Freehand 3D Ultrasound Imaging Based on Probe-mounted Vision and IMU System

OBJECTIVES

Freehand three-dimensional (3D) ultrasound (US) is of great significance for clinical diagnosis and treatment, it is often achieved with the aid of external devices (optical and/or electromagnetic, etc.) that monitor the location and orientation of the US probe. However, this external monitoring is often impacted by imaging environment such as optical occlusions and/or electromagnetic (EM) interference.

METHODS

To address the above issues, we integrated a binocular camera and an inertial measurement unit (IMU) on a US probe. Subsequently, we built a tight coupling model utilizing the unscented Kalman algorithm based on Lie groups (UKF-LG), combining vision and inertial information to infer the probe's movement, through which the position and orientation of the US image frame are calculated. Finally, the volume data was reconstructed with the voxel-based hole-filling method.

RESULTS

The experiments including calibration experiments, tracking performance evaluation, phantom scans, and real scenarios scans have been conducted. The results show that the proposed system achieved the accumulated frame position error of 3.78 mm and the orientation error of 0.36° and reconstructed 3D US images with high quality in both phantom and real scenarios.

CONCLUSIONS

The proposed method has been demonstrated to enhance the robustness and effectiveness of freehand 3D US. Follow-up research will focus on improving the accuracy and stability of multi-sensor fusion to make the system more practical in clinical environments.

Development and Validation of a Deep Learning Radiomics Model to Predict High-Risk Pathologic Pulmonary Nodules Using Preoperative Computed Tomography

RATIONALE AND OBJECTIVES

To accurately identify the high-risk pathological factors of pulmonary nodules, our study constructed a model combined with clinical features, radiomics features, and deep transfer learning features to predict high-risk pathological pulmonary nodules.

MATERIALS AND METHODS

The study cohort consisted of 469 cases of lung adenocarcinoma patients, divided into a training cohort (n = 400) and an external validation cohort (n = 69). We obtained computed tomography (CT) semantic features and clinical characteristics, as well as extracted radiomics and deep transfer learning (DTL) features from the CT images. Selected features were used for constructing prediction models using the logistic regression (LR) algorithm. The performance of the models was evaluated through metrics including the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, calibration curve, and decision curve analysis.

RESULTS

The clinical model achieved an AUC of 0.774 (95% CI: 0.728-0.821) in the training cohort and 0.762 (95% confidence interval [CI]: 0.650-0.873) in the external validation cohort. The radiomics model demonstrated an AUC of 0.847 (95% CI: 0.810-0.884) in the training cohort and 0.800 (95% CI: 0.693-0.907) in the external validation cohort. The radiomics-DTL (Rad-DTL) model showed an AUC of 0.871 (95% CI: 0.838-0.905) in the training cohort and 0.806 (95% CI: 0.698-0.914) in the external validation cohort. The proposed combined model yielded AUC values of 0.872 and 0.814 in the training and external validation cohorts, respectively. The combined model demonstrated superiority over both the clinical model and the Rad-DTL model. There were no statistically significant differences observed in the comparison between the combined model incorporating clinical features and the Rad-DTL model. Decision curve analysis (DCA) indicated that the models provided a net benefit in predicting high-risk pathologic pulmonary nodules.

CONCLUSION

Rad-DTL signature is a potential biomarker for predicting high-risk pathologic pulmonary nodules using preoperative CT, determining the appropriate surgical strategy, and guiding the extent of resection.

Clinical evaluation of pulmonary quantitative computed tomography parameters for diagnosing eosinophilic chronic obstructive pulmonary disease: Characteristics and diagnostic performance

Aims

To investigate the characteristics and diagnostic performance of quantitative computed tomography (QCT) parameters in eosinophilic chronic obstructive pulmonary disease (COPD) patients.

Methods

High-resolution CT scans of COPD patients were retrospectively analyzed, and various emphysematous parenchyma measurements, including lung volume (LC), lung mean density (LMD), lung standard deviation (LSD), full-width half maximum (FWHM), and lung relative voxel number (LRVN) were performed. The QCT parameters were compared between eosinophilic and noneosinophilic COPD patients, using a definition of eosinophilic COPD as blood eosinophil values ≥ 300 cells·µL-1 on at least three times. Receiver operating characteristic curves and area under the curve (ROC-AUC) and python were used to evaluate discriminative efficacy of QCT.

Results

Noneosinophilic COPD patients had a significantly lower TLMD (-846.3 ± 47.9 Hounsfield Unit [HU]) and TFWHM(162.5 ± 30.6 HU) compared to eosinophilic COPD patients (-817.8 ± 54.4, 177.3 ± 33.1 HU, respectively) (p = 0.018, 0.03, respectively). Moreover, the total LC (TLC) and TLSD were significantly lower in eosinophilic COPD group (3234.4 ± 1145.8, 183.8 ± 33.9 HU, respectively) than the noneosinophilic COPD group (5600.2 ± 1248.4, 203.5 ± 20.4 HU, respectively) (p = 0.009, 0.002, respectively). The ROC-AUC values for TLC, TLMD, TLSD, and TFWHM were 0.91 (95% confidence interval [CI], 0.828-0.936), 0.66 (95% CI, 0.546-0.761), 0.64 (95% CI, 0.524-0.742), and 0.63 (95% CI, 0.511-0.731), respectively. When the TLC value was 4110 mL, the sensitivity was 90.7% (95% CI, 79.7-96.9), specificity was 77.8% (95% CI, 57.7-91.4) and accuracy was 86.4%. Notably, TLC demonstrated the highest discriminative efficiency with an F1 Score of 0.79, diagnostic Odds Ratio of 34.3 and Matthews Correlation Coefficient of 0.69, surpassing TLMD (0.55, 3.66, 0.25), TLSD (0.56, 3.95, 0.26), and TFWHM (0.56, 4.16, 0.33).

Conclusion

Eosinophilic COPD patients exhibit lower levels of emphysema and a more uniform density distribution throughout the lungs compared to noneosinophilic COPD patients. Furthermore, TLC demonstrated the highest diagnostic efficiency and may serve as a valuable diagnostic marker for distinguishing between the two groups.

[Analysis of ultrasound images features and diagnostic model establishment of alveolar soft part sarcoma and intramuscular capillary-type hemangiomas]

Objective: The ultrasonography features of alveolar soft part sarcoma (ASPS) and intramuscular capillary-type hemangiomas (ICTH) were analyzed, and the diagnostic model of ASPS was established. Methods: A cross-sectional study was carried out. The clinical data of 52 patients [28 males and 24 females, aged (20.7±15.1) years] with pathologically confirmed ASPS and ICTH admitted to People's Hospital of Henan Province from January 2005 to February 2023 were included in the study. According to pathological types, the patients were divided into ASPS group and ICTH group. Clinical data of patients were retrospectively collected, and meaningful indicators in the univariate analysis were included in the regression analysis for screening. After comprehensive consideration of clinical significance and statistical significance, eligible indicators were selected for inclusion in the regression analysis. Binary logistic regression analysis was used to screen the factors that distinguished the pathological types of ASPS and ICTH, and the diagnostic model was established. The area under receiver operating characteristic (ROC) curve (AUC) was used to evaluate the diagnostic effectiveness of the diagnostic model in distinguishing ASPS from ICTH. Results: There were 20 patients in ASPS group, 10 males and 10 females, aged (26.9±13.5) years, and 32 patients in ICTH group, 18 males and 14 females, aged (16.8±15.0) years. The age difference between the ASPS group and the ICTH group was statistically significant (P<0.05), and there were statistically significant differences in the ultrasound imaging features of "clear boundary" "peripheral lobe" "thin blood vessels inside the lesion are straight and out of shape" "intra-lesion liquification" "peripheral thick blood vessels" and "peripheral muscle fiber disruption" between the two groups (all P<0.001).Variables with clinical and statistical significance were selected as independent variables. Binary logistic regression analysis showed that peripheral muscle fiber interruption (OR=97.358, 95%CI:6.833-1 387.249) and internal thin blood vessels were flat and out of shape (OR=0.052, 95%CI:0.003-0.921) was the correlation factor to distinguish the pathological types of ASPS and ICTH. Two ultrasonic image features of "peripheral muscle fiber interruption" and "internal thin blood vessels are straight and out of shape" were used to establish the diagnostic model. The sensitivity of "peripheral muscle fiber interruption" diagnostic model was 81.3%, and the specificity was 95.0%. The AUC was 0.811(95%CI: 0.761-0.954). The sensitivity, specificity and AUC of the diagnosis model of "internal thin vessels with flat misshape" were 90.0%, 96.9% and 0.934(95%CI: 0.830-0.984). The sensitivity, specificity and AUC of the combined diagnosis model of "peripheral muscle fiber interruption" and "internal thin blood vessel straight out of shape" were 96.9%, 90.0% and 0.974(95%CI:0.877-0.999). Conclusion: Ultrasonography can be used to distinguish ASPS from ICTH, and the combined diagnostic model based on the two ultrasonic imaging features of "peripheral muscle fiber interruption" and "internal thin blood vessel straight out of shape" can further improve the diagnostic efficiency.

Highly active antiretroviral therapy-related effects on morphological connectivity in HIV

OBJECTIVE

Suboptimal concentration of the antiretroviral drug is insufficient to inhibit HIV destruction on brain structure and function due to the resistance of blood brain barrier. We aimed to investigate highly active antiretroviral therapy (HAART)-related effects on the morphological connectivity in people with HIV (PWH).

DESIGN

Case-control study.

METHODS

Fifty-five HAART-treated for more than 3 months and 54 untreated PWH, as well as 66 demographically matched healthy controls underwent a high-resolution 3D T1-weighted MRI. Individual-level morphological brain network based on gray matter volume of 90 brain regions was constructed and network topological properties were analyzed. Network-based statistics (NBS) was performed to identify sub-networks showing significant differences in morphological connectivity. Correlation and mediation analyses were employed to evaluate associations between the morphological properties and clinical variables of PWH.

RESULTS

Although PWH exhibited small-world architecture in their morphological brain networks, untreated PWH demonstrated altered network properties while HAART-treated PWH showed relatively similar network properties compared to healthy controls. Furthermore, HAART-related effects were mainly involved the bilateral putamen and left thalamus. The findings of NBS further indicated the cortico-striatum-thalamic-cortical loop was involved in the therapeutic-associated morphological network. The positive correlations between the HAART treatment and nodal degree and efficiency of the putamen were mediated by the number of CD4 + T lymphocytes.

CONCLUSIONS

The topological properties are recovered to normal in PWH after HAART and the effects induced by HAART are mostly within the cortical-subcortical circuit.

Quantitative CT features on admission combined with laboratory biomarkers for predicting severe acute pancreatitis

AIM

To assess the association of quantitative computed tomography (CT) features on admission with acute pancreatitis (AP) severity, and to explore the performance of combined CT and laboratory markers for predicting severe AP (SAP).

MATERIALS AND METHODS

Data from 208 AP patients were reviewed retrospectively. Pancreas volume, the area of extrapancreatic inflammation, extrapancreatic fluid collection volume, and number were calculated based on CT images on admission. Laboratory biomarkers within 24 h of admission were collected. Interobserver agreement for CT measurements was measured by calculating interclass correlation coefficient (ICC). The associations of quantitative CT features with AP severity were evaluated. Predictive models for SAP were constructed based on CT and laboratory markers. Performances of single marker and the models were evaluated using receiver operating characteristic (ROC) curve and area under the ROC curve (AUC).

RESULTS

Pancreas volume, area of extrapancreatic inflammation, extrapancreatic fluid collection volume, and number were significantly different between severe and non-severe AP groups. In predicting SAP, the AUCs of quantitative CT indicators ranged from 0.72 to 0.79; the AUCs of laboratory biomarkers were between 0.53 and 0.66. The combined model of area of extrapancreatic inflammation, serum calcium, and haematocrit yielded an AUC of 0.84, significantly higher than that of the laboratory model, single CT, or laboratory marker. Interobserver agreements for quantitative CT indicators were excellent, with ICC ranging from 0.91 to 0.98.

CONCLUSION

Quantitative CT features on admission were significantly associated with AP severity; the combination of extrapancreatic inflammation area, serum calcium, and haematocrit could be taken as a new method for predicting SAP.

Comparative genomics reveals evolutionary drivers of the dietary shift in Hemiptera

Hemiptera insects exhibit a close relationship to plants and demonstrate a diverse range of dietary preferences, encompassing phytophagy as the predominant feeding habit while a minority engages in carnivorous or haematophagous behaviour. To counteract the challenges posed by phytophagous insects, plants have developed an array of toxic compounds, causing significant evolutionary selection pressure on these insects. In this study, we employed a comparative genomics approach to analyse the expansion and contraction of gene families specific to phytophagous insect lineages, along with their adaptive evolutionary traits, utilising representative species from the Hemiptera order. Our investigation revealed substantial expansions of gene families within the phytophagous lineages, especially in the Pentatomomorpha branch represented by Oncopeltus fasciatus and Riptortus pedestris. Notably, these expansions of gene families encoding enzymes are potentially involved in hemipteran-plant interactions. Moreover, the adaptive evolutionary analysis of these lineages revealed a higher prevalence of adaptively evolved genes in the Pentatomomorpha branch. The observed branch-specific gene expansions and adaptive evolution likely contribute significantly to the diversification of species within Hemiptera. These results help enhance our understanding of the genomic characteristics of the evolution of different feeding habits in hemipteran insects.

Predicting brain age using partition modeling strategy and atlas-based attentional enhancement in the Chinese population

As a biomarker of human brain health during development, brain age is estimated based on subtle differences in brain structure from those under typical developmental. Magnetic resonance imaging (MRI) is a routine diagnostic method in neuroimaging. Brain age prediction based on MRI has been widely studied. However, few studies based on Chinese population have been reported. This study aimed to construct a brain age predictive model for the Chinese population across its lifespan. We developed a partition prediction method based on transfer learning and atlas attention enhancement. The participants were separated into four age groups, and a deep learning model was trained for each group to identify the brain regions most critical for brain age prediction. The Atlas attention-enhancement method was also used to help the models focus only on critical brain regions. The proposed method was validated using 354 participants from domestic datasets. For prediction performance in the testing sets, the mean absolute error was 2.218 ± 1.801 years, and the Pearson correlation coefficient (r) was 0.969, exceeding previous results for wide-range brain age prediction. In conclusion, the proposed method could provide brain age estimation to assist in assessing the status of brain health.

Investigation of rare earth-based magnetic nanocomposites for specific enrichment of exosomes from human plasma

Exosomes, also known as small extracellular vesicles, are widely present in a variety of body fluids (e.g., blood, urine, and saliva). Exosomes are becoming an alternative promising source of diagnostic markers for disease rich in cargo of metabolites, proteins, and nucleic acids. However, due to the low abundance and structure similarity with protein complex, the efficient isolation of exosomes is one of the most important issues for biomedical applications. With a higher order of f-orbitals in rare earth element, it will have strong adsorption toward the phosphate group on the surface of the phospholipid bilayer of exosomes. In this study, we systematically investigated the ability of various rare earths interacting with phosphate-containing molecules and plasma exosomes. One of the best binding europium was selected and used to synthesize core-shell magnetic nanomaterials (Fe3O4@SiO2@Eu2O3) for the enrichment of exosomes from human plasma. The developed nanomaterials exhibited higher enrichment capacity, less time consumption and more convenient handling compared to commonly used ultracentrifugation method. The nanomaterials were applied to separate exosomes from the plasma of patients with hepatocellular carcinoma and healthy controls for metabolomics study with high-resolution mass spectrometry, where 70 differentially expressed metabolites were identified, involving amino acid and lipid metabolic pathway. We anticipated the rare earth-based materials to be an alternative approach on exosome isolation for disease diagnosis or postoperative clinical monitoring.

Diffusely Charged Polymeric Zwitterions as Loosely Hydrated Marine Antifouling Coatings

Polymeric zwitterions exhibit exceptional fouling resistance through the formation of a strongly hydrated surface of immobilized water molecules. While being extensively tested for their performance in biomedical, membrane, and, to a lesser extent, marine environments, few studies have investigated how the molecular design of the zwitterion may enhance its performance. Furthermore, while theories of zwitterion antifouling mechanisms exist for molecular-scale foulant species (e.g., proteins and small molecules), it remains unclear how molecular-scale mechanisms influence the micro- and macroscopic interactions of relevance for marine applications. The present study addresses these gaps through the use of a modular zwitterion chemistry platform, which is characterized by a combination of surface-sensitive sum frequency generation (SFG) vibrational spectroscopy and marine assays. Zwitterions with increasingly delocalized cations demonstrate improved fouling resistance against the green alga Ulva linza. SFG spectra correlate well with the assay results, suggesting that the more diffuse charges exhibit greater surface hydration with more bound water molecules. Hence, the number of bound interfacial water molecules appears to be more influential in determining the marine antifouling activities of zwitterionic polymers than the binding strength of individual water molecules at the interface.

Diamond-Like Carbon: A Surface for Extreme, High-Wear Environments

In this study, we present an in-depth characterization of a diamond-like carbon (DLC) film, using a range of techniques to understand the structure and chemistry of the film both in the interior and particularly at the DLC/air surface and DLC/liquid interface. The DLC film is found to be a combination of sp2 and sp3 carbon, with significant oxygen present at the surface. The oxygen seems to be present as OH groups, making the DLC somewhat hydrophilic. Quartz-Crystal Microbalance (QCM) isotherms and complementary neutron reflectivity data indicate significant adsorption of a model additive, bis(2-ethylhexyl) sulfosuccinate sodium salt (AOT) surfactant, onto the DLC from water solutions and indicate the adsorbed film is a bilayer. This initial study of the structure and composition of a model surfactant is intended to give a clearer insight into how DLC and additives function as antiwear systems.

Beyond Vision: A View from Eye to Alzheimer's Disease and Dementia

With the aging of the global population, the health care burden of Alzheimer's disease (AD) and dementia is considered to increase dramatically in the coming decades. Given the insufficiency of effective interventions for AD and dementia, clinical research on identifying potentially modifiable risk factors and early diagnostic biomarkers becomes a public health priority. Currently, extracerebral manifestations with a large proportion of ocular involvement are usually recognized to precede the symptoms of AD and dementia. Growing epidemiologic evidence also suggests that eye disorders, such as cataracts, age-related macular degeneration, glaucoma, diabetic retinopathy, and so on, are closely associated with and even have a higher incidence of AD and dementia. The eye, as an extension of the central nervous system, therefore has the potential to provide a feasible approach to detecting structural and functional abnormalities of the brain. Numerous new imaging modalities are developed and give novel insights into the detection of several neurodegenerative, vascular, neuropathological, and other ocular abnormalities of AD and dementia in scientific research and clinical application. This review provides an overview of the epidemiologic associations between eye disorders and AD or dementia and summarizes the recent advances in ocular examinations and techniques employed for the detection of AD and dementia. With more brain-and-eye interconnections being identified, the eye is becoming a noninvasive and easily accessible window for the early diagnosis and prevention of AD and dementia.

Factors influencing the detection rate of fumarate peak in 1H MR spectroscopy of fumarate hydratase-deficient renal cell carcinoma at 3 T MRI

AIM

To identify factors that may be associated with fumarate detection rate in 1H-magnetic resonance spectroscopy (MRS) in fumarate hydratase-deficient renal cell carcinoma (FH-RCC).

MATERIALS AND MEHODS

Between February 2018 and March 2022, 16 FH-RCC patients with 30 lesions underwent 1H-MRS. Detection results were classified as having a detected fumarate peak (n=12), undetected peak (n=10), or technical failure (n=8). Factors including tumour size, tumour location, treatment history, and metastasis status were collected and analysed. A Bayesian logistic regression model was applied to evaluate the association between these factors and the detection result.

RESULTS

Bayesian analysis demonstrated significant associations between fumarate detection results and the following factors: long-axis diameter (odds ratio [OR] of 1.64; 95% confidence interval [CI] of 1.07-2.53), short-axis diameter (OR of 1.90; 95% CI of 1.19-3.06), voxel size (OR of 2.85; 95% CI of 1.70-4.75), treatment history (OR of 0.35; 95% CI of 0.21-0.58), non-metastatic state (OR of 2.45; 95% CI of 1.48-4.06), and lymph node metastasis (OR of 0.35; 95% CI of 0.21-0.58). Technical failure results were associated with factors such as treatment history (OR of 2.59; 95% CI of 1.37-4.66), non-metastatic state (OR of 0.36; 95% CI of 0.19-0.66), and lymph node metastasis (OR of 2.61; 95% CI of 1.39-4.74).

CONCLUSION

Tumour size, treatment history, and metastasis character were associated with the detection of abnormal fumarate accumulation. This finding will serve as a reference for interpreting 1H-MRS results and for selecting suitable scenarios to evaluate FH-RCC.

Clonal hematopoiesis related TET2 loss-of-function impedes IL1β-mediated epigenetic reprogramming in hematopoietic stem and progenitor cells

Clonal hematopoiesis (CH) is defined as a single hematopoietic stem/progenitor cell (HSPC) gaining selective advantage over a broader range of HSPCs. When linked to somatic mutations in myeloid malignancy-associated genes, such as TET2-mediated clonal hematopoiesis of indeterminate potential or CHIP, it represents increased risk for hematological malignancies and cardiovascular disease. IL1β is elevated in patients with CHIP, however, its effect is not well understood. Here we show that IL1β promotes expansion of pro-inflammatory monocytes/macrophages, coinciding with a failure in the demethylation of lymphoid and erythroid lineage associated enhancers and transcription factor binding sites, in a mouse model of CHIP with hematopoietic-cell-specific deletion of Tet2. DNA-methylation is significantly lost in wild type HSPCs upon IL1β administration, which is resisted by Tet2-deficient HSPCs, and thus IL1β enhances the self-renewing ability of Tet2-deficient HSPCs by upregulating genes associated with self-renewal and by resisting demethylation of transcription factor binding sites related to terminal differentiation. Using aged mouse models and human progenitors, we demonstrate that targeting IL1 signaling could represent an early intervention strategy in preleukemic disorders. In summary, our results show that Tet2 is an important mediator of an IL1β-promoted epigenetic program to maintain the fine balance between self-renewal and lineage differentiation during hematopoiesis.

CUDC‑101 is a potential target inhibitor for the EGFR‑overexpression bladder cancer cells

Bladder cancer is one of the most common urological malignancies worldwide. The molecular mechanism underlying its development is complex, but its carcinogenesis has been proposed to occur with cell proliferation and resistance to apoptosis, driven by the signaling activity of abundant EGFR and receptor tyrosine‑protein kinase erbB‑2. In the present study, T24 bladder cancer cell lines with EGFR‑overexpression were constructed, before the multi‑target inhibitor CUDC‑101 was used to investigate its potential as a targeted therapeutic agent for bladder cancer using chemosensitivity methods. The results showed that CUDC‑101 induced cytotoxic effects, inhibited growth vitality and proliferation in a dose‑dependent manner. CUDC‑101 also altered the skeletal morphology and microfilament structure, while blocking cell cycle progression and causing apoptosis. These results supported the proposed cytotoxic effects of CUDC‑101, in addition to its inhibitory effects on cell division and proliferation in EGFR‑overexpressing bladder cancer cells. Therefore CUDC‑101 may to be a potential therapeutic option for the treatment of bladder cancer.

Roxadustat protects rat renal tubular epithelial cells from hypoxia-induced injury through the TGF-β1/Smad3 signaling pathway

OBJECTIVE

Roxadustat is used to treat renal anemia. The renoprotective effect of roxadustat needs to be further confirmed, and the mechanism of action is unknown. This study aims to evaluate the effect and mechanism of roxadustat in hypoxia-related nephropathy with the renal tubular epithelial cell line NRK-52E.

MATERIALS AND METHODS

The cell Counting Kit-8 (CCK-8) assay was employed to assess cellular proliferation in the current investigation. Flow cytometry was used to conduct cell apoptosis analysis. The utilization of electron microscopy facilitated the identification of changes in cellular ultrastructure. Immunofluorescence was used to detect the expression trend of hypoxia-inducible factor-1α (HIF-1α). The connective tissue growth factor (CTGF), transforming growth factor-β1 (TGF-β1), Smad family member 3 (Smad3), p-Smad3, α-smooth muscle actin (α-SMA), collagen I, and HIF-1α were assessed by western blotting. Real-time fluorescent quantitative PCR (RT-qPCR) was used to measure TGF-β1 and Smad3 mRNA.

RESULTS

Significant growth inhibition and increased apoptosis were observed in NRK-52E cells cultured under hypoxic conditions (1% and 5% O2), which can be rescued by roxadustat. From a morphological perspective, it has been observed that roxadustat can counteract cellular damage features produced by hypoxia. These features include the contraction of the nuclear envelope and an increase in the formation of apoptotic bodies. Roxadustat increases HIF-1α expression acutely at 24 h, followed by a gradual reduction of HIF-1α expression to levels significantly below that of the hypoxia group by 72 h. Roxadustat can also inhibit hypoxia-induced increased expression of CTGF, TGF-β1, p-Smad3, α-SMA, collagen I, and HIF-1α. Combined treatment with roxadustat and siRNA against TGF-β1 synergistically reduced the expression of CTGF and HIF-1α, while the effect on TGF-β1 and p-Smad3 were comparable to that of the individual treatment alone. Comparably, the combined administration of roxadustat and siRNA targeting Smad3 had a synergistic impact on diminishing the expression of CTGF.

CONCLUSIONS

These findings indicate that roxadustat attenuates experimental renal fibrosis likely by inhibiting the TGF-β1/Smad3 pathways, while its effect on CTGF and HIF-1α may involve other signaling pathways.

Hydration behaviors of nonfouling zwitterionic materials

Zwitterionic materials have emerged as highly effective ultralow fouling materials for many applications, however the underlying mechanism of fouling resistance remains unclear. Using ab initio molecular dynamics simulations and surface-sensitive sum frequency generation vibrational spectroscopy, we studied the hydration behaviors of zwitterionic materials, including trimethylamine-N-oxide (TMAO) and carboxybetaines of different charge-separation distances, to understand their fouling-resistant mechanism and provide a design principle for improved performance. Our study reveals that the interplay among hydrogen bonding, net charge, and dipole moment is crucial to the fouling-resistant capabilities of zwitterionic materials. Shortening of the zwitterionic spacing strengthens hydrogen bonding with water against biomolecule attachment due to the increased electrostatic and induction interactions, charge transfer, and improved structural stability. Moreover, the shortened charge separation reduces the dipole moment of zwitterionic materials with an intrinsic near-neutral net charge, decreasing their electrostatic and dipole-dipole interactions with biofoulers, and increasing their resistance to fouling. Compared to carboxybetaine compounds, TMAO has the shortest zwitterionic spacing and exhibits the strongest hydrogen bonding, the smallest net charge, and the minimum dipole moment, making it an excellent nonfouling material.

Towards texture accurate slice interpolation of medical images using PixelMiner

Quantitative image analysis models are used for medical imaging tasks such as registration, classification, object detection, and segmentation. For these models to be capable of making accurate predictions, they need valid and precise information. We propose PixelMiner, a convolution-based deep-learning model for interpolating computed tomography (CT) imaging slices. PixelMiner was designed to produce texture-accurate slice interpolations by trading off pixel accuracy for texture accuracy. PixelMiner was trained on a dataset of 7829 CT scans and validated using an external dataset. We demonstrated the model's effectiveness by using the structural similarity index (SSIM), peak signal to noise ratio (PSNR), and the root mean squared error (RMSE) of extracted texture features. Additionally, we developed and used a new metric, the mean squared mapped feature error (MSMFE). The performance of PixelMiner was compared to four other interpolation methods: (tri-)linear, (tri-)cubic, windowed sinc (WS), and nearest neighbor (NN). PixelMiner produced texture with a significantly lowest average texture error compared to all other methods with a normalized root mean squared error (NRMSE) of 0.11 (p < .01), and the significantly highest reproducibility with a concordance correlation coefficient (CCC) ≥ 0.85 (p < .01). PixelMiner was not only shown to better preserve features but was also validated using an ablation study by removing auto-regression from the model and was shown to improve segmentations on interpolated slices.

Mutually communicated model based on multi-parametric MRI for automated segmentation and classification of prostate cancer

BACKGROUND

Multiparametric magnetic resonance imaging (mp-MRI) is introduced and established as a noninvasive alternative for prostate cancer (PCa) detection and characterization.

PURPOSE

To develop and evaluate a mutually communicated deep learning segmentation and classification network (MC-DSCN) based on mp-MRI for prostate segmentation and PCa diagnosis.

METHODS

The proposed MC-DSCN can transfer mutual information between segmentation and classification components and facilitate each other in a bootstrapping way. For classification task, the MC-DSCN can transfer the masks produced by the coarse segmentation component to the classification component to exclude irrelevant regions and facilitate classification. For segmentation task, this model can transfer the high-quality localization information learned by the classification component to the fine segmentation component to mitigate the impact of inaccurate localization on segmentation results. Consecutive MRI exams of patients were retrospectively collected from two medical centers (referred to as center A and B). Two experienced radiologists segmented the prostate regions, and the ground truth of the classification refers to the prostate biopsy results. MC-DSCN was designed, trained, and validated using different combinations of distinct MRI sequences as input (e.g., T2-weighted and apparent diffusion coefficient) and the effect of different architectures on the network's performance was tested and discussed. Data from center A were used for training, validation, and internal testing, while another center's data were used for external testing. The statistical analysis is performed to evaluate the performance of the MC-DSCN. The DeLong test and paired t-test were used to assess the performance of classification and segmentation, respectively.

RESULTS

In total, 134 patients were included. The proposed MC-DSCN outperforms the networks that were designed solely for segmentation or classification. Regarding the segmentation task, the classification localization information helped to improve the IOU in center A: from 84.5% to 87.8% (p < 0.01) and in center B: from 83.8% to 87.1% (p < 0.01), while the area under curve (AUC) of PCa classification was improved in center A: from 0.946 to 0.991 (p < 0.02) and in center B: from 0.926 to 0.955 (p < 0.01) as a result of the additional information provided by the prostate segmentation.

CONCLUSION

The proposed architecture could effectively transfer mutual information between segmentation and classification components and facilitate each other in a bootstrapping way, thus outperforming the networks designed to perform only one task.

Six first-line tyrosine kinase inhibitors reveal novel inhibition potential for the EGFR S768I mutation

Lung cancer, the leading cause of cancer-related mortality, is the most commonly diagnosed cancer. Tyrosine kinase inhibitors (TKIs) are considered a drug-targeted therapy for non-small cell lung cancers (NSCLCs) with epidermal growth factor receptor (EGFR) mutations. However, limited data are available involving the activity of EGFR TKIs against rare EGFR mutations. Here, based on an endogenous EGFR-depleted cell Line H3255 by CRISPR, H3255 cells with rare mutant EGFR^S768I and compound mutations EGFR^S768I+L858R were tested using cell proliferation assay, cytotoxicity, membrane potential, flow cytometry and Western blot analysis. We conducted cytotoxicity screening of EGFR mutations on six front-line TKIs based on first-, second-, and third-generation TKIs (afatinib, dacomitinib, osimertinib, erlotinib, gefitinib, and icotinib). The results showed that the sensitivity of these mutants containing rare variants EGFR^S768I to six front-line TKIs was enriched in the irreversible TKI cytotoxicity assays by determining their change in cytotoxicity, apoptosis, cell proliferation and signal pathway factors. Importantly, the variants harboring EGFR^L858R (H3255), EGFR^S768I (H3255^S768I) and EGFR^S768I+L858R (H3255^S768I+L858R) were sensitive to six TKIs and induced cytotoxicity through different pathways. Moreover, the compound mutations EGFR^S768I+L858R showed more TKI resistance than EGFR^S768I mutation and EGFR^L858R mutation. We present a comprehensive reference for the sensitivity of EGFR^S768I variants to six front-line TKIs. For patients with the EGFR S768I mutation and compound mutations EGFR^S768I+L858R, six first-line TKIs appear to be reasonable therapeutic options.

Identification of novel characteristic biomarkers and immune infiltration profile for the anaplastic thyroid cancer via machine learning algorithms

PURPOSE

Anaplastic thyroid cancer (ATC) is a rare and lethal malignant cancer. In recent years, the application of molecular-driven targeted therapy and immunotherapy has markedly improved the prognosis of ATC. This study aimed to identify characteristic genes for ATC diagnosis and revealed the role of ATC characteristic genes in drug sensitivity and immune cell infiltration.

METHODS

We downloaded ATC RNA-sequencing data from the GEO database. Following the combination and normalization of the dataset, we first divided the combined datasets into the training cohort and the validation cohort. We identified differentially expressed genes (DEGs) in ATC by differential expression analysis in the training cohort. We used two machine learning algorithms, least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE) to identify ATC characteristic genes. The CIBERSORT algorithm was performed to calculate the abundance of various immune cells in ATC. Finally, we validated the expression of ATC characteristic genes by quantitative RT-PCR (RT-qPCR) in ATC cell lines and immunohistochemistry (IHC).

RESULTS

A total of 425 DEGs were identified in the training cohort, including 240 upregulated genes and 185 downregulated genes. Four ATC characteristic genes (ADM, PXDN, MMP1, and TFF3) were identified, and their diagnostic value was validated in the validation cohort (AUC in ROC analysis > 0.75). We established a practical gene expression-based nomogram to accurately predict the probability of ATC. We also found that ATC characteristic biomarkers are associated with the tumor immune microenvironment and drug sensitivity.

CONCLUSION

ADM, PXDN, MMP1, and TFF3 might serve as potential ATC diagnostic biomarkers and may be helpful for ATC molecular targeted therapy and immunotherapy.

Defining the sensitivity landscape of EGFR variants to tyrosine kinase inhibitors

Tyrosine kinase inhibitor (TKI) is a standard treatment for patients with NSCLC harboring constitutively active epidermal growth factor receptor (EGFR) mutations. However, most rare EGFR mutations lack treatment regimens except for the well-studied ones. We constructed two EGFR variant libraries containing substitutions, deletions, or insertions using the saturation mutagenesis method. All the variants were located in the EGFR mutation hotspot (exons 18-21). The sensitivity of these variants to afatinib, erlotinib, gefitinib, icotinib, and osimertinib was systematically studied by determining their enrichment in massively parallel cytotoxicity assays using an endogenous EGFR-depleted cell line. A total of 3914 and 70,475 variants were detected in the constructed EGFR Substitution-Deletion (Sub-Del) and exon 20 Insertion (Ins) libraries. Of the 3914 Sub-Del variants, 221 proliferated fast in the control assay and were sensitive to EGFR-TKIs. For the 70,475 Ins variants, insertions at amino acid positions 770-774 were highly enriched in all 5 TKI cytotoxicity assays. Moreover, the top 5% of the enriched insertion variants included a glycine or serine insertion at high frequency. We present a comprehensive reference for the sensitivity of EGFR variants to five commonly used TKIs. The approach used here should be applicable to other genes and targeted drugs. BACKGROUND: Tyrosine kinase inhibitors (TKIs) therapy is a standard treatment for patients with advanced non-small-cell lung carcinoma (NSCLC) when activating epidermal growth factor receptor (EGFR) mutations are detected. However, except for the well-studied EGFR mutations, most EGFR mutations lack treatment regimens. TRANSLATIONAL SIGNIFICANCE: The results demonstrated that patients with rare EGFR mutations were most likely to benefit from osimertinib therapy compared to afatinib, erlotinib, gefitinib, or icotinib therapy. This study provides a case of deep mutational scanning that simultaneously assayed substitution, deletion, and insertion variants. This approach is applicable for other oncogenes and targeted drugs.

[Application of branch-first technique in total thoracic aorta replacement: short and medium term effect of 11 cases]

Objective: To examine the short and medium term effect of branch-first technique in total thoracic aorta replacement. Methods: The clinical data of eleven patients with ascending aortic aneurysms or type A aortic dissection+Crawford Ⅰ or Ⅱ total thoracoabdominal aortic aneurysm who were treated at Department of Cardiovascular Surgery in Henan Province Chest Hospital from January 2018 to July 2021 were retrospectively analyzed. There were 7 males and 4 females, aging (38±5) years (range: 28 to 45 years), 7 cases of whom were diagnosed with Marfan syndrome, 1 case was diagnosed with coarctation of aorta. Operations were performed under mild hypothermic and branch-first technique. Firstly, the middle and small incision in the chest was combined with the 6th intercostal incision in the left posterior lateral side. Secondly, four branches artificial blood vessels were anastomosed with the brachiocephalic artery to ensure the blood supply to the brain. After the circulation was blocked, intracardiac and aortic proximal operations were performed. Intercostal artery reconstruction and thoracic descending aorta replacement were completed after opening circulation. Results: The operative time of this group was (645.9±91.7) minutes (range: 505 to 840 minutes). One case had cerebral infarction and 1 case had chylothorax. The patients were followed up 4 to 47 months, 1 patient underwent thoracic and abdominal aorta+iliac artery resection and replacement due to the progression of abdominal aortic aneurysm 3 months after operation. Intercostal artery obstruction occurred in 2 cases, and the rest lived well. Conclusions: One-stage whole thoracic aorta replacement with branch-first technique has satisfactory results in the short and medium term, with no risk of residual aortic aneurysm rupture. It is an effective treatment for young and organs function well patients with complex aortic lesions.

Sensitivity analysis of EGFR L861Q mutation to six tyrosine kinase inhibitors

PURPOSE

Lung cancer is one of the most common carcinomas with the highest mortality in the world. Non-small cell lung carcinoma has a large proportion of epidermal growth factor receptor (EGFR) mutations, of which rare EGFR mutations account for about 10%-20%. Currently, tyrosine kinase inhibitors (TKIs) therapy is a standard treatment for patients with non-small cell lung carcinoma with EGFR mutations. To date, the toxicological effects of the EGFR L861Q variant (less than 2%) have been rarely reported, so further investigation of its sensitivity to six first-in-class TKIs is of great clinical interest.

METHODS

In this study, two EGFR L861Q variants cell lines (EGFR L861Q variant and EGFR L861Q + exon 19 deletion variant) were established by CRISPR-Cas9 gene-editing technology. The steady-state plasma concentrations of six TKIs (gefitinib/erlotinib/icotinib, the first generation; dacomitinib/afatinib, the second generation; and osimertinib, the third generation) were tested, respectively. The change of cell viability, proliferation, cloning ability, mitochondrial membrane potential and apoptosis were detected by MTT assay, EdU staining assay, colony formation assay, mitochondrial membrane potential and apoptosis test. TUNEL and Annexin V / PI staining were used to detect cell apoptosis, and flow cytometry was employed to explore the sensitivity of two variants to six TKIs.

RESULTS

Our study indicated that the six TKIs inhibited the viability of the two cell lines in a time-dependent manner, and the inhibitory time of six TKIs on proliferation was different between the two cell lines. The proliferation and cloning ability of two cell lines were inhibited by six TKIs. The cytoskeleton morphology, microfilament structure and distribution of the two cell lines were changed by six TKIs. Compared with the control, the mitochondrial membrane potential decreased while the apoptosis increased of the two of variants after treatment with the six TKIs, and the associated mechanisms were elucidated.

CONCLUSIONS

Based on the above results, EGFR L861Q + 19del variant and EGFR L861Q variant showed significant sensitivity to six first-in-class TKIs. Among the six TKIs, the first generation TKIs (gefitinib/erlotinib/icotinib), showed stronger inhibition ability to the EGFR L861Q + 19del variant and EGFR L861Q variant, among which gefitinib showed the strongest inhibition.

P15.11.A 18F-Fluciclovine PET/CT to distinguish radiation necrosis from tumour progression in brain metastases treated with stereotactic radiosurgery: results of a prospective pilot study

Background

Amino acid PET radiopharmaceutical, 18F-fluciclovine, shows increased uptake in brain tumors relative to normal tissue and may be a useful tool for detecting recurrent brain metastases. Here, we report results from a prospective pilot study evaluating the use of 18F-fluciclovine PET/CT to distinguish radiation necrosis from tumour progression among patients with brain metastases treated with stereotactic radiosurgery (SRS).

Material and Methods

The primary objective was to estimate the accuracy of 18F-fluciclovine PET/CT in distinguishing radiation necrosis from tumour progression. The trial included adults with brain metastases who underwent SRS and presented with a follow up MRI brain (with DSC MR perfusion) which was equivocal for radiation necrosis versus tumour progression. Within 30 days of equivocal MRI brain, patients underwent an 18F-fluciclovine PET/CT (Siemens mCT) acquired 5-15 min post-injection with images generated by PSF reconstruction. Quantitative metrics for each lesion were documented and lesion to normal brain SUVmean ratios were calculated. The reference standard for diagnosis of radiation necrosis vs tumour progression was clinical follow up with MRI brain every 2-4 months until multidisciplinary consensus or tissue confirmation.

Results

Of 16 patients enrolled between 7/2019-11/2020, 1 patient died prior to diagnosis, allowing 15 evaluable subjects with 20 lesions. Primary histology was NSCLC in 9 (45%) lesions, breast in 7 (35%), melanoma in 3 (15%), and endometrial in 1 (5%). The final diagnosis was radiation necrosis in 16 (80%) lesions and tumour progression in 4 (20%). SUVmax was a statistically significant predictor of tumour progression (P = 0.011), with higher SUVmax values indicative of tumour progression. The area under the ROC curve was 0.833 (95% CI: 0.590, 1.0). A cutoff of 4.3 provided a sensitivity to identify tumour progression of 1.0 (4/4) and specificity to rule out tumour progression of 0.63 (10/16). SUVmean (P = 0.018), SUVpeak (P = 0.007), and SUVpeak/normal (P = 0.002) also reached statistical significance as predictors of tumour progression, with higher SUVmax values indicative of tumour progression. SUVmax/normal (P = 0.1) and SUVmean/normal (P = 0.5) were not statistically significant. The AUC for SUVmax was not significantly higher than the AUCs for the other quantitative variables (P-values &gt; 0.2).

Conclusion

In this prospective pilot study, 18F Fluciclovine PET/CT demonstrated promising accuracy to distinguish radiation necrosis from tumour progression among patients with brain metastases previously treated with SRS. Using SUVmax, a cutpoint of 4.3 provided a sensitivity of 1.0 and specificity of 0.63. Confirmatory phase II and III studies are ongoing.

3D-Stacked Multistage Inertial Microfluidic Chip for High-Throughput Enrichment of Circulating Tumor Cells

Whether for cancer diagnosis or single-cell analysis, it remains a major challenge to isolate the target sample cells from a large background cell for high-efficiency downstream detection and analysis in an integrated chip. Therefore, in this paper, we propose a 3D-stacked multistage inertial microfluidic sorting chip for high-throughput enrichment of circulating tumor cells (CTCs) and convenient downstream analysis. In this chip, the first stage is a spiral channel with a trapezoidal cross-section, which has better separation performance than a spiral channel with a rectangular cross-section. The second and third stages adopt symmetrical square serpentine channels with different rectangular cross-section widths for further separation and enrichment of sample cells reducing the outlet flow rate for easier downstream detection and analysis. The multistage channel can separate 5 μm and 15 μm particles with a separation efficiency of 92.37% and purity of 98.10% at a high inlet flow rate of 1.3 mL/min. Meanwhile, it can separate tumor cells (SW480, A549, and Caki-1) from massive red blood cells (RBCs) with a separation efficiency of >80%, separation purity of >90%, and a concentration fold of ~20. The proposed work is aimed at providing a high-throughput sample processing system that can be easily integrated with flowing sample detection methods for rapid CTC analysis.

Diagnosis of Idiopathic Pulmonary Fibrosis in High-Resolution Computed Tomography Scans Using a Combination of Handcrafted Radiomics and Deep Learning

Purpose

To develop handcrafted radiomics (HCR) and deep learning (DL) based automated diagnostic tools that can differentiate between idiopathic pulmonary fibrosis (IPF) and non-IPF interstitial lung diseases (ILDs) in patients using high-resolution computed tomography (HRCT) scans.

Material and Methods

In this retrospective study, 474 HRCT scans were included (mean age, 64.10 years ± 9.57 [SD]). Five-fold cross-validation was performed on 365 HRCT scans. Furthermore, an external dataset comprising 109 patients was used as a test set. An HCR model, a DL model, and an ensemble of HCR and DL model were developed. A virtual in-silico trial was conducted with two radiologists and one pulmonologist on the same external test set for performance comparison. The performance was compared using DeLong method and McNemar test. Shapley Additive exPlanations (SHAP) plots and Grad-CAM heatmaps were used for the post-hoc interpretability of HCR and DL models, respectively.

Results

In five-fold cross-validation, the HCR model, DL model, and the ensemble of HCR and DL models achieved accuracies of 76.2 ± 6.8, 77.9 ± 4.6, and 85.2 ± 2.7%, respectively. For the diagnosis of IPF and non-IPF ILDs on the external test set, the HCR, DL, and the ensemble of HCR and DL models achieved accuracies of 76.1, 77.9, and 85.3%, respectively. The ensemble model outperformed the diagnostic performance of clinicians who achieved a mean accuracy of 66.3 ± 6.7% (p < 0.05) during the in-silico trial. The area under the receiver operating characteristic curve (AUC) for the ensemble model on the test set was 0.917 which was significantly higher than the HCR model (0.817, p = 0.02) and the DL model (0.823, p = 0.005). The agreement between HCR and DL models was 61.4%, and the accuracy and specificity for the predictions when both the models agree were 93 and 97%, respectively. SHAP analysis showed the texture features as the most important features for IPF diagnosis and Grad-CAM showed that the model focused on the clinically relevant part of the image.

Conclusion

Deep learning and HCR models can complement each other and serve as useful clinical aids for the diagnosis of IPF and non-IPF ILDs.

Automated detection and segmentation of non-small cell lung cancer computed tomography images

Detection and segmentation of abnormalities on medical images is highly important for patient management including diagnosis, radiotherapy, response evaluation, as well as for quantitative image research. We present a fully automated pipeline for the detection and volumetric segmentation of non-small cell lung cancer (NSCLC) developed and validated on 1328 thoracic CT scans from 8 institutions. Along with quantitative performance detailed by image slice thickness, tumor size, image interpretation difficulty, and tumor location, we report an in-silico prospective clinical trial, where we show that the proposed method is faster and more reproducible compared to the experts. Moreover, we demonstrate that on average, radiologists & radiation oncologists preferred automatic segmentations in 56% of the cases. Additionally, we evaluate the prognostic power of the automatic contours by applying RECIST criteria and measuring the tumor volumes. Segmentations by our method stratified patients into low and high survival groups with higher significance compared to those methods based on manual contours.

Correct interpretation of computer tomography (CT) scans is important for the correct assessment of a patient’s disease but can be subjective and timely. Here, the authors develop a system that can automatically segment the non-small cell lung cancer on CT images of patients and show in an in silico trial that the method was faster and more reproducible than clinicians.

Machine learning-based combined nomogram for predicting the risk of pulmonary invasive fungal infection in severely immunocompromised patients

Background

Early and accurate diagnosis of invasive fungal infection (IFI) is pivotal for the initiation of effective antifungal therapy for patients with hematologic malignancies.

Methods

This retrospective study involved 235 patients with hematologic malignancies and pulmonary infections diagnosed as IFIs (n=118) or bacterial pneumonia (n=117). Patients were randomly divided into training (n=188) and validation (n=47) datasets. Four feature selection methods with nine classifiers were implemented to select the optimal machine learning (ML) model using five-fold cross-validation. A radiomic signature was constructed using a linear ML algorithm, and a radiomic score (Radscore) was calculated. The combined model was developed with the Radscore, the significant clinical and radiologic factors were selected using multivariable logistic regression, and the results were presented as a clinical radiomic nomogram. A prospective pilot study was also conducted to compare the classification performance of the combined nomogram with practicing radiologists.

Results

Significant differences were found in the Radscore between IFI and bacterial pneumonia patients in the training (0.683 vs. −0.724, P<0.001) and validation set (0.353 vs. −0.717, P=0.002). The combined model showed good discrimination performance in the validation cohort [area under the curve (AUC) =0.844] and outperformed the clinical (AUC =0.696) and radiomics (AUC =0.767) model alone (both P<0.05).

Conclusions

The clinical radiomic nomogram can serve as a promising predictive tool for IFI in patients with hematologic malignancies.

Self-Amplifying Assembly of Peptides in Macrophages for Enhanced Inflammatory Treatment

Enzyme-regulated in situ self-assembly of peptides represents one versatile strategy in the creation of theranostic agents, which, however, is limited by the strong dependence on enzyme overexpression. Herein, we reported the self-amplifying assembly of peptides precisely in macrophages associated with enzyme expression for improving the anti-inflammatory efficacy of conventional drugs. The self-amplifying assembling system was created via coassembling an enzyme-responsive peptide with its derivative functionalized with a protein ligand. Reduction of the peptides by the enzyme NAD(P)H quinone dehydrogenase 1 (NQO1) led to the formation of nanofibers with high affinity to the protein, thereby facilitating NQO1 expression. The improved NQO1 level conversely promoted the assembly of the peptides into nanofibers, thus establishing an amplifying relationship between the peptide assembly and the NQO1 expression in macrophages. Utilization of the amplifying assembling system as vehicles for drug dexamethasone allowed for its passive targeting delivery to acute injured lungs. Both in vitro and in vivo studies confirmed the capability of the self-amplifying assembling system to enhance the anti-inflammatory efficacy of dexamethasone via simultaneous alleviation of the reactive oxygen species side effect and downregulation of proinflammatory cytokines. Our findings demonstrate the manipulation of the assembly of peptides in living cells with a regular enzyme level via a self-amplification process, thus providing a unique strategy for the creation of supramolecular theranostic agents in living cells.

To explore the mechanism of tobacco addiction using structural and functional MRI: a preliminary study of the role of the cerebellum-striatum circuit

Previous studies have found that the striatum and the cerebellum played important roles in nicotine dependence, respectively. In heavy smokers, however, the effect of resting-state functional connectivity of cerebellum-striatum circuits in nicotine dependence remained unknown. This study aimed to explore the role of the circuit between the striatum and the cerebellum in addiction in heavy smokers using structural and functional magnetic resonance imaging. The grey matter volume differences and the resting-state functional connectivity differences in cerebellum-striatum circuits were investigated between 23 heavy smokers and 23 healthy controls. The cigarette dependence in heavy smokers and healthy controls were evaluated by using Fagerström Test. Then, we applied mediation analysis to test whether the resting-state functional connectivity between the striatum and the cerebellum mediates the relationship between the striatum morphometry and the nicotine dependence in heavy smokers. Compared with healthy controls, the heavy smokers' grey matter volumes decreased significantly in the cerebrum (bilateral), and increased significantly in the caudate (bilateral). Seed-based resting-state functional connectivity analysis showed significantly higher resting-state functional connectivity among the bilateral caudate, the left cerebellum, and the right middle temporal gyrus in heavy smokers. The cerebellum-striatum resting-state functional connectivity fully mediated the relationship between the striatum morphometry and the nicotine dependence in heavy smokers. Heavy smokers showed abnormal interactions and functional connectivity between the striatum and the cerebellum, which were associated with the striatum morphometry and nicotine dependence. Such findings could provide new insights into the neural correlates of nicotine dependence in heavy smokers.

Optimization of cationic polymer-mediated transfection for RNA interference

Transfection efficiency was estimated to optimize the conditions for RNA interference (RNAi), including transfection time, validity, and nucleic acid concentration and type, using the EZ Trans Cell Reagent, a cationic polymer. An shRNA against GFP was designed and transfected into cells using the EZ transfection reagent. The shRNA significantly decreased the expression of GFP. In addition, pre-diluted transfection reagent at room temperature and small nucleic acids increased the transfection efficiency, which peaked at 24 h. Compared with circular nucleic acids, linear nucleic acids showed higher transfection efficiency and a higher genome integration rate. We optimized cationic polymer-mediated RNAi conditions, and our data will be useful for future RNAi studies.

Molecular simulation of linear octacosane via a CG10 coarse grain scheme

Following our previous work on the united-atom simulation on octacosane (C₂₈H₅₈) (Dai et al., Phys. Chem. Chem. Phys., 2021, 23, 21262-21271), we developed a coarse grain scheme (CG10), which is able to reproduce the pivotal phase characteristics of octacosane with highly improved computational efficiency. The CG10 octacosane chain was composed of 10 consecutive beads, maintaining the fundamental zigzag chain morphology. When the potential functions were set up and the coefficients were parameterized, our CG10 models yielded solid phase diagrams and transitions during an annealing process. We also detected the melting point by various means: direct observation, bond order, density tracking, and an enthalpy plot. Furthermore, our CG10 successfully reproduced the liquid density with only 2% underestimation, indicating its applicability across the solid and liquid phases. Therefore, with the ability to reproduce critical structure and property characteristics, our CG10 scheme provides an effective means of numerically modelling octacosane with highly improved computational efficiency.

A Handcrafted Radiomics-Based Model for the Diagnosis of Usual Interstitial Pneumonia in Patients with Idiopathic Pulmonary Fibrosis

The most common idiopathic interstitial lung disease (ILD) is idiopathic pulmonary fibrosis (IPF). It can be identified by the presence of usual interstitial pneumonia (UIP) via high-resolution computed tomography (HRCT) or with the use of a lung biopsy. We hypothesized that a CT-based approach using handcrafted radiomics might be able to identify IPF patients with a radiological or histological UIP pattern from those with an ILD or normal lungs. A total of 328 patients from one center and two databases participated in this study. Each participant had their lungs automatically contoured and sectorized. The best radiomic features were selected for the random forest classifier and performance was assessed using the area under the receiver operator characteristics curve (AUC). A significant difference in the volume of the trachea was seen between a normal state, IPF, and non-IPF ILD. Between normal and fibrotic lungs, the AUC of the classification model was 1.0 in validation. When classifying between IPF with a typical HRCT UIP pattern and non-IPF ILD the AUC was 0.96 in validation. When classifying between IPF with UIP (radiological or biopsy-proved) and non-IPF ILD, an AUC of 0.66 was achieved in the testing dataset. Classification between normal, IPF/UIP, and other ILDs using radiomics could help discriminate between different types of ILDs via HRCT, which are hardly recognizable with visual assessments. Radiomic features could become a valuable tool for computer-aided decision-making in imaging, and reduce the need for unnecessary biopsies.