axes, ultimately yielding SRCT. PSNR, SSIM, FID, subjective evaluation scores, and objective evaluation parameters related to pulmonary nodule segmentation in the testing set were recorded and analyzed. RESULTS: DCRN obtained a PSNR of 52.16, SSIM of 0.9941, FID of 137.713, and an average diameter difference of 0.0981 mm. DGCN obtained a PSNR of 46.50, SSIM of 0.9990, FID of 166.421, and an average diameter difference of 0.0981 mm on 39 testing cases. There were no significant differences between the SRCT and UHRCT images in subjective evaluation. CONCLUSION: Our model exhibited a significant enhancement in generating HRCT and SRCT images and outperformed established methods regarding image quality and clinical segmentation accuracy across both internal and external testing datasets.

Linearized EUV mask optimization based on the adjoint method.

Mask optimization, a compensation method for the thick mask effect and the optical proximity effect in projection lithography, is essential for advanced EUV-enabled production nodes. However, owing to high computation costs and the absence of gradient calculations, it is challenging to optimize EUV masks under rigorous consideration of the thick mask effect. In this work, a linearized EUV mask optimization method based on the adjoint method is proposed to provide fast and effective optimizations. The adjoint method is introduced to calculate the gradient of the EUV mask model. Additionally, a linearized gradient is proposed to quickly compensate for wafer pattern distortion caused by the prominent thick mask effect. Two examples of the EUV mask optimization implemented with a two-step strategy were provided, from which it was observed that the linearized gradient can improve the efficiency by about 40% in the coarse optimization step. The proposed method is promising for accurate full-chip EUV mask optimization.

Treatment of metastatic rectal squamous cell carcinoma in a pregnant patient.

Rectal squamous cell carcinoma is an exceedingly rare form of rectal cancer, with limited data available regarding its presentation and effective treatment. Rectal cancer occurring during pregnancy is uncommon as well. This is a case of metastatic rectal squamous cell carcinoma presenting in a 22-week pregnant, female patient in her early 30s. The patient was treated with 5-fluorouracil and cisplatin and delivered a healthy male child born via uncomplicated vaginal delivery at 35 weeks. This article demonstrates that despite the rare nature of this cancer, in the already rare context of pregnancy, effective and safe treatment is possible with a multidisciplinary team.

CT-Based Intratumoral and Peritumoral Radiomics Nomograms for the Preoperative Prediction of Spread Through Air Spaces in Clinical Stage IA Non-small Cell Lung Cancer.

The study aims to investigate the value of intratumoral and peritumoral radiomics and clinical-radiological features for predicting spread through air spaces (STAS) in patients with clinical stage IA non-small cell lung cancer (NSCLC). A total of 336 NSCLC patients from our hospital were randomly divided into the training cohort (n = 236) and the internal validation cohort (n = 100) at a ratio of 7:3, and 69 patients from the other two external hospitals were collected as the external validation cohort. Univariate and multivariate analyses were used to select clinical-radiological features and construct a clinical model. The GTV, PTV5, PTV10, PTV15, PTV20, GPTV5, GPTV10, GPTV15, and GPTV20 models were constructed based on intratumoral and peritumoral (5 mm, 10 mm, 15 mm, 20 mm) radiomics features. Additionally, the radscore of the optimal radiomics model and clinical-radiological predictors were used to construct a combined model and plot a nomogram. Lastly, the ROC curve and AUC value were used to evaluate the diagnostic performance of the model. Tumor density type (OR = 6.738) and distal ribbon sign (OR = 5.141) were independent risk factors for the occurrence of STAS. The GPTV10 model outperformed the other radiomics models, and its AUC values were 0.887, 0.876, and 0.868 in the three cohorts. The AUC values of the combined model constructed based on GPTV10 radscore and clinical-radiological predictors were 0.901, 0.875, and 0.878. DeLong test results revealed that the combined model was superior to the clinical model in the three cohorts. The nomogram based on GPTV10 radscore and clinical-radiological features exhibited high predictive efficiency for STAS status in NSCLC.

Exploring the Mpemba effect: a universal ice pressing enables porous ceramics.

Piezoceramics with global porosity and local compaction are highly desired to exploit the combination of mechanical and electrical properties. However, achieving such a functional combination is challenging because of the lack of techniques for applying uniform pressure inside porous ceramic green parts. Nature provides many examples of generating strong forces inside the macro and micro channels via the state transformation of water. Inspired by these phenomena, we present a technique of "ice and fire", that is, water freezing (ice pressing) and high-temperature sintering (fire), to produce ideal porous piezoceramics. We introduce a new compaction method called the "ice pressing method", which manipulates liquid phase transition for compaction. This method has several advantages, including uniform pressure distribution, a wide pressure range, high effectiveness, and selective freezing. It can generate an ultrahigh pressure of up to 180 MPa on the piezoceramic green skeletons in minutes while retaining their functional pore structures. By exploiting the Mpemba phenomenon, we further accelerate the compaction procedure by 11%. The first ice-pressed and second fire-consolidated lead zirconate titanate (PZT) ceramics are highly densified and exhibit an outstanding piezoelectric response (d33 = 531 pC N-1), comparable to conventional pressed bulk counterparts and 10-20 times higher than those of unpressed materials. The novel ice pressing method breaks the limitation of lacking a compaction technique for porous ceramics. The versatile and effective ice pressing method is a green and low-cost route promoting applications in sensors, acoustics, water filtration, catalyst substrates, and energy harvesting.

A novel photo-enzyme platform based on non-metallic modified carbon nitride for removal of bisphenol A in water.

A nonmetallic composite photocatalyst with 2D/2D structure was prepared by hydrothermal in-situ polymerization and used for the immobilization of cytochrome C (Cyt c). The photo-enzyme coupling system has a very high enzyme load, which can reach 528.29 mg g-1 after optimization. Compared with free Cyt c, Cytc/PEDOT/CN showed better enzymatic activity, stability and catalytic efficiency. Even after being stored at 100 °C for 60 min, the enzyme activity remained at 49.42 % and remained at 57.89 % after 8 cycles. Moreover, Cytc0.5/PEDOT3/CN showed excellent photocatalytic degradation performance in the degradation experiment of bisphenol A (BPA), reaching 68.22 % degradation rate within 60 min, which was 3.9 times higher than that of pure g-C3N4 and 1.61 times higher than that of pure PEDOT3/CN. This study shows that the introduction of conductive polymers is of great significance to the photo-enzyme coupling system and provides a new strategy for the treatment of phenol-containing wastewater.

Research progress in predicting visceral pleural invasion of lung cancer: a narrative review.

Background and Objective: In lung cancer, visceral pleural invasion (VPI) affects the selection of surgical methods, the scope of lymph node dissection and the need for adjuvant chemotherapy. Preoperative or intraoperative prediction and diagnosis of VPI of lung cancer is helpful for choosing the best treatment plan and improving the prognosis of patients. This review aims to summarize the research progress of the clinical significance of VPI assessment, the intraoperative diagnosis technology of VPI, and various imaging methods for preoperative prediction of VPI. The diagnostic efficacy, advantages and disadvantages of various methods were summarized. The challenges and prospects for future research will also be discussed. Methods: A comprehensive, non-systematic review of the latest literature was carried out in order to investigate the progress of predicting VPI. PubMed database was being examined and the last run was on 4 August 2022. Key Content and Findings: The pathological diagnosis and clinical significance of VPI of lung cancer were discussed in this review. The research progress of prediction and diagnosis of VPI in recent years was summarized. The results showed that preoperative imaging examination and intraoperative freezing pathology were of great value. Conclusions: VPI is one of the adverse prognostic factors in patients with lung cancer. Accurate prediction of VPI status before surgery can provide guidance and help for the selection of clinical operation and postoperative treatment. There are some advantages and limitations in predicting VPI based on traditional computed tomography (CT) signs, 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET)/CT and magnetic resonance imaging (MRI) techniques. As an emerging technology, radiomics and deep learning show great potential and represent the future research direction.

Efficient cocrystal coformer screening based on a Machine learning Strategy: A case study for the preparation of imatinib cocrystal with enhanced physicochemical properties.

Cocrystal engineering, which involves the self-assembly of two or more components into a solid-state supramolecular structure through non-covalent interactions, has emerged as a promising approach to tailor the physicochemical properties of active pharmaceutical ingredient (API). Efficient coformer screening for cocrystal remains a challenge. Herein, a prediction strategy based on machine learning algorithms was employed to predict cocrystal formation and seven reliable models with accuracy over 0.890 were successfully constructed. Imatinib was selected as the model drug and the models established were applied to screen 31 potential coformers. Experimental verification results indicated RF-8 is the optimal model among seven models with an accuracy of 0.839. When the seven models were combined for coformer screening of Imatinib, the combinational model achieved an accuracy of 0.903, and eight new solid forms were observed and characterized. Benefiting from intermolecular interactions, the obtained multicomponent crystals displayed enhanced physicochemical properties. Dissolution and solubility experiments showed the prepared multicomponent crystals had higher cumulative dissolution rate and remarkably improved the solubility of imatinib, and IM-MC exhibited comparable solubility to Imatinib mesylate α form. Stability test and cytotoxicity results showed that multicomponent crystals exhibited excellent stability and the drug-drug cocrystal IM-5F exhibited higher cytotoxicity than pure API.