Chemoenzymatic synthesis of sialyl-α2,3-lactoside-functionalized BSA conjugate inhibits influenza infection.

Influenza remains a global public health threat, and the development of new antivirals is crucial to combat emerging drug-resistant influenza strains. In this study, we report the synthesis and evaluation of a sialyl lactosyl (TS)-bovine serum albumin (BSA) conjugate as a potential multivalent inhibitor of the influenza virus. The key trisaccharide component, TS, was efficiently prepared via a chemoenzymatic approach, followed by conjugation to dibenzocyclooctyne-modified BSA via a strain-promoted azide-alkyne cycloaddition reaction. Biophysical and biochemical assays, including surface plasmon resonance, isothermal titration calorimetry, hemagglutination inhibition, and neuraminidase inhibition, demonstrated the strong binding affinity of TS-BSA to the hemagglutinin (HA) and neuraminidase (NA) proteins of the influenza virus as well as intact virion particles. Notably, TS-BSA exhibited potent inhibitory activity against viral entry and release, preventing cytopathic effects in cell culture. This multivalent presentation strategy highlights the potential of glycocluster-based antivirals for combating influenza and other drug-resistant viral strains.

A Zanamivir-protein conjugate mimicking mucin for trapping influenza virion particles and inhibiting neuraminidase activity.

Influenza viruses contribute significantly to the global health burden, necessitating the development of strategies against transmission as well as effective antiviral treatments. The present study reports a biomimetic strategy inspired by the natural antiviral properties of mucins. A bovine serum albumin (BSA) conjugate decorated with the multivalent neuraminidase inhibitor Zanamivir (ZA-BSA) was synthesized using copper-free click chemistry. This synthetic pseudo-mucin exhibited potent neuraminidase inhibitory activity against several influenza strains. Virus capture and growth inhibition assays demonstrated its effective absorption of virion particles and ability to prevent viral infection in nanomolar concentrations. Investigation of the underlying antiviral mechanism of ZA-BSA revealed a dual mode of action, involving disruption of the initial stages of host-cell binding and fusion by inducing viral aggregation, followed by blocking the release of newly assembled virions by targeting neuraminidase activity. Notably, the conjugate also exhibited potent inhibitory activity against Oseltamivir-resistant neuraminidase variant comparable to the monomeric Zanamivir. These findings highlight the application of multivalent drug presentation on protein scaffold to mimic mucin adsorption of viruses, together with counteracting drug resistance. This innovative approach has potential for the creation of antiviral agents against influenza and other viral infections.

Optimizing the image quality of peripancreatic blood vessels through the clinical application of single-energy spectral computed tomography (CT) imaging.

Background: With the increase of pancreatic tumor patients in recent years, there is an urgent need to find a way to treat pancreatic tumors. Surgery is one of the best methods for the treatment of pancreatic tumors, the success of which depends on the evaluation of peripancreatic vessels before surgery. Computed tomography (CT), as a non-invasive, fast, and economical auxiliary examination method, is undoubtedly one of the best means of clinical auxiliary examination. In this study, we investigated the impact of single-energy spectral CT imaging on the image quality of peripancreatic blood vessels and the clinical value of low-keV imaging in enhancing the image quality of peripancreatic arteriovenous vessels. Methods: We prospectively enrolled 103 patients who underwent abdominal vascular-enhanced CT examinations at the Affiliated Hospital of Hebei University between December 2022 and May 2023 and who were all scanned with the dual-energy feature on the United Imaging ATLAS scanner. The images were reconstructed at 70 keV, mixed energy, and optimized single energy in the post-processing station of United Imaging Healthcare Technology Co., Ltd. The CT value and contrast-to-noise ratio (CNR) of the superior mesenteric artery (SMA), gastroduodenal artery (GDA), inferior pancreaticoduodenal artery (IPDA), and superior mesenteric vein (SMV) were compared across energy levels, and then the image quality was subjectively evaluated. One-way analysis of variance and rank-sum tests were utilized for the statistical analysis. Results: The CT values of SMA, GDA, IPDA, and SMV in the optimal single energy group were 358.37±70.24, 323.36±88.23, 300.76±76.27, and 257.74±20.56 Hounsfield unit (HU), respectively, which were superior to those in the mixed energy (241.66±47.69, 235.17±53.71, 207.36±45.17, and 187.39±23.21 HU) and 70 keV groups (260.89±54.27, 252.41±58.87, 223.17±43.65, and 203.18±18.17 HU) (P<0.05). The diagnostic efficacy was greater in the optimal single energy group than in the other 2 groups (4.63±0.50, 3.91±0.57, and 4.23±0.83) (P<0.05). Conclusions: The optimal single energy for showing peripancreatic blood vessels is 62±7 keV when utilizing single-energy spectral CT imaging.

Quantitative radiomics analysis of imaging features in adults and children Mycoplasma pneumonia.

Purpose: This study aims to explore the value of clinical features, CT imaging signs, and radiomics features in differentiating between adults and children with Mycoplasma pneumonia and seeking quantitative radiomic representations of CT imaging signs. Materials and methods: In a retrospective analysis of 981 cases of mycoplasmal pneumonia patients from November 2021 to December 2023, 590 internal data (adults:450, children: 140) randomly divided into a training set and a validation set with an 8:2 ratio and 391 external test data (adults:121; children:270) were included. Using univariate analysis, CT imaging signs and clinical features with significant differences (p < 0.05) were selected. After segmenting the lesion area on the CT image as the region of interest, 1,904 radiomic features were extracted. Then, Pearson correlation analysis (PCC) and the least absolute shrinkage and selection operator (LASSO) were used to select the radiomic features. Based on the selected features, multivariable logistic regression analysis was used to establish the clinical model, CT image model, radiomic model, and combined model. The predictive performance of each model was evaluated using ROC curves, AUC, sensitivity, specificity, accuracy, and precision. The AUC between each model was compared using the Delong test. Importantly, the radiomics features and quantitative and qualitative CT image features were analyzed using Pearson correlation analysis and analysis of variance, respectively. Results: For the individual model, the radiomics model, which was built using 45 selected features, achieved the highest AUCs in the training set, validation set, and external test set, which were 0.995 (0.992, 0.998), 0.952 (0.921, 0.978), and 0.969 (0.953, 0.982), respectively. In all models, the combined model achieved the highest AUCs, which were 0.996 (0.993, 0.998), 0.972 (0.942, 0.995), and 0.986 (0.976, 0.993) in the training set, validation set, and test set, respectively. In addition, we selected 11 radiomics features and CT image features with a correlation coefficient r greater than 0.35. Conclusion: The combined model has good diagnostic performance for differentiating between adults and children with mycoplasmal pneumonia, and different CT imaging signs are quantitatively represented by radiomics.

Development of Vogt-Koyanagi-Harada disease-like uveitis during treatment by anti-programmed death-1 antibody: a case report.

BACKGROUND: Several immune checkpoint inhibitors (ICIs) have been linked to the occurrence of Vogt-Koyanagi-Harada disease (VKHD)-like uveitis. Among the ICIs, there has been no report of immune-related adverse events (irAEs) caused by a new programmed death protein-1(PD-1) monoclonal antibody (Toripalimab). CASE PRESENTATION: This paper presents a case of VKHD-like uveitis that arose following Toripalimab therapy for urothelial cancer of the bladder, and the patient experienced symptoms 10 days after the final dosage of 20 months of medication treatment. This patient with bladder uroepithelial carcinoma had severe binocular acute panuveitis with exudative retinal detachment after receiving Toripalimab therapy. Binocular VKHD-like uveitis was suggested as a diagnosis. Both eyes recovered after discontinuing immune checkpoint inhibitors and local and systemic corticosteroid treatment. CONCLUSIONS: This report suggests that VKHD-like uveitis can also occur in patients receiving novel PD-1 antibodies and the importance of paying attention to eye complications in patients receiving treatment over a long period.

A study on the application of radiomics based on cardiac MR non-enhanced cine sequence in the early diagnosis of hypertensive heart disease.

BACKGROUND: The prevalence of hypertensive heart disease (HHD) is high and there is currently no easy way to detect early HHD. Explore the application of radiomics using cardiac magnetic resonance (CMR) non-enhanced cine sequences in diagnosing HHD and latent cardiac changes caused by hypertension. METHODS: 132 patients who underwent CMR scanning were divided into groups: HHD (42), hypertension with normal cardiac structure and function (HWN) group (46), and normal control (NOR) group (44). Myocardial regions of the end-diastolic (ED) and end-systolic (ES) phases of the CMR short-axis cine sequence images were segmented into regions of interest (ROI). Three feature subsets (ED, ES, and ED combined with ES) were established after radiomic least absolute shrinkage and selection operator feature selection. Nine radiomic models were built using random forest (RF), support vector machine (SVM), and naive Bayes. Model performance was analyzed using receiver operating characteristic curves, and metrics like accuracy, area under the curve (AUC), precision, recall, and specificity. RESULTS: The feature subsets included first-order, shape, and texture features. SVM of ED combined with ES achieved the highest accuracy (0.833), with a macro-average AUC of 0.941. AUCs for HHD, HWN, and NOR identification were 0.967, 0.876, and 0.963, respectively. Precisions were 0.972, 0.740, and 0.826; recalls were 0.833, 0.804, and 0.863, respectively; and specificities were 0.989, 0.863, and 0.909, respectively. CONCLUSIONS: Radiomics technology using CMR non-enhanced cine sequences can detect early cardiac changes due to hypertension. It holds promise for future use in screening for latent cardiac damage in early HHD.

An oleanic acid decorated gold nanorod for highly efficient inhibition of hemagglutinin and visible rapid detection of the influenza virus.

Accurate diagnosis and effective antiviral treatments are urgently needed for the prevention and control of flu caused by influenza viruses. In this study, a novel oleanic acid (OA) functionalized gold nanorod OA-AuNP was prepared through a convenient ligand-exchange reaction. As hemagglutinin (HA) on the viral surface binds strongly to the multiple OA molecules on the surface of the nanoparticle, the prepared OA-AuNP was found to exhibit potent antiviral activity against a wide range of influenza A virus strains. Furthermore, the change in color resulting from the specific binding between HA and OA and the resultant aggregation of the OA-AuNP can be visually observed or measured by UV-vis spectra with a detection limit of 2 and 0.18 hemagglutination units (HAU), respectively, which is comparable to the commercially available influenza colloid gold rapid diagnostic kits. These findings demonstrate the potential of the OA-AuNP for the development of novel multivalent antiviral conjugates and the diagnosis of influenza virus.

Comparison of Machine Learning Models Using Diffusion-Weighted Images for Pathological Grade of Intrahepatic Mass-Forming Cholangiocarcinoma.

Is the radiomic approach, utilizing diffusion-weighted imaging (DWI), capable of predicting the various pathological grades of intrahepatic mass-forming cholangiocarcinoma (IMCC)? Furthermore, which model demonstrates superior performance among the diverse algorithms currently available? The objective of our study is to develop DWI radiomic models based on different machine learning algorithms and identify the optimal prediction model. We undertook a retrospective analysis of the DWI data of 77 patients with IMCC confirmed by pathological testing. Fifty-seven patients initially included in the study were randomly assigned to either the training set or the validation set in a ratio of 7:3. We established four different classifier models, namely random forest (RF), support vector machines (SVM), logistic regression (LR), and gradient boosting decision tree (GBDT), by manually contouring the region of interest and extracting prominent radiomic features. An external validation of the model was performed with the DWI data of 20 patients with IMCC who were subsequently included in the study. The area under the receiver operating curve (AUC), accuracy (ACC), precision (PRE), sensitivity (REC), and F1 score were used to evaluate the diagnostic performance of the model. Following the process of feature selection, a total of nine features were retained, with skewness being the most crucial radiomic feature demonstrating the highest diagnostic performance, followed by Gray Level Co-occurrence Matrix lmc1 (glcm-lmc1) and kurtosis, whose diagnostic performances were slightly inferior to skewness. Skewness and kurtosis showed a negative correlation with the pathological grading of IMCC, while glcm-lmc1 exhibited a positive correlation with the IMCC pathological grade. Compared with the other three models, the SVM radiomic model had the best diagnostic performance with an AUC of 0.957, an accuracy of 88.2%, a sensitivity of 85.7%, a precision of 85.7%, and an F1 score of 85.7% in the training set, as well as an AUC of 0.829, an accuracy of 76.5%, a sensitivity of 71.4%, a precision of 71.4%, and an F1 score of 71.4% in the external validation set. The DWI-based radiomic model proved to be efficacious in predicting the pathological grade of IMCC. The model with the SVM classifier algorithm had the best prediction efficiency and robustness. Consequently, this SVM-based model can be further explored as an option for a non-invasive preoperative prediction method in clinical practice.

Purinergic P2X7 receptor involves in anti-retinal photodamage effects of berberine.

Berberine (BBR) is a Chinese herb with antioxidant and anti-inflammatory properties. In a previous study, we found that BBR had a protective effect against light-induced retinal degeneration in BALB/c mice. The purinergic P2X7 receptor (P2X7R) plays a key role in retinal degeneration via inducing oxidative stress, inflammatory changes, and cell death. The aim of this study was to investigate whether BBR can induce protective effects in light damage experiments and whether P2X7R can get involved in these effects. C57BL/6 J mice and P2X7 knockout (KO) mice on the C57BL/6 J background were used. We found that BBR preserved the outer nuclear layer (ONL) thickness and retinal ganglion cells following light stimulation. Furthermore, BBR significantly suppressed photoreceptor apoptosis, pro-apoptotic c-fos expression, pro-inflammatory responses of Mϋller cells, and inflammatory factors (TNF-α, IL-1ß). In addition, protein levels of P2X7R were downregulated in BBR-treated mice. Double immunofluorescence showed that BBR reduced overexpression of P2X7R in retinal ganglion cells and Mϋller cells. Furthermore, BBR combined with the P2X7R agonist BzATP blocked the effects of BBR on retinal morphology and photoreceptor apoptosis. However, in P2X7 KO mice, BBR had an additive effect resulting in thicker ONL and more photoreceptors. The data suggest that the P2X7 receptor is involved in retinal light damage, and BBR inhibits this process by reducing histological impairment, cell death, and inflammatory responses.

Predicting Risk Stratification in Early-Stage Endometrial Carcinoma: Significance of Multiparametric MRI Radiomics Model.

Endometrial carcinoma (EC) risk stratification prior to surgery is crucial for clinical treatment. In this study, we intend to evaluate the predictive value of radiomics models based on magnetic resonance imaging (MRI) for risk stratification and staging of early-stage EC. The study included 155 patients who underwent MRI examinations prior to surgery and were pathologically diagnosed with early-stage EC between January, 2020, and September, 2022. Three-dimensional radiomics features were extracted from segmented tumor images captured by MRI scans (including T2WI, CE-T1WI delayed phase, and ADC), with 1521 features extracted from each of the three modalities. Then, using five-fold cross-validation and a multilayer perceptron algorithm, these features were filtered using Pearson's correlation coefficient to develop a prediction model for risk stratification and staging of EC. The performance of each model was assessed by analyzing ROC curves and calculating the AUC, accuracy, sensitivity, and specificity. In terms of risk stratification, the CE-T1 sequence demonstrated the highest predictive accuracy of 0.858 ± 0.025 and an AUC of 0.878 ± 0.042 among the three sequences. However, combining all three sequences resulted in enhanced predictive accuracy, reaching 0.881 ± 0.040, with a corresponding increase in the AUC to 0.862 ± 0.069. In the context of staging, the utilization of a combination involving T2WI with CE-T1WI led to a notably elevated predictive accuracy of 0.956 ± 0.020, surpassing the accuracy achieved when employing any singular feature. Correspondingly, the AUC was 0.979 ± 0.022. When incorporating all three sequences concurrently, the predictive accuracy reached 0.956 ± 0.000, accompanied by an AUC of 0.986 ± 0.007. It is noteworthy that this level of accuracy surpassed that of the radiologist, which stood at 0.832. The MRI radiomics model has the potential to accurately predict the risk stratification and early staging of EC.

Potent inhibitors targeting cyclin-dependent kinase 9 discovered via virtual high-throughput screening and absolute binding free energy calculations.

Due to the crucial regulatory mechanism of cyclin-dependent kinase 9 (CDK9) in mRNA transcription, the development of kinase inhibitors targeting CDK9 holds promise as a potential treatment strategy for cancer. A structure-based virtual screening approach has been employed for the discovery of potential novel CDK9 inhibitors. First, compounds with kinase inhibitor characteristics were identified from the ZINC15 database via virtual high-throughput screening. Next, the predicted binding modes were optimized by molecular dynamics simulations, followed by precise estimation of binding affinities using absolute binding free energy calculations based on the free energy perturbation scheme. The binding mode of molecule 006 underwent an inward-to-outward flipping, and the new binding mode exhibited binding affinity comparable to the small molecule T6Q in the crystal structure (PDB ID: 4BCF), highlighting the essential role of molecular dynamics simulation in capturing a plausible binding pose bridging docking and absolute binding free energy calculations. Finally, structural modifications based on these findings further enhanced the binding affinity with CDK9. The results revealed that enhancing the molecule's rigidity through ring formation, while maintaining the major interactions, reduced the entropy loss during the binding process and, thus, enhanced binding affinities.

Training machine learning potentials for reactive systems: A Colab tutorial on basic models.

In the last several years, there has been a surge in the development of machine learning potential (MLP) models for describing molecular systems. We are interested in a particular area of this field - the training of system-specific MLPs for reactive systems - with the goal of using these MLPs to accelerate free energy simulations of chemical and enzyme reactions. To help new members in our labs become familiar with the basic techniques, we have put together a self-guided Colab tutorial (https://cc-ats.github.io/mlp_tutorial/), which we expect to be also useful to other young researchers in the community. Our tutorial begins with the introduction of simple feedforward neural network (FNN) and kernel-based (using Gaussian process regression, GPR) models by fitting the two-dimensional Müller-Brown potential. Subsequently, two simple descriptors are presented for extracting features of molecular systems: symmetry functions (including the ANI variant) and embedding neural networks (such as DeepPot-SE). Lastly, these features will be fed into FNN and GPR models to reproduce the energies and forces for the molecular configurations in a Claisen rearrangement reaction.

Radiomics model based on multi-sequence MRI for preoperative prediction of ki-67 expression levels in early endometrial cancer.

To validate a radiomics model based on multi-sequence magnetic resonance imaging (MRI) in predicting the ki-67 expression levels in early-stage endometrial cancer, 131 patients with early endometrial cancer who had undergone pathological examination and preoperative MRI scan were retrospectively enrolled and divided into two groups based on the ki-67 expression levels. The radiomics features were extracted from the T2 weighted imaging (T2WI), dynamic contrast enhanced T1 weighted imaging (DCE-T1WI), and apparent diffusion coefficient (ADC) map and screened using the Pearson correlation coefficients (PCC). A multi-layer perceptual machine and fivefold cross-validation were used to construct the radiomics model. The receiver operating characteristic (ROC) curves analysis, calibration curves, and decision curve analysis (DCA) were used to assess the models. The combined multi-sequence radiomics model of T2WI, DCE-T1WI, and ADC map showed better discriminatory powers than those using only one sequence. The combined radiomics models with multi-sequence fusions achieved the highest area under the ROC curve (AUC). The AUC value of the validation set was 0.852, with an accuracy of 0.827, sensitivity of 0.844, specificity of 0.773, and precision of 0.799. In conclusion, the combined multi-sequence MRI based radiomics model enables preoperative noninvasive prediction of the ki-67 expression levels in early endometrial cancer. This provides an objective imaging basis for clinical diagnosis and treatment.

Using machine learning-based radiomics to differentiate between glioma and solitary brain metastasis from lung cancer and its subtypes.

OBJECTIVE: To establish a machine learning-based radiomics model to differentiate between glioma and solitary brain metastasis from lung cancer and its subtypes, thereby achieving accurate preoperative classification. MATERIALS AND METHODS: A retrospective analysis was conducted on MRI T1WI-enhanced images of 105 patients with glioma and 172 patients with solitary brain metastasis from lung cancer, which were confirmed pathologically. The patients were divided into the training group and validation group in an 8:2 ratio for image segmentation, extraction, and filtering; multiple layer perceptron (MLP), support vector machine (SVM), random forest (RF), and logistic regression (LR) were used for modeling; fivefold cross-validation was used to train the model; the validation group was used to evaluate and assess the predictive performance of the model, ROC curve was used to calculate the accuracy, sensitivity, and specificity of the model, and the area under curve (AUC) was used to assess the predictive performance of the model. RESULTS: The accuracy and AUC of the MLP differentiation model for high-grade glioma and solitary brain metastasis in the validation group was 0.992, 1.000, respectively, while the sensitivity and specificity were 1.000, 0.968, respectively. The accuracy and AUC for the MLP and SVM differentiation model for high-grade glioma and small cell lung cancer brain metastasis in the validation group was 0.966, 1.000, respectively, while the sensitivity and specificity were 1.000, 0.929, respectively. The accuracy and AUC for the MLP differentiation model for high-grade glioma and non-small cell lung cancer brain metastasis in the validation group was 0.982, 0.999, respectively, while the sensitivity and specificity were 0.958, 1.000, respectively. CONCLUSION: The application of machine learning-based radiomics has a certain clinical value in differentiating glioma from solitary brain metastasis from lung cancer and its subtypes. In the HGG/SBM and HGG/NSCLC SBM validation groups, the MLP model had the best diagnostic performance, while in the HGG/SCLC SBM validation group, the MLP and SVM models had the best diagnostic performance.

[Comparison of coagulation function between adrenocorticotropic hormone independent Cushing syndrome and nonfunctional adrenal adenoma and its influence factors].

OBJECTIVE: To investigate the coagulation function indicators and identify influence factors of hypercoagulability in patients with adrenocorticotropic hormone (ACTH) independent Cushing syndrome (CS). METHODS: In our retrospective study, the electronic medical records system of Peking University First Hospital was searched for the patients diagnosed with ACTH independent CS on discharge from January 2014 to June 2019. Nonfunctional adrenal adenoma patients were chosen as control group and matched 1 ∶1 by body mass index (BMI), gender, and discharge date. Clinical features and coagulation function indicators were compared between the two groups. RESULTS: In the study, 171 patients were included in each group. Compared with control group, activated partial thromboplastin time (APTT), and prothrombin time (PT) in ACTH independent CS group were significantly lower [(29.22±3.39) s vs. (31.86±3.63) s, P < 0.001; (29.22±3.39) s vs. (31.86±3.63) s, P < 0.001], and both D-dimer and fibrin degradation products (FDP) levels were significantly higher (P < 0.05). Percentage of APTT levels under the lower limit of reference range in the CS patients was significantly higher than that in nonfunctional group (21.6% vs. 3.5%, P < 0.001). Percentage of D-dimer levels over the upper limit of reference range in the CS patients was significantly higher than that in nonfunctional group (13.5% vs. 6.6%, P=0.041). There were three patients with deep venous thrombosis and one patient with pulmonary embolism in CS group, however none was in control group. The area under curve (AUC) of serum cortisol rhythm (8:00, 16:00 and 24:00) levels was negatively associated with the levels of PT (r=-0.315, P < 0.001) and APTT (r=-0.410, P < 0.001), and positively associated with FDP (r=0.303, P < 0.001) and D-dimer levels (r=0.258, P < 0.001). There were no differences in coagulation function indicators among different histopathologic subgroups (adrenocortical adenoma, adrenocortical hyperplasia, oncocytic adenoma, adrenocortical carcinoma). With Logistic regression analysis, the AUC of cortisol and glycosylated hemoglobin A1c (HbA1c) levels were independent risk factors for hypercoagulability in the ACTH independent CS patients (P < 0.05). CONCLUSION: ACTH independent CS patients were more likely in hypercoagulable state compared with nonfunctional adrenal adenoma, especially in ACTH independent CS patients with higher levels of cortisol AUC and HbA1c. These patients should be paid attention to for the hypercoagulability and thrombosis risk.

Value of Multimodal Diffusion-weighted Imaging in Preoperative Evaluation of Ki-67 Expression in Endometrial Carcinoma.

PURPOSE: To investigate the value of multimodal diffusion weighted imaging (DWI) in preoperative evaluation of Ki-67 expression of endometrial carcinoma (EC). MATERIALS AND METHODS: Patients who had undergone pelvic DWI, intravoxel incoherent motion (IVIM), and diffusion kurtosis imaging (DKI) sequence MRI scan before surgery were retrospectively enrolled. Single index model, double index model, and DKI were used for post-processing of the DWI data, and the apparent diffusion coefficient (ADC), real diffusion coefficient (D), pseudo diffusion coefficient (D*), perfusion fraction (f), non-Gaussian mean diffusion kurtosis (MK), mean diffusion coefficient (MD) and anisotropy fraction (FA) were calculated and compared between the Ki-67 high (≥50%) and low (<50%) expression groups. RESULTS: Forty-two patients with a median age of 56 (range 37 - 75) years were enrolled, including 15 patients with a high Ki-67 (≥50%) expression and 27 with a low Ki-67 (<50%) expression. The MK (0.91 ± 0.12 vs. 0.76 ± 0.12) was significantly (P<0.05) higher while MD (0.99 ± 0.17 vs. 1.16 ± 0.22), D (0.55 ± 0.06 vs. 0.62 ± 0.08), and f (0.21 vs. 0.28) were significantly (P<0.05) lower in the high than in the low expression group. The combined model of MK, MD, D, and f-values had the largest area under the curve (AUC) value of 0.869 (95% CI: 0.764-0.974), sensitivity 0.733 and specificity 0.852, followed by the MK value with an AUC value 0.827 (95% CI: 0.700-0.954), sensitivity 0.733 and specificity 0.815. CONCLUSIONS: IVIM and DKI have certain diagnostic values for preoperative evaluation of the EC Ki-67 expression, and the combined model has the highest diagnostic efficiency.

[Preliminary observation on the clinical application of rehabilitation support for lumbar disc herniation based on 3D printing technology].

OBJECTIVE: To analyze the important effect of 3D printing personalized lumbar support on lumbar pain and lumbar function in patients with lumbar disc herniation. METHODS: From October 2018 to May 2021, 60 patients initially diagnosed with lumbar disc herniation were selected and divided into an observation group and a control group, with 30 patients in each group. Among them, there were 18 males and 12 females in the observation group;the age ranged from 24 to 56 years old, with an average of (45.23±6.07) years old. The course of disease ranged from 1 to 24 months, with an average of(6.25±0.82) months, and rehabilitation treatment was carried out by wearing 3D printed personalized lumbar support. There were 19 males and 11 females in the control group;the age ranged from 25 to 57 years old, with an average of (42.78±7.58) years old. The course of disease ranged from 1 to 24 months, with an average of (6.72±1.36) months, and rehabilitation treatment is carried out by wearing traditional lumbar protective equipment. The Japanese Orthopaedic Association (JOA) scores, lumbar Oswestry dysfunction index (ODI) and visual analogue scale (VAS) were evaluated and compared between the two groups before and 1 course after treatment (3 weeks). RESULTS: There was no statistically significant difference in JOA, ODI, and VAS between two groups before treatment (P>0.05). After one course of treatment (3 weeks), JOA scores of both groups was increased compared to before treatment (P<0.05), while ODI and VAS decreased compared to before treatment (P<0.05). After treatment, JOA score of observation group was higher than that of control group (P<0.05), while ODI and VAS scores were lower than those of control group. No adverse events occurred in both groups. CONCLUSION: The application of 3D printing personalized lumbar support can effectively alleviate the pain of patients with lumbar disc herniation and improve their lumbar function of patients.

Tracking the Early Stage of Triplet-Induced Photodamage in a DNA Dimer and Oligomer Containing 5-Methylcytosine.

Methylation at the C5 position of cytosine, a naturally occurring epigenetic modification on DNA, shows a high correlation with mutational hotspots in disease such as skin cancer. Due to its essential biological relevance, numerous studies were devoted to confirming that the methylated sites favor the formation of the cyclobutane pyrimidine dimer (CPD), a well-known UV-induced lesion. However, photophysical and photochemical properties of dinucleotides and polynucleotides containing 5-methylcytosine (5mC) remain elusive. Herein, a charge transfer (CT) triplet state, generated via intersystem crossing (ISC) from a CT singlet state that enhanced after methylation on cytosine, is directly observed by using femtosecond transient absorption (TA) and time-resolved mid-infrared (TRIR) spectroscopy together with quantum chemical calculations for the first time in the T5mC dimer. Such an ISC process is quenched due to limitations of the ground-state geometries in 5mC-containing single-strand oligomer d(T5mC)9. This mechanistic information is important for understanding the early stage of triplet state-induced CPD formation in 5mC containing DNA.

Deep learning for predicting the human epidermal growth factor receptor 2 status of breast cancer liver metastases based on contrast-enhanced computed tomography: a development and validation study.

Background: This study investigated the value of a deep learning (DL) model based on computed tomography (CT) enhancement for predicting human epidermal growth factor receptor 2 (HER2) expression in patients with liver metastasis from breast cancer. Methods: Data were collected for 151 female patients with liver metastasis from breast cancer who underwent abdominal enhanced CT examination in the Department of Radiology at the Affiliated Hospital of Hebei University between January 2017 and March 2022. Liver metastases were confirmed in all patients by pathology. The HER2 status of the liver metastases was assessed and enhanced CT examinations were performed before treatment. Of the 151 patients, 93 were HER2 negative and 58 were HER2 positive. Liver metastases were manually labeled with rectangular frames, layer by layer, and the labeled data were processed. Five basic networks (ResNet34, ResNet50, ResNet101, ResNeXt50, and Swim Transformer) were used for training and optimization, and the model's performance was tested. Receiver operating characteristic (ROC) curves were used to analyze the area under the curve (AUC), as well as the accuracy, sensitivity, and specificity of the networks in predicting HER2 expression in breast cancer liver metastases. Results: Overall, ResNet34 demonstrated the best prediction efficiency. The accuracy of the validation and test set models in predicting HER2 expression in liver metastases was 87.4% and 80.5%, respectively. The AUC, sensitivity, and specificity of the test set model in predicting HER2 expression in liver metastases were 0.778, 77.0%, and 84.0%, respectively. Conclusions: Our DL model based on CT enhancement has good stability and diagnostic efficacy, and is a potential non-invasive method for identifying HER2 expression in liver metastases from breast cancer.

Perspective: Reference-Potential Methods for the Study of Thermodynamic Properties in Chemical Processes: Theory, Applications, and Pitfalls.

In silico investigations of enzymatic reactions and chemical reactions in condensed phases often suffer from formidable computational costs due to a large number of degrees of freedom and enormous important volume in phase space. Usually, accuracy must be compromised to trade for efficiency by lowering the reliability of the Hamiltonians employed or reducing the sampling time. Reference-potential methods (RPMs) offer an alternative approach to reaching high accuracy of simulation without much loss of efficiency. In this Perspective, we summarize the idea of RPMs and showcase some recent applications. Most importantly, the pitfalls of these methods are also discussed, and remedies to these pitfalls are presented.