Stacking-dependent interlayer magnetic interactions in CrSe2.

Recently, CrSe2, a new ferromagnetic van der Waals two-dimensional material, was discovered to be highly stable under ambient conditions, making it an attractive candidate for fundamental research and potential device applications. Here, we study the interlayer interactions of bilayer CrSe2using first-principles calculations. We demonstrate that the interlayer interaction depends on the stacking structure. The AA and AB stackings exhibit antiferromagnetic (AFM) interlayer interactions, while the AC stacking exhibits ferromagnetic (FM) interlayer interaction. Furthermore, the interlayer interaction can be further tuned by tensile strain and charge doping. Specifically, under large tensile strain, most stacking structures exhibit FM interlayer interactions. Conversely, under heavy electron doping, all stacking structures exhibit AFM interlayer interactions. These findings are useful for designing spintronic devices based on CrSe2.

Metal-phenolic network as precursor complex coating for forward osmosis membrane with enhanced antifouling property.

In this study, a multi-functional layer was developed based on the commercially available cellulose triacetate (CTA) forward osmosis (FO) membrane to improve its antifouling property. Tannic acid/ferric ion (TA/Fe3+) complexes were firstly coated as a precursor layer on the membrane surface via self-assembly. Afterwards, the tannic acid/diethylenetriamine (TA/DETA) hydrophilic functional layer was further coated, following Ag/polyvinylpyrrolidone (PVP) anti-bacterial layer was formed in situ through the reducibility of TA to obtain TA/Fe3+-TA/DETA-Ag/PVP-modified membrane. The optimized precursor layer was acquired by adjusting the buffer solution pH to 8, TA/Fe3+ ratio to 4 and the number of self-assembled layers to 5. The permeability testing results illustrated that the functional layer had an insignificant effect on the membrane transport parameters. The TA/Fe3+-TA/DETA-Ag/PVP-modified membrane simultaneously exhibited excellent physical and chemical stability. The coated membrane also demonstrated enhanced anti-bacterial properties, achieving 98.63 and 97.30% inhibition against Staphylococcus aureus and Escherichia coli, respectively. Furthermore, the dynamic fouling experiment showed a 12% higher water flux decrease for the TA/Fe3+-TA/DETA-Ag/PVP CTA membrane compared to the nascent CTA membrane, which proved its excellent antifouling performance. This work provides a feasible strategy to heighten the antifouling property of the CTA FO membrane.

MRI-Based Radiomics Nomogram for Preoperative Differentiation Between Ocular Adnexal Lymphoma and Idiopathic Orbital Inflammation.

BACKGROUND: Ocular adnexal lymphoma (OAL) and idiopathic orbital inflammation (IOI) are malignant and benign lesions for which radiotherapy and corticosteroids are indicated, but similar clinical manifestations make their differentiation difficult. PURPOSE: To develop and validate an MRI-based radiomics nomogram for individual diagnosis of OAL vs. IOI. STUDY TYPE: Retrospective. POPULATION: A total of 103 patients (46.6% female) with mean age of 56.4 ± 16.3 years having OAL (n = 58) or IOI (n = 45) were divided into an independent training (n = 82) and a testing dataset (n = 21). FIELD STRENGTH/SEQUENCE: A 3-T, precontrast T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), and postcontrast T1WI (T1 + C). ASSESSMENT: Radiomics features were extracted and selected from segmented tumors and peritumoral regions in MRI before-and-after filtering. These features, alone or combined with clinical characteristics, were used to construct a radiomics or joint signature to differentiate OAL from IOI, respectively. A joint nomogram was built to show the impact of the radiomics signature and clinical characteristics on individual risk of developing OAL. STATISTICAL TESTS: Area under the curve (AUC) and accuracy (ACC) were used for performance evaluation. Mann-Whitney U and Chi-square tests were used to analyze continuous and categorical variables. Decision curve analysis, kappa statistics, DeLong and Hosmer-Lemeshow tests were also conducted. P < 0.05 was considered statistically significant. RESULTS: The joint signature achieved an AUC of 0.833 (95% confidence interval [CI]: 0.806-0.870), slightly better than the radiomics signature with an AUC of 0.806 (95% CI: 0.767-0.838) (P = 0.778). The joint and radiomics signatures were comparable to experienced radiologists referencing to clinical characteristics (ACC = 0.810 vs. 0.796-0.806, P > 0.05) or not (AUC = 0.806 vs. 0.753-0.791, P > 0.05), respectively. The joint nomogram gained more net benefits than the radiomics nomogram, despite both showing good calibration and discriminatory efficiency (P > 0.05). DATA CONCLUSION: The developed radiomics-based analysis might help to improve the diagnostic performance and reveal the association between radiomics features and individual risk of developing OAL. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: 3.

In situ surface modification of forward osmosis membrane by polydopamine/polyethyleneimine-silver nanoparticle for anti-fouling improvement in municipal wastewater treatment.

In this work, we demonstrate the surface functionalization of cellulose triacetate membrane with co-deposition of polydopamine (PDA)/polyethyleneimine (PEI) and silver nanoparticles (AgNPs) for antifouling property in municipal wastewater treatment. PDA/PEI was first coated on the membrane surface by single-step co-deposition, while AgNPs were formed in situ through catechol groups of PDA immobilizing silver ions and subsequently reducing. The successful surface modification was verified by different membrane characterization techniques. The modified PDA/PEI-nAg CTA membrane exhibits enhanced hydrophilicity and improved antiadhesion and antimicrobial activity. Furthermore, the functional layer had an indistinctive effect on the membrane transport parameters. In addition, dynamic forward osmosis (FO) fouling experiment with raw municipal wastewater as feed solution indicated that the PDA/PEI-nAg CTA membrane exhibited notably lower water flux decrease compared to the nascent CTA membrane. The results of confocal laser scanning microscopy (CLSM) showed that PDA/PEI-nAg CTA membranes effectively reduced the adsorption of organic foulants (proteins and polysaccharides) and inhibited the formation and development of the fouling layer. The membrane surface modification of the CTA membrane with PDA/PEI and AgNPs efficiently mitigated membrane fouling in municipal wastewater treatment.

Prognostic Value of Pretreatment Geriatric Nutrition Risk Index in Lung Cancer Patients: A Meta-Analysis.

PURPOSE: To explore the prognostic value of pretreatment Geriatric Nutrition Risk Index (GNRI) in lung cancer patients. METHODS: The PubMed, EMBASE and Web of Science electronic databases were searched up to October 5, 2021 for relevant studies. The primary and second outcomes were the overall survival (OS) and progression-free survival (PFS) and the hazard ratios (HRs) with 95% confidence intervals (CIs) were combined to assess the association between GNRI and prognosis of lung cancer patients. RESULTS: A total of eight retrospective studies involving 2012 patients were included and all of them were from Asian countries. The pooled results demonstrated that lower pretreatment GNRI was significantly associated with poor OS (HR = 2.01, 95% CI: 1.68-2.42, P < 0.001) and PFS (HR = 1.50, 95% CI: 1.24-1.81, P < 0.001). Subgroup analysis based on the treatment (surgery vs non-surgery) and tumor type (non-small cell lung cancer vs small cell lung cancer) showed similar results. CONCLUSION: Lower pretreatment GNRI was an independent prognostic risk factor for lung cancer patients and might contribute to the evaluation of prognosis and formulation of therapy strategies in lung cancer.

A deep learning model combining multimodal radiomics, clinical and imaging features for differentiating ocular adnexal lymphoma from idiopathic orbital inflammation.

OBJECTIVES: To evaluate the value of deep learning (DL) combining multimodal radiomics and clinical and imaging features for differentiating ocular adnexal lymphoma (OAL) from idiopathic orbital inflammation (IOI). METHODS: Eighty-nine patients with histopathologically confirmed OAL (n = 39) and IOI (n = 50) were divided into training and validation groups. Convolutional neural networks and multimodal fusion layers were used to extract multimodal radiomics features from the T1-weighted image (T1WI), T2-weighted image, and contrast-enhanced T1WI. These multimodal radiomics features were then combined with clinical and imaging features and used together to differentiate between OAL and IOI. The area under the curve (AUC) was used to evaluate DL models with different features under five-fold cross-validation. The Student t-test, chi-squared, or Fisher exact test was used for comparison of different groups. RESULTS: In the validation group, the diagnostic AUC of the DL model using combined features was 0.953 (95% CI, 0.895-1.000), higher than that of the DL model using multimodal radiomics features (0.843, 95% CI, 0.786-0.898, p < 0.01) or clinical and imaging features only (0.882, 95% CI, 0.782-0.982, p = 0.13). The DL model built on multimodal radiomics features outperformed those built on most bimodalities and unimodalities (p < 0.05). In addition, the DL-based analysis with the orbital cone area (covering both the orbital mass and surrounding tissues) was superior to that with the region of interest (ROI) covering only the mass area, although the difference was not significant (p = 0.33). CONCLUSIONS: DL-based analysis that combines multimodal radiomics features with clinical and imaging features may help to differentiate between OAL and IOI. KEY POINTS: • It is difficult to differentiate OAL from IOI due to the overlap in clinical and imaging manifestations. • Radiomics has shown potential for noninvasive diagnosis of different orbital lymphoproliferative disorders. • DL-based analysis combining radiomics and imaging and clinical features may help the differentiation between OAL and IOI.

Tracing analgesic constituents from crude and vinegar-processed resin of Boswellia carterii by integrating ultra-performance liquid chromatography tandem mass spectrometry-based determination, analgesic evaluation in mice, and gray relationship analysis.

The analgesic effect of the resin of Boswellia carterii (BC) is well known; however, the constituents that contribute to the analgesic effect remain elusive. The current study integrates ultrasonic-assisted extraction, quantitative determination, analgesic evaluation in rats, and gray relationship analysis for tracing analgesic constituents from the resin of BC. First, a robust and precise ultra-performance liquid chromatography tandem mass spectrometry approach with multiple reaction monitoring mode was developed for the simultaneous quantification of seven major constituents in crude and vinegar-processed resin of BC. Glycyrrhetinic acid was chosen as the internal standard. The approach showed good linearity. The intra- and inter-day precisions of each constituent were within 3.0%. The recoveries of each constituent were in the range of 96.4-102.7%. The approach was then applied to determine the seven constituents in 10 batches of crude and vinegar-processed resin of BC. Second, the analgesic effects of crude and vinegar-processed resin of BC were assessed in mice. Third, chemometrics methods, gray relationship analysis, and partial least squares regression were employed for determining the relationship between the contents of seven constituents and their analgesic effects. 11-Keto-ß-boswellic acid, 3-acetyl-ß-boswellic acid, 3-acetyl-α-boswellic acid, 3-acetyl-11-keto-ß-boswellic acid, and ß-sitosterol were identified as the key analgesic constituents of BC.

Signal Source Localization with Long-Term Observations in Distributed Angle-Only Sensors.

Angle-only sensors cannot provide range information of targets and in order to determine accurate position of a signal source, one can connect distributed passive sensors with communication links and implement a fusion algorithm to estimate target position. To measure moving targets with sensors on moving platforms, most of existing algorithms resort to the filtering method. In this paper, we present two fusion algorithms to estimate both the position and velocity of moving target with distributed angle-only sensors in motion. The first algorithm is termed as the gross least square (LS) algorithm, which takes all observations from distributed sensors together to form an estimate of the position and velocity and thus needs a huge communication cost and a huge computation cost. The second algorithm is termed as the linear LS algorithm, which approximates locations of sensors, locations of targets, and angle-only measures for each sensor by linear models and thus does not need each local sensors to transmit raw data of angle-only observations, resulting in a lower communication cost between sensors and then a lower computation cost at the fusion center. Based on the second algorithm, a truncated LS algorithm, which estimates the target velocity through an average operation, is also presented. Numerical results indicate that the gross LS algorithm, without linear approximation operation, often benefits from more observations, whereas the linear LS algorithm and the truncated LS algorithm, both bear lower communication and computation costs, may endure performance loss if the observations are collected in a long period such that the linear approximation model becomes mismatch.

Bag-of-features-based radiomics for differentiation of ocular adnexal lymphoma and idiopathic orbital inflammation from contrast-enhanced MRI.

OBJECTIVES: To evaluate the effectiveness of bag-of-features (BOF)-based radiomics for differentiating ocular adnexal lymphoma (OAL) and idiopathic orbital inflammation (IOI) from contrast-enhanced MRI (CE-MRI). METHODS: Fifty-six patients with pathologically confirmed IOI (28 patients) and OAL (28 patients) were randomly divided into training (n = 42) and testing (n = 14) groups. One hundred sixty texture features extracted from the CE-MR image were encoded into the BOF representation with fewer features. The support vector machine (SVM) with a linear kernel was used as the classifier. Data augmented was performed by cropping orbital lesions in different directions to alleviate the over-fitting problem. Student's t test and the Holm-Bonferroni method were employed to compare the performance of different analysis methods. The chi-square test was used to compare the analysis with MRI and human radiological diagnosis. RESULTS: In the independent testing group, the differentiation by the BOF features with augmentation achieved an area under the curve (AUC) of 0.803 (95% CI: 0.725-0.880), which was significantly higher than that of the BOF features without augmentation and that of the texture features (p < 0.05). In addition, the same radiomic analysis with pre-contrast MRI obtained an AUC of 0.618 (95% CI: 0.560-0.677), which was significantly lower than that with CE-MRI. The diagnostic performance of the analysis with CE-MRI was significantly better than the radiology resident (p < 0.05) but had no significant difference with the experienced radiologist, even though there was less consistency between the radiomic analysis and the human visual diagnosis. CONCLUSIONS: The BOF-based radiomics may be helpful for the differentiation between OAL and IOI. KEY POINTS: • It is challenging to differentiate OAL from IOI due to the similar clinical and image features. • Radiomics has great potential for the noninvasive diagnosis of orbital diseases. • The BOF representation from patch to image may help the differentiation of OAL and IOI.

Isolation, synthesis, and cytotoxicity evaluation of two impurities in nomegestrol acetate.

Nomegestrol acetate (NOMAc) is a synthetic progesterone analog and classified as a fourth-generation progestin. It has been approved in many countries for oral contraception, hormonal replacement therapy (HRT), and treatment of various gynecological disorders. There are several synthetic routes reported for the synthesis of NOMAc and they all share the very similar last three to five steps toward the conversion of 6-methylene to 6-methyl-6,7-unsaturated structure. Therefore the final product from different processing routes may have similar impurity profiles. In the analysis of NOMAc, we identified two impurities, impurity A (listed in EP 8.0) and impurity B (not specified in EP 8.0). Both impurities were further confirmed by synthesis. In addition, both impurities and NOMAc were evaluated for their in vitro cytotoxicities against L02 liver cells, mesenchymal stem cells, MCF-7 breast cancer cells, and C33A cervical cancer cells. These three analogs are not cytotoxic to the four cell lines at low concentrations (<20 µM). NOMAc and impurity A showed cytotoxicity to L02, MCF-7, and C33A cells at high concentrations, while impurity B did not show significant cytotoxicity to any of the cell lines tested.

Design, Synthesis and Antibacterial Evaluation of Some New 2-Phenyl-quinoline-4-carboxylic Acid Derivatives.

A series of new 2-phenyl-quinoline-4-carboxylic acid derivatives was synthesized starting from aniline, 2-nitrobenzaldehyde, pyruvic acid followed by Doebner reaction, amidation, reduction, acylation and amination. All of the newly-synthesized compounds were characterized by ¹H-NMR, (13)C-NMR and HRMS. The antibacterial activities of these compounds against Gram-negative (Escherichia coli, Pseudomonas aeruginosa) and Gram-positive bacteria (Staphylococcus aureus, Bacillus subtilis), as well as one strain of methicillin-resistant Staphylococcus aureus (MRSA) bacteria were evaluated by the agar diffusion method (zone of inhibition) and a broth dilution method (minimum inhibitory concentration (MIC)), and their structure-activity relationships were obtained and discussed. The results revealed that some compounds displayed good antibacterial activity against Staphylococcus aureus, and Compounds 5a4 and 5a7 showed the best inhibition with an MIC value of 64 µg/mL against Staphylococcus aureus and with an MIC value of 128 µg/mL against Escherichia coli, respectively. The results of the MTT assay illustrated the low cytotoxicity of Compound 5a4.

Clinical role of pretreatment albumin-to-alkaline phosphatase ratio in lung cancer: a meta-analysis.

The association between pretreatment albumin-to-alkaline phosphatase ratio (AAPR) and clinicopathological parameters and prognosis in lung cancer is unclear. The study aimed to identify the clinical role of pretreatment AAPR among lung cancer patients. Several databases were searched for relevant studies. The primary outcome and secondary outcome were long-term survival including the overall survival (OS) and progression-free survival (PFS) and clinicopathological characteristics, respectively. The hazard ratios (HRs) and relative risks (RRs) with 95% confidence intervals (CIs) were combined. A total of 11 publications involving 10,589 participants were included in this meta-analysis. The pooled results manifested that a lower pretreatment AAPR predicted poorer OS (HR = 0.65, 95% CI 0.59-0.71, P < 0.001) and PFS (HR = 0.68, 95% CI 0.59-0.78, P < 0.001). Furthermore, subgroup analysis for the OS and PFS based on the pathological type and treatment showed similar results and pretreatment AAPR was significantly associated with worse prognosis. Besides, pretreatment AAPR was significantly associated with male (RR = 1.08, 95% CI 1.03-1.13, P < 0.001), poor differentiation (RR = 1.33, 95% CI 1.03-1.73, P = 0.029), advanced T stage (RR = 1.25, 95% CI 1.03-1.52, P = 0.026), N stage (RR = 1.34, 95% CI 1.15-1.55, P < 0.001) and TNM stage (RR = 1.14, 95% CI 1.06-1.223, P < 0.001). Therefore, pretreatment AAPR is significantly related to prognosis and tumor stage in lung cancer and patients with a lower pretreatment AAPR are more likely to experience poor survival and advanced tumor stage.

Prognostic role of lymph node micrometastasis in pN0 esophageal cancer: A meta-analysis.

BACKGROUND: To further identify the association between the lymph node micrometastasis (LNM) and long-term survival among pN0 esophageal cancer patients receiving the surgery. METHODS: Several databases were searched for relevant studies up to June 22, 2023. The primary and secondary outcomes were separately overall survival (OS) and relapse-free survival (RFS) and hazard ratios (HRs) with 95% confidence intervals (CIs) were combined. Subgroup analysis based on pathological type and source of HR was further performed. All statistical analyses were conducted by STATA 15.0 software. RESULTS: A total of 20 studies involving 1830 pN0 patients were included in this meta-analysis. The pooled results demonstrated that the presence of LNM indicated significantly worse OS (HR = 2.19, 95% CI = 1.77-2.70, P < .001) and RFS (HR = 2.15, 95% CI = 1.65-2.80, P < .001). Besides, subgroup analysis for the OS and RFS stratified by the pathological type (squamous cell carcinoma vs mixed esophageal cancer) and source of HR (reported vs estimated) further identified the significant relationship of LNM with prognosis in surgical esophageal cancer. CONCLUSION: The presence of LNM indicated significantly poorer long-term survival among operated pN0 esophageal cancer patients. LNM could serve as a novel and reliable prognostic indicator in surgical esophageal cancer.

Export priority technique for Uni-portal thoracoscopic left upper lobectomy.

BACKGROUND: Further explore the safety and feasibility of Uni-portal video assisted thoracoscopic (UVATS) left upper lobectomy by optimizing the treatment of incisions and blood vessels. METHODS: We conducted a retrospective analysis of data from 32 patients who underwent UVATS left upper lobectomy and systematic mediastinal lymph node dissection utilizing the Export priority technique between January 2021 and December 2022. We documented perioperative indicators, including surgical time, intraoperative blood loss, the number of lymph nodes dissected, and postoperative pathological staging. RESULTS: All surgeries were conducted utilizing the Export priority technique in UVATS. The mean surgical duration was (98.93 ± 14.98) minutes, with an average intraoperative blood loss of (79.53 ± 37.96) ml. The mean count of dissected lymph nodes was (13.96 ± 2.69). The length of hospital Stay averaged (5.62 ± 1.81) days. On the first postoperative day, the thoracic drainage volume was (101.87 ± 49.46) ml. The mean duration of postoperative thoracic tube insertion was (3.1 ± 1.84) days. No occurrences of postoperative hoarseness, pulmonary infection, or complications such as bronchopleural fistula were observed. CONCLUSION: The application of the Export priority technique improves the safety and feasibility of UVATS left upper lobectomy.

Prognostic value of lung immune prognostic index in non-small cell lung cancer patients receiving immune checkpoint inhibitors: a meta-analysis.

Background and Purpose: Until now, it has been difficult to accurately predict the efficacy of immunotherapy in patients with non-small cell lung cancer (NSCLC). A novel indicator, the lung immune prognostic index (LIPI), has shown relatively high prognostic value in patients with solid cancer. Therefore, this study aimed to further identify the association between LIPI and the survival of patients with NSCLC who receive immune checkpoint inhibitors (ICIs). Methods: Several electronic databases were searched for available publications up to April 23, 2023. Immunotherapy outcomes included overall survival (OS), progression-free survival (PFS), and hazard ratios (HRs) with 95% confidence intervals (CIs). Subgroup analysis based on the study design and comparison of the LIPI was conducted. Results: In this meta-analysis, 21 studies with 9,010 patients were included in this meta-analysis. The pooled results demonstrated that elevated LIPI was significantly associated with poor OS (HR = 2.50, 95% CI:2.09-2.99, p < 0.001) and PFS (HR = 1.77, 95% CI:1.64-1.91, p < 0.001). Subgroup analyses stratified by study design (retrospective vs. prospective) and comparison of LIPI (1 vs. 0, 2 vs. 0, 1-2 vs. 0, 2 vs. 1 vs. 0, 2 vs. 0-1 and 2 vs. 1) showed similar results. Conclusion: LIPI could serve as a novel and reliable prognostic factor in NSCLC treated with ICIs, and elevated LIPI predicts worse prognosis.

CORONet: A Cross-Sequence Joint Representation and Hypergraph Convolutional Network for Classifying Molecular Subtypes of Breast Cancer Using Incomplete DCE-MRI.

Breast cancer, the predominant malignancy among women, is characterized by significant heterogeneity, leading to the emergence of distinct molecular subtypes. Accurate differentiation of these molecular subtypes holds paramount clinical significance, owing to substantial variations in prognosis, therapeutic strategies, and survival outcomes. In this study, we propose a cross-sequence joint representation and hypergraph convolution network (CORONet) for classifying molecular subtypes of breast cancer using incomplete DCE-MRI. Specifically, we first build a cross-sequence joint representation (COR) module to integrate image imputation and feature representation into a unified framework, encouraging effective feature extraction for subsequent classification. Then, we fuse multiple COR features and applied feature selection to reduce the redundant information between sequences. Finally, we deploy hypergraph structures to model high-order correlation among different subjects and extracted high-level semantic features by hypergraph convolutions for molecular subtyping. Extensive experiments on incomplete DCE-MRIs of 395 patients from the TCIA repository showed a significant improvement of our CORONet over state of the arts, with the area under the curve (AUC) of 0.891 and 0.903 for luminal and triple-negative (TN) subtype prediction, respectively. Similar advantages of CORONet were also confirmed in partial complete DCE-MRIs of 144 patients, achieving an AUC of 0.858 and 0.832 for predicting luminal and TN subtypes of breast cancer, respectively. Nevertheless, both of these values were lower compared to the scenario where DCE-MRIs from all 395 patients were utilized. Our study contributes to the precise molecular subtyping using incomplete multi-sequence DCE-MRI, thereby offering promising prospects for future risk stratification of breast cancer patients.

A nomogram combining CT-based radiomic features with clinical features for the differentiation of benign and malignant cystic pulmonary nodules.

BACKGROUND: Currently, the differentiation between benign and malignant cystic pulmonary nodules poses a significant challenge for clinicians. The objective of this retrospective study was to construct a predictive model for determining the likelihood of malignancy in patients with cystic pulmonary nodules. METHODS: The current study involved 129 patients diagnosed with cystic pulmonary nodules between January 2017 and June 2023 at the Neijiang First People's Hospital. The study gathered the clinical data, preoperative imaging features of chest CT, and postoperative histopathological results for both cohorts. Univariate and multivariate logistic regression analyses were employed to identify independent risk factors, from which a prediction model and nomogram were developed. In addition, The model's performance was assessed through receiver operating characteristic (ROC) curve analysis, calibration curve analysis, and decision curve analysis (DCA). RESULTS: A cohort of 129 patients presenting with cystic pulmonary nodules, consisting of 92 malignant and 37 benign lesions, was examined. Logistic data analysis identified a cystic airspace with a mural nodule, spiculation, mural morphology, and the number of cystic cavities as significant independent predictors for discriminating between benign and malignant cystic lung nodules. The nomogram prediction model demonstrated a high level of predictive accuracy, as evidenced by an area under the ROC curve (AUC) of 0.874 (95% CI: 0.804-0.944). Furthermore, the calibration curve of the model displayed satisfactory calibration. DCA proved that the prediction model was useful for clinical application. CONCLUSION: In summary, the risk prediction model for benign and malignant cystic pulmonary nodules has the potential to assist clinicians in the diagnosis of such nodules and enhance clinical decision-making processes.

A Deep Learning Model for Predicting Molecular Subtype of Breast Cancer by Fusing Multiple Sequences of DCE-MRI From Two Institutes.

RATIONALE AND OBJECTIVES: To evaluate the performance of deep learning (DL) in predicting different breast cancer molecular subtypes using DCE-MRI from two institutes. MATERIALS AND METHODS: This retrospective study included 366 breast cancer patients from two institutes, divided into training (n = 292), validation (n = 49) and testing (n = 25) sets. We first transformed the public DCE-MRI appearance to ours to alleviate small-data-size and class-imbalance issues. Second, we developed a multi-branch convolutional-neural-network (MBCNN) to perform molecular subtype prediction. Third, we assessed the MBCNN with different regions of interest (ROIs) and fusion strategies, and compared it to previous DL models. Area under the curve (AUC) and accuracy (ACC) were used to assess different models. Delong-test was used for the comparison of different groups. RESULTS: MBCNN achieved the optimal performance under intermediate fusion and ROI size of 80 pixels with appearance transformation. It outperformed CNN and convolutional long-short-term-memory (CLSTM) in predicting luminal B, HER2-enriched and TN subtypes, but without demonstrating statistical significance except against CNN in TN subtypes, with testing AUCs of 0.8182 vs. [0.7208, 0.7922] (p=0.44, 0.80), 0.8500 vs. [0.7300, 0.8200] (p=0.36, 0.70) and 0.8900 vs. [0.7600, 0.8300] (p=0.03, 0.63), respectively. When predicting luminal A, MBCNN outperformed CNN with AUCs of 0.8571 vs. 0.7619 (p=0.08) without achieving statistical significance, and is comparable to CLSTM. For four-subtype prediction, MBCNN achieved an ACC of 0.64, better than CNN and CLSTM models with ACCs of 0.48 and 0.52, respectively. CONCLUSION: Developed DL model with the feature extraction and fusion of DCE-MRI from two institutes enabled preoperative prediction of breast cancer molecular subtypes with high diagnostic performance.

Skin and soft tissue infection suspiciously caused by Klebsiellapneumoniae in an aquaculture worker: A case report.

Skin and soft tissue infections (SSTIs), which are mainly caused by Gram-positive cocci existing on the skin surface, are more common than those caused by Gram-negative bacteria; however, the role of Gram-negative bacteria as emerging pathogens in SSTIs cannot to be ignored. Klebsiella pneumoniae is an opportunistic pathogenic bacterium that mainly inhabits the respiratory and intestinal tracts of humans and animals, as well as the environment, including aquaculture farms. This bacterium can cause multiple infections in humans and animals. The present study reports the case of a SSTI which was suspected to be caused by Klebsiella pneumoniae in a 74-year-old farmer with venous thrombosis. The patient had exposed his four bare limbs to the farmed shrimps and shrimp pond routinely. Pustule and skin ulcers were observed on both the legs of the patient. After receiving anti-infection therapy, the SSTI was almost completely resolved on day 9 and the patient was then discharged.

FetchEEG: a hybrid approach combining feature extraction and temporal-channel joint attention for EEG-based emotion classification.

Objective. Electroencephalogram (EEG) analysis has always been an important tool in neural engineering, and the recognition and classification of human emotions are one of the important tasks in neural engineering. EEG data, obtained from electrodes placed on the scalp, represent a valuable resource of information for brain activity analysis and emotion recognition. Feature extraction methods have shown promising results, but recent trends have shifted toward end-to-end methods based on deep learning. However, these approaches often overlook channel representations, and their complex structures pose certain challenges to model fitting.Approach. To address these challenges, this paper proposes a hybrid approach named FetchEEG that combines feature extraction and temporal-channel joint attention. Leveraging the advantages of both traditional feature extraction and deep learning, the FetchEEG adopts a multi-head self-attention mechanism to extract representations between different time moments and channels simultaneously. The joint representations are then concatenated and classified using fully-connected layers for emotion recognition. The performance of the FetchEEG is verified by comparison experiments on a self-developed dataset and two public datasets.Main results. In both subject-dependent and subject-independent experiments, the FetchEEG demonstrates better performance and stronger generalization ability than the state-of-the-art methods on all datasets. Moreover, the performance of the FetchEEG is analyzed for different sliding window sizes and overlap rates in the feature extraction module. The sensitivity of emotion recognition is investigated for three- and five-frequency-band scenarios.Significance. FetchEEG is a novel hybrid method based on EEG for emotion classification, which combines EEG feature extraction with Transformer neural networks. It has achieved state-of-the-art performance on both self-developed datasets and multiple public datasets, with significantly higher training efficiency compared to end-to-end methods, demonstrating its effectiveness and feasibility.