Applying dynamic contrast-enhanced MRI tracer kinetic models to differentiate benign and malignant soft tissue tumors.

BACKGROUND: To explore the potential of different quantitative dynamic contrast-enhanced (qDCE)-MRI tracer kinetic (TK) models and qDCE parameters in discriminating benign from malignant soft tissue tumors (STTs). METHODS: This research included 92 patients (41females, 51 males; age range 16-86 years, mean age 51.24 years) with STTs. The qDCE parameters (Ktrans, Kep, Ve, Vp, F, PS, MTT and E) for regions of interest of STTs were estimated by using the following TK models: Tofts (TOFTS), Extended Tofts (EXTOFTS), adiabatic tissue homogeneity (ATH), conventional compartmental (CC), and distributed parameter (DP). We established a comprehensive model combining the morphologic features, time-signal intensity curve shape, and optimal qDCE parameters. The capacities to identify benign and malignant STTs was evaluated using the area under the curve (AUC), degree of accuracy, and the analysis of the decision curve. RESULTS: TOFTS-Ktrans, EXTOFTS-Ktrans, EXTOFTS-Vp, CC-Vp and DP-Vp demonstrated good diagnostic performance among the qDCE parameters. Compared with the other TK models, the DP model has a higher AUC and a greater level of accuracy. The comprehensive model (AUC, 0.936, 0.884-0.988) demonstrated superiority in discriminating benign and malignant STTs, outperforming the qDCE models (AUC, 0.899-0.915) and the traditional imaging model (AUC, 0.802, 0.712-0.891) alone. CONCLUSIONS: Various TK models successfully distinguish benign from malignant STTs. The comprehensive model is a noninvasive approach incorporating morphological imaging aspects and qDCE parameters, and shows significant potential for further development.

Multi-institutional validation of a radiomics signature for identification of postoperative progression of soft tissue sarcoma.

BACKGROUND: To develop a magnetic resonance imaging (MRI)-based radiomics signature for evaluating the risk of soft tissue sarcoma (STS) disease progression. METHODS: We retrospectively enrolled 335 patients with STS (training, validation, and The Cancer Imaging Archive sets, n = 168, n = 123, and n = 44, respectively) who underwent surgical resection. Regions of interest were manually delineated using two MRI sequences. Among 12 machine learning-predicted signatures, the best signature was selected, and its prediction score was inputted into Cox regression analysis to build the radiomics signature. A nomogram was created by combining the radiomics signature with a clinical model constructed using MRI and clinical features. Progression-free survival was analyzed in all patients. We assessed performance and clinical utility of the models with reference to the time-dependent receiver operating characteristic curve, area under the curve, concordance index, integrated Brier score, decision curve analysis. RESULTS: For the combined features subset, the minimum redundancy maximum relevance-least absolute shrinkage and selection operator regression algorithm + decision tree classifier had the best prediction performance. The radiomics signature based on the optimal machine learning-predicted signature, and built using Cox regression analysis, had greater prognostic capability and lower error than the nomogram and clinical model (concordance index, 0.758 and 0.812; area under the curve, 0.724 and 0.757; integrated Brier score, 0.080 and 0.143, in the validation and The Cancer Imaging Archive sets, respectively). The optimal cutoff was - 0.03 and cumulative risk rates were calculated. DATA CONCLUSION: To assess the risk of STS progression, the radiomics signature may have better prognostic power than a nomogram/clinical model.

An Artificial Enzyme for Asymmetric Nitrocyclopropanation of α,ß-Unsaturated Aldehydes-Design and Evolution.

The introduction of an abiological catalytic group into the binding pocket of a protein host allows for the expansion of enzyme chemistries. Here, we report the generation of an artificial enzyme by genetic encoding of a non-canonical amino acid that contains a secondary amine side chain. The non-canonical amino acid and the binding pocket function synergistically to catalyze the asymmetric nitrocyclopropanation of α,ß-unsaturated aldehydes by the iminium activation mechanism. The designer enzyme was evolved to an optimal variant that catalyzes the reaction at high conversions with high diastereo- and enantioselectivity. This work demonstrates the application of genetic code expansion in enzyme design and expands the scope of enzyme-catalyzed abiological reactions.

Preoperative CT-based deep learning radiomics model to predict lymph node metastasis and patient prognosis in bladder cancer: a two-center study.

OBJECTIVE: To establish a model for predicting lymph node metastasis in bladder cancer (BCa) patients. METHODS: We retroactively enrolled 239 patients who underwent three-phase CT and resection for BCa in two centers (training set, n = 185; external test set, n = 54). We reviewed the clinical characteristics and CT features to identify significant predictors to construct a clinical model. We extracted the hand-crafted radiomics features and deep learning features of the lesions. We used the Minimum Redundancy Maximum Relevance algorithm and the least absolute shrinkage and selection operator logistic regression algorithm to screen features. We used nine classifiers to establish the radiomics machine learning signatures. To compensate for the uneven distribution of the data, we used the synthetic minority over-sampling technique to retrain each machine-learning classifier. We constructed the combined model using the top-performing radiomics signature and clinical model, and finally presented as a nomogram. We evaluated the combined model's performance using the area under the receiver operating characteristic, accuracy, calibration curves, and decision curve analysis. We used the Kaplan-Meier survival curve to analyze the prognosis of BCa patients. RESULTS: The combined model incorporating radiomics signature and clinical model achieved an area under the receiver operating characteristic of 0.834 (95% CI: 0.659-1.000) for the external test set. The calibration curves and decision curve analysis demonstrated exceptional calibration and promising clinical use. The combined model showed good risk stratification performance for progression-free survival. CONCLUSION: The proposed CT-based combined model is effective and reliable for predicting lymph node status of BCa patients preoperatively. CRITICAL RELEVANCE STATEMENT: Bladder cancer is a type of urogenital cancer that has a high morbidity and mortality rate. Lymph node metastasis is an independent risk factor for death in bladder cancer patients. This study aimed to investigate the performance of a deep learning radiomics model for preoperatively predicting lymph node metastasis in bladder cancer patients. KEY POINTS: • Conventional imaging is not sufficiently accurate to determine lymph node status. • Deep learning radiomics model accurately predicted bladder cancer lymph node metastasis. • The proposed method showed satisfactory patient risk stratification for progression-free survival.

Deep learning signature based on multiphase enhanced CT for bladder cancer recurrence prediction: a multi-center study.

Background: Accurate stratification of recurrence risk for bladder cancer (BCa) is essential for precise individualized therapy. This study aimed to develop and validate a model for predicting the risk of recurrence in BCa patients postoperatively using 3-phase enhanced CT images. Methods: We retrospectively enrolled 874 BCa patients across four centers between January 2006 and December 2021. Patients from one center were used as training set, while the remaining patients went into the validation set. We trained a deep learning (DL) model based on convolutional neural networks using 3-phase enhanced CT images. The resulting prediction scores were entered into Cox regression analysis to obtain DL scores and construct a DL signature. DL scores and clinical features were then used as deep learning radioclinical signature. The predictive performance of DL signature was assessed according to concordance index and area under curve compared with deep learning radioclinical signature, clinical model and a widely accepted staging grading system. Recurrence-free survival (RFS) and overall survival (OS) were also predicted in order to further assess survival benefits. Findings: DL signature showed strong power for predicting recurrence (concordance index, 0.869; area under curve, 0.889) in validation set, outperforming other models and system. In addition, we divided RFS and OS into high and low risk groups by selecting appropriate cutoff values for DL signature, and calculated cumulative recurrence risk rates for both groups. Interpretation: Our proposed DL signature shows promising potential as clinical aid for predicting postoperative recurrence risk in BCa and for stratifying the risk of RFS and OS, which can be applied to guide personalized precision therapy. Funding: There are no sources of funding for this manuscript.

Multiplicative enhancement of stereoenrichment by a single catalyst for deracemization of alcohols.

Stereochemical enrichment of a racemic mixture by deracemization must overcome unfavorable entropic effects as well as the principle of microscopic reversibility; recently, photochemical reaction pathways unveiled by the energetic input of light have led to innovations toward this end, most often by ablation of a stereogenic C(sp3)-H bond. We report a photochemically driven deracemization protocol in which a single chiral catalyst effects two mechanistically different steps, C-C bond cleavage and C-C bond formation, to achieve multiplicative enhancement of stereoinduction, which leads to high levels of stereoselectivity. Ligand-to-metal charge transfer excitation of a titanium catalyst coordinated by a chiral phosphoric acid or bisoxazoline efficiently enriches racemic alcohols that feature adjacent and fully substituted stereogenic centers to enantiomeric ratios up to 99:1. Mechanistic investigations support a pathway of sequential radical-mediated bond scission and bond formation through a common prochiral intermediate and reveal that, although the overall stereoenrichment is high, the selectivity in each individual step is moderate.

CT-based deep learning radiomics nomogram for the prediction of pathological grade in bladder cancer: a multicenter study.

BACKGROUND: To construct and assess a computed tomography (CT)-based deep learning radiomics nomogram (DLRN) for predicting the pathological grade of bladder cancer (BCa) preoperatively. METHODS: We retrospectively enrolled 688 patients with BCa (469 in the training cohort, 219 in the external test cohort) who underwent surgical resection. We extracted handcrafted radiomics (HCR) features and deep learning (DL) features from three-phase CT images (including corticomedullary-phase [C-phase], nephrographic-phase [N-phase] and excretory-phase [E-phase]). We constructed predictive models using 11 machine learning classifiers, and we developed a DLRN by combining the radiomic signature with clinical factors. We assessed performance and clinical utility of the models with reference to the area under the curve (AUC), calibration curve, and decision curve analysis (DCA). RESULTS: The support vector machine (SVM) classifier model based on HCR and DL combined features was the best radiomic signature, with AUC values of 0.953 and 0.943 in the training cohort and the external test cohort, respectively. The AUC values of the clinical model in the training cohort and the external test cohort were 0.752 and 0.745, respectively. DLRN performed well on both data cohorts (training cohort: AUC = 0.961; external test cohort: AUC = 0.947), and outperformed the clinical model and the optimal radiomic signature. CONCLUSION: The proposed CT-based DLRN showed good diagnostic capability in distinguishing between high and low grade BCa.

Size- and Emission-Controlled Synthesis of Full-Color Luminescent Metal-Organic Frameworks for Tryptophan Detection.

The development of nanoscaled luminescent metal-organic frameworks (nano-LMOFs) with organic linker-based emission to explore their applications in sensing, bioimaging and photocatalysis is of great interest as material size and emission wavelength both have remarkable influence on their performances. However, there is lack of platforms that can systematically tune the emission and size of nano-LMOFs with customized linker design. Herein two series of fcu- and csq-type nano-LMOFs, with precise size control in a broad range and emission colors from blue to near-infrared, were prepared using 2,1,3-benzothiadiazole and its derivative based ditopic- and tetratopic carboxylic acids as the emission sources. The modification of tetratopic carboxylic acids using OH and NH2 as the substituent groups not only induces significant emission bathochromic shift of the resultant MOFs, but also endows interesting features for their potential applications. As one example, we show that the non-substituted and NH2 -substituted nano-LMOFs exhibit turn-off and turn-on responses for highly selective and sensitive detection of tryptophan over other nineteen natural amino acids. This work sheds light on the rational construction of nano-LMOFs with specific emission behaviours and sizes, which will undoubtedly facilitate their applications in related areas.

Resurgence and advancement of photochemical hydrogen atom transfer processes in selective alkane functionalizations.

The selective functionalization of alkanes has long been recognized as a prominent challenge and an arduous task in organic synthesis. Hydrogen atom transfer (HAT) processes enable the direct generation of reactive alkyl radicals from feedstock alkanes and have been successfully employed in industrial applications such as the methane chlorination process, etc. Nevertheless, challenges in the regulation of radical generation and reaction pathways have created substantial obstacles in the development of diversified alkane functionalizations. In recent years, the application of photoredox catalysis has provided exciting opportunities for alkane C-H functionalization under extremely mild conditions to trigger HAT processes and achieve radical-mediated functionalizations in a more selective manner. Considerable efforts have been devoted to building more efficient and cost-effective photocatalytic systems for sustainable transformations. In this perspective, we highlight the recent development of photocatalytic systems and provide our views on current challenges and future opportunities in this field.

Lead detoxification of edible fungi Auricularia auricula and Pleurotus ostreatus: the purification of the chelation substances and their effects on rats.

Lead is a global pollutant that causes widespread concern. When a lead enters the body, it is distributed throughout the body and accumulates in the brain, bone, and soft tissues such as the kidney, liver, and spleen. Chelators used for lead poisoning therapy all have side effects to some extent and other drawbacks including high cost. Exploration and utilization of natural antidotes become necessary. To date, few substances originating from edible fungi that are capable of adsorbing lead have been reported. In this study, we found that two commonly eaten mushrooms Auricularia auricula and Pleurotus ostreatus exhibited lead adsorption capacity. A. auricula active substance (AAAS) and P. ostreatus active substance (POAS) were purified by hot-water extraction, ethanol precipitation from its fruiting bodies followed by ion exchange chromatography, ultrafiltration, and gel filtration chromatography, respectively. AAAS was 3.6 kDa, while POAS was 4.9 kDa. They were both constituted of polysaccharides and peptides. The peptide sequences obtained by liquid chromatography combined with tandem mass spectrometry (LC-MS/MS) proved that they were rich in amino acids with side chain groups such as hydroxyl, carboxyl, carbonyl, sulfhydryl, and amidogen. Two rat models were established, but only a chronic lead-induced poisoning model was employed to determine the detoxification of AAAS/POAS and their fruiting body powder. For rats receiving continuous lead treatment, either AAAS or POAS could reduce the lead levels in the blood. They also promoted the elimination of the burden of lead in the spleen and kidney. The fruiting bodies were also proved to have lead detoxification effects. This is the first study to identify new functions of A. auricula and P. ostreatus in reducing lead toxicity and to provide dietary strategies for the treatment of lead toxicity.

Preoperative contrast-enhanced CT-based radiomics nomogram for differentiating benign and malignant primary retroperitoneal tumors.

OBJECTIVES: This study evaluated the ability of a preoperative contrast-enhanced CT (CECT)-based radiomics nomogram to differentiate benign and malignant primary retroperitoneal tumors (PRT). METHODS: Images and data from 340 patients with pathologically confirmed PRT were randomly placed into training (n = 239) and validation sets (n = 101). Two radiologists independently analyzed all CT images and made measurements. Key characteristics were identified through least absolute shrinkage selection combined with four machine-learning classifiers (support vector machine, generalized linear model, random forest, and artificial neural network back propagation) to create a radiomics signature. Demographic data and CECT characteristics were analyzed to formulate a clinico-radiological model. Independent clinical variables were merged with the best-performing radiomics signature to develop a radiomics nomogram. The discrimination capacity and clinical value of three models were quantified by the area under the receiver operating characteristics (AUC), accuracy, and decision curve analysis. RESULTS: The radiomics nomogram was able to consistently differentiate between benign and malignant PRT in the training and validation datasets, with AUCs of 0.923 and 0.907, respectively. Decision curve analysis manifested that the nomogram achieved higher clinical net benefits than did separate use of the radiomics signature and clinico-radiological model. CONCLUSIONS: The preoperative nomogram is valuable for differentiating between benign and malignant PRT; it can also aid in treatment planning. KEY POINTS: • A noninvasive and accurate preoperative determination of benign and malignant PRT is crucial to identifying suitable treatments and predicting disease prognosis. • Associating the radiomics signature with clinical factors facilitates differentiation of malignant from benign PRT with improved diagnostic efficacy (AUC) and accuracy from 0.772 to 0.907 and from 0.723 to 0.842, respectively, compared with the clinico-radiological model alone. • For some PRT with anatomically special locations and when biopsy is extremely difficult and risky, a radiomics nomogram may provide a promising preoperative alternative for distinguishing benignity and malignancy.

Copper-Catalyzed Formal [4 + 1] Annulation toward Diverse Trifunctionalized Indolizines from Pyridinium 1,4-Zwitterionic Thiolates and Diazos.

Pyridinium 1,4-zwitterionic thiolates were regarded as powerful and versatile building blocks to prepare nitrogen- and sulfur-containing heterocycles. Herein, we reported a copper-catalyzed formal [4 + 1] annulation of pyridinium 1,4-zwitterionic thiolates and diazo compounds without any additives to access a library of trifunctionalized indolizines in good yields. Besides, isoquinolinium 1,4-zwitterionic thiolates and imidazolium 1,4-zwitterionic thiolates were also applied to this formal [4 + 1] annulation reaction. Of particular note is that various functional groups such as -CO2R, -CO2NR2, -CF3, -CN, and -(O)P(OR)2 could be easily introduced into cycloaddition products indolizines by this strategy.

A multi-view co-training network for semi-supervised medical image-based prognostic prediction.

Prognostic prediction has long been a hotspot in disease analysis and management, and the development of image-based prognostic prediction models has significant clinical implications for current personalized treatment strategies. The main challenge in prognostic prediction is to model a regression problem based on censored observations, and semi-supervised learning has the potential to play an important role in improving the utilization efficiency of censored data. However, there are yet few effective semi-supervised paradigms to be applied. In this paper, we propose a semi-supervised co-training deep neural network incorporating a support vector regression layer for survival time estimation (Co-DeepSVS) that improves the efficiency in utilizing censored data for prognostic prediction. First, we introduce a support vector regression layer in deep neural networks to deal with censored data and directly predict survival time, and more importantly to calculate the labeling confidence of each case. Then, we apply a semi-supervised multi-view co-training framework to achieve accurate prognostic prediction, where labeling confidence estimation with prior knowledge of pseudo time is conducted for each view. Experimental results demonstrate that the proposed Co-DeepSVS has a promising prognostic ability and surpasses most widely used methods on a multi-phase CT dataset. Besides, the introduction of SVR layer makes the model more robust in the presence of follow-up bias.

A novel mushroom (Auricularia polytricha) glycoprotein protects against lead-induced hepatoxicity, promotes lead adsorption, inhibits organ accumulation of lead, upregulates detoxifying proteins, and enhances immunoregulation in rats.

Introduction: Lead is a ubiquitous environmental and industrial pollutant. Its nonbiodegradable toxicity induces a plethora of human diseases. A novel bioactive glycoprotein containing 1.15% carbohydrate, with the ability of adsorbing lead and effecting detoxification, has been purified from Auricularia polytricha and designated as APL. Besides, its mechanisms related to regulation of hepatic metabolic derangements at the proteome level were analyzed in this study. Methods: Chromatographic techniques were utilized to purify APL in the current study. For investigating the protective effects of APL, Sprague-Dawley rats were given daily intraperitoneal injections of lead acetate for establishment of an animal model, and different dosages of APL were gastrically irrigated for study of protection from lead detoxification. Liver samples were prepared for proteomic analyses to explore the detoxification mechanisms. Results and discussion: The detoxifying glycoprotein APL displayed unique molecular properties with molecular weight of 252-kDa, was isolated from fruiting bodies of the edible fungus A. polytricha. The serum concentrations of lead and the liver function biomarkers aspartate and alanine aminotransferases were significantly (p<0.05) improved after APL treatment, as well as following treatment with the positive control EDTA (300 mg/kg body weight). Likewise, results on lead residue showed that the clearance ratios of the liver and kidneys were respectively 44.5% and 18.1% at the dosage of APL 160 mg/kg, which was even better than the corresponding data for EDTA. Proteomics disclosed that 351 proteins were differentially expressed following lead exposure and the expression levels of 41 proteins enriched in pathways mainly involved in cell detoxification and immune regulation were normalized after treatment with APL-H. The results signify that APL ameliorates lead-induced hepatic injury by positive regulation of immune processing, and suggest that APL can be applied as a therapeutic intervention of lead poisoning in clinical practice. This report represents the first demonstration of the protective action of a novel mushroom protein on lead-elicited hepatic toxicity.

Lumbar MR-based radiomics nomogram for detecting minimal residual disease in patients with multiple myeloma.

OBJECTIVES: Minimal residual disease (MRD) is a standard for assessing treatment response in multiple myeloma (MM). MRD negativity is considered to be the most powerful predictor of long-term good outcomes. This study aimed to develop and validate a radiomics nomogram based on magnetic resonance imaging (MRI) of the lumbar spine to detect MRD after MM treatment. METHODS: A total of 130 MM patients (55 MRD negative and 75 MRD positive) who had undergone MRD testing through next-generation flow cytometry were divided into a training set (n = 90) and a test set (n = 40). Radiomics features were extracted from lumbar spinal MRI (T1-weighted images and fat-suppressed T2-weighted images) by means of the minimum redundancy maximum relevance method and the least absolute shrinkage and selection operator algorithm. A radiomics signature model was constructed. A clinical model was established using demographic features. A radiomics nomogram incorporating the radiomics signature and independent clinical factor was developed using multivariate logistic regression analysis. RESULTS: Sixteen features were used to establish the radiomics signature. The radiomics nomogram included the radiomics signature and the independent clinical factor (free light chain ratio) and showed good performance in detecting the MRD status (area under the curve: 0.980 in the training set and 0.903 in the test set). CONCLUSIONS: The lumbar MRI-based radiomics nomogram showed good performance in detecting MRD status in MM patients after treatment, and it is helpful for clinical decision-making. KEY POINTS: • The presence or absence of minimal residual disease status has a strong predictive significance for the prognosis of patients with multiple myeloma. • A radiomics nomogram based on lumbar MRI is a potential and reliable tool for evaluating minimal residual disease status in MM.

An MRI-Based Radiomics Nomogram to Assess Recurrence Risk in Sinonasal Malignant Tumors.

BACKGROUND: Sinonasal malignant tumors (SNMTs) have a high recurrence risk, which is responsible for the poor prognosis of patients. Assessing recurrence risk in SNMT patients is a current problem. PURPOSE: To establish an MRI-based radiomics nomogram for assessing relapse risk in patients with SNMT. STUDY TYPE: Retrospective. POPULATION: A total of 143 patients with 68.5% females (development/validation set, 98/45 patients). FIELD STRENGTH/SEQUENCE: A 1.5-T and 3-T, fat-suppressed fast spin echo (FSE) T2-weighted imaging (FS-T2WI), FSE T1-weighted imaging (T1WI), and FSE contrast-enhanced T1WI (T1WI + C). ASSESSMENT: Three MRI sequences were used to manually delineate the region of interest. Three radiomics signatures (T1WI and FS-T2WI sequences, T1WI + C sequence, and three sequences combined) were built through dimensional reduction of high-dimensional features. The clinical model was built based on clinical and MRI features. The Ki-67-based and tumor-node-metastasis (TNM) model were established for comparison. The radiomics nomogram was built by combining the clinical model and best radiomics signature. The relapse-free survival analysis was used among 143 patients. STATISTICAL TESTS: The intraclass/interclass correlation coefficients, univariate/multivariate Cox regression analysis, least absolute shrinkage and selection operator Cox regression algorithm, concordance index (C index), area under the curve (AUC), integrated Brier score (IBS), DeLong test, Kaplan-Meier curve, log-rank test, optimal cutoff values. A P value < 0.05 was considered statistically significant. RESULTS: The T1 + C-based radiomics signature had best prognostic ability than the other two signatures (T1WI and FS-T2WI sequences, and three sequences combined). The radiomics nomogram had better prognostic ability and less error than the clinical model, Ki-67-based model, and TNM model (C index, 0.732; AUC, 0.765; IBS, 0.185 in the validation set). The cutoff values were 0.2 and 0.7 and then the cumulative risk rates were calculated. DATA CONCLUSION: A radiomics nomogram for assessing relapse risk in patients with SNMT may provide better prognostic ability than the clinical model, Ki-67-based model, and TNM model. EVIDENCE LEVEL: 3. TECHNICAL EFFICACY: Stage 5.

Developmental changes in proteins of casein micelles in goat milk using data-independent acquisition-based proteomics methods during the lactation cycle.

Casein micelles (CM) play an important role in milk secretion, stability, and processing. The composition and content of milk proteins are affected by physiological factors, which have been widely investigated. However, the variation in CM proteins in goat milk throughout the lactation cycle has yet to be fully clarified. In the current study, milk samples were collected at d 1, 3, 30, 90, 150, and 240 of lactation from 15 dairy goats. The size of CM was determined using laser light scattering, and CM proteins were separated, digested, and identified using data-independent acquisition (DIA) and data-dependent acquisition (DDA)-based proteomics approaches. According to clustering and principal component analysis, protein profiles identified using DIA were similar to those identified using the DDA approach. Significant differences in the abundance of 115 proteins during the lactation cycle were identified using the DIA approach. Developmental changes in the CM proteome corresponding to lactation stages were revealed: levels of lecithin cholesterol acyltransferase, folate receptor α, and prominin 2 increased from 1 to 240 d, whereas levels of growth/differentiation factor 8, peptidoglycan-recognition protein, and 45 kDa calcium-binding protein decreased in the same period. In addition, lipoprotein lipase, glycoprotein IIIb, and α-lactalbumin levels increased from 1 to 90 d and then decreased to 240 d, which is consistent with the change in CM size. Protein-protein interaction analysis showed that fibronectin, albumin, and apolipoprotein E interacted more with other proteins at the central node. These findings indicate that changes in the CM proteome during lactation could be related to requirements of newborn development, as well as mammary gland development, and may thus contribute to elucidating the physical and chemical properties of CM.

Comparative analysis of changes in whey proteins of goat milk throughout the lactation cycle using quantitative proteomics.

The composition and content of goat milk proteins are affected by many factors and have been extensively studied. However, variation in whey protein composition in goat milk throughout the lactation cycle has not been clarified. In the current study, 15 dairy goats were selected, and milk samples were collected at 1, 3, 30, 90, 150, and 240 d after delivery. Whey proteins were separated and digested and then identified using data-independent acquisition (DIA) and data-dependent acquisition proteomics approaches. Protein profiles identified using DIA were consistent with those of the data-dependent acquisition proteomics approach according to clustering and principal component analyses. Significant differences in the abundance of 238 proteins around the lactation cycle were identified using the DIA approach. Developmental changes of the whey proteome corresponding to lactation stage were revealed: plasminogen, α-2-macroglobulin, and fibronectin levels decreased from d 1 to 240, whereas polymeric immunoglobulin receptor, nucleobindin 2, fatty acid-binding protein 3, and lactoperoxidase increased from d 1 to 240. Protein-protein interaction analysis showed that fibronectin with a higher degree of connectivity is a central node. The findings are of great significance to better understanding the potential role of specific proteins and the mechanism of protein biosynthesis or intercellular transport in the mammary glands related to the physiological changes of dairy goats.

Bioactive extracts and association with C and N in Eleutherococcus senticosus subjected to chitosan nanoparticles in contrasting light spectra.

Bioactive compounds are major reasons for the value of Eleutherococcus senticosus, which can be modified by different lighting spectra. Light-emitting diode (LED) provides lights with specific spectra which can interact with other treatments to impact plant bioactive production. Chitosan nanoparticle (CN) is a biopolymer derived from marine creatures. It's usage may be a practical approach to cope with uncertainties in secondary metabolites induced by illumination. Carbon (C) and nitrogen (N) cyclings link plant eco-physiological performance and bioactive substance; hence their associations may reveal the mechanism of joint light-CN interaction. In this study, E. senticosus seedlings were raised under artificial lighting spectra from high-pressure sodium (HPS) lamps (44% red, 55% green, 1% blue) and white (44% red, 47% green, 8% blue) and red colored (73% red, 13% green, 14% blue) LED panels. Half of the seedlings received CN and the other half received distilled water as the control. Compared to the HPS spectrum, the red-light induced stronger shoot growth with greater biomass accumulation and higher water uptake but resulted in lower N concentration and biomass ratio in the root. The white light caused more biomass allocated to the root and strengthened stem C concentration. Stem eleutheroside B increased with shoot growth, while root eleutheroside B had a positive association with leaf C and stem protocatechuic acid had a negative association with leaf N. Having the CN treatment in white and red LED lights is recommended for increasing accumulation of bioactive compounds in the shoots and roots of E. senticosus seedlings, respectively.

The predictive potential of contrast-enhanced computed tomography based radiomics in the preoperative staging of cT4 gastric cancer.

Background: The accuracy of preoperative staging is crucial for cT4 stage gastric cancer patients. The aim of this study was to develop the radiomics model and evaluate its predictive potential for differentiating preoperative cT4 stage gastric cancer patients into pT4b and no-pT4b patients. Methods: A multicenter retrospective analysis of 704 gastric cancer patients with preoperative contrast-enhanced computed tomography (CE-CT) staging cT4 between January 2008 and December 2021. These patients were divided into the training cohort (478 patients, the Affiliated Hospital of Qingdao University) and validation cohort (226 patients, the Weihai Wendeng District People's Hospital). According to the pathological stage of the tumors, the patients were divided into pT4b or no-pT4b stage. In the training cohort, the clinical and radiomics features were analyzed to construct the clinical model, tri-phase radiomics signatures and nomogram. Two kinds of methods were employed to achieve dimensionality reduction: (I) the least absolute shrinkage and selection operator (LASSO); and (II) the minimum redundancy maximum relevance (mRMR) algorithms. We utilized Logistic regression, support vector machine (SVM), Decision tree and Adaptive boosted tree (AdaBoost) algorithms as the machine learning classifiers. The nomogram was constructed on the clinical characteristics and the Rad-score. The performance of the models was evaluated by receiver operating characteristic (ROC) area under the curve (AUC), Decision Curve Analysis (DCA) curve and calibration curve. Results: The 345 pT4b and 359 no-pT4b stage patients were included in this study. In the validation cohort, the AUC of the clinical model was 0.793 (95% CI: 0.732-0.855). The tri-phase radiomics features combined with the SVM algorithm was the best radiomics signature with an AUC of 0.862 (95% CI: 0.812-0.912). The nomogram was the best predictive model of all with an AUC of 0.893 (95% CI: 0.834-0.927). In the training and validation cohorts, the calibration curves and DCA curves of the nomogram showed satisfactory result. Conclusions: CE-CT-based radiomics nomogram offers good accuracy and stability in differentiating preoperative cT4 stage gastric cancer patients into pT4b and non-pT4b stages, which has a great clinical relevance for selecting the course of treatment for cT4 stage gastric cancer patients.