Development and Validation of Clinical-Radiomics Nomogram for Preoperative Prediction of Central Lymph Node Metastasis in Papillary Thyroid Carcinoma.

BACKGROUND: This investigation sought to create and verify a nomogram utilizing ultrasound radiomics and crucial clinical features to preoperatively identify central lymph node metastasis (CLNM) in patients diagnosed with papillary thyroid carcinoma (PTC). METHODS: We enrolled 1069 patients with PTC between January 2022 and January 2023. All patients were randomly divided into a training cohort (n = 748) and a validation cohort (n = 321). We extracted 129 radiomics features from the original gray-scale ultrasound image. Then minimum Redundancy-Maximum Relevance and Least Absolute Shrinkage and Selection Operator regression were used to select the CLNM-related features and calculate the radiomic signature. Incorporating the radiomic signature and clinical risk factors, a clinical-radiomics nomogram was constructed using multivariable logistic regression. The predictive performance of clinical-radiomics nomogram was evaluated by calibration, discrimination, and clinical utility in the training and validation cohorts. RESULTS: The clinical-radiomics nomogram which consisted of five predictors (age, tumor size, margin, lateral lymph node metastasis, and radiomics signature), showed good calibration and discrimination in both the training (AUC 0.960; 95% CI, 0.947-0.972) and the validation (AUC 0.925; 95% CI, 0.895-0.955) cohorts. Discrimination of the clinical-radiomics nomogram showed better discriminative ability than the clinical signature, radiomics signature, and conventional ultrasound model in both the training and validation cohorts. Decision curve analysis showed satisfactory clinical utility of the nomogram. CONCLUSION: The clinical-radiomics nomogram incorporating radiomic signature and key clinical features was efficacious in predicting CLNM in PTC patients.

A self-assembling peptide inhibits the growth and function of fungi via a wrapping strategy.

Fungal infections contribute substantially to human morbidity and mortality. A particular concern is the high rate of mortality associated with invasive fungal infections, which often exceeds 50.0% despite the availability of several antifungal drugs. Herein, we show a self-assembling antifungal peptide (AFP), which is able to bind to chitin on the fungal cell wall and in situ form AFP nanofibers, wrapping fungi. As a result, AFP limits the proliferation of fungi, slows down the morphological transformation of biphasic fungi, and inhibits the adhesion of fungi to host cells and the formation of biofilms. Compared to the broad-spectrum antifungal fluconazole, AFP achieved a comparable inhibitory effect (MIC50 = 3.5 µM) on fungal proliferation. In addition, AFP significantly inhibited the formation of fungal biofilms with the inhibition rate of 69.6% at 1 µM, better than fluconazole (17.2% at 1 µM). In a skin infection model of mice, it was demonstrated that AFP showed significantly superior efficacy to fluconazole. In the systemic candidiasis mouse model, AFP showed similar efficacy to first-line antifungal amphotericin B (AmpB) and anidulafungin (AFG). This study provides a promising wrapping strategy for anti-fungal infection.

Construction of a nomogram for central lymph node metastasis in papillary thyroid cancer based on gender grouping.

BACKGROUND: The status of central lymph nodes is crucial for determining the surgical approach to papillary thyroid carcinoma (PTC). Because of the differences between genders in central lymph node metastasis (CLNM), we aimed to construct separate predictive models for CLNM according to gender. METHODS: In our study, a total of 1258 PTC patients who underwent thyroid cancer surgery from September 2021 to March 2023 were analyzed retrospectively. The data were analysed univariately and multivariately using SPSS software grouped according to gender and nomograms of CLNM were plotted using R software. The variables included in this study were sex, Age, body mass index, Diabetes, chronic lymphocytic thyroiditis (CLT), Suspicious central lymph node (SCLN), A/T, Margin, Microcalcification (MC), BRAF, Number, Location, CLNM. RESULTS: The preoperative nomogram in male patients included four clinical variables: CLT, Margin, Number, Size. The preoperative nomogram of female patients included six clinical variables: Age, SCLN, Margin, MC, Number, Size. The calibration curves showed great agreement in both the training group and the validation group. The decision curve analysis showed the feasibility of nomogram in predicting CLNM in both man and woman. CONCLUSION: Based on the successful establishment of nomogram, we can analyze the variability of CLNM between male and female, which may provide clinicians with personalized clinical schemes in the treatment of PTC.

Binding-induced fibrillogenesis peptide inhibits RANKL-mediated osteoclast activation against osteoporosis.

Osteoporosis is primarily driven by an imbalance between bone resorption and formation, stemming from enhanced osteoclast activity during bone remodeling. At the crux of this mechanism lies the pivotal RANK-RANKL-OPG axis. In our study, we designed two binding-induced fibrillogenesis (BIF) peptides, namely BIFP and BIFY, targeting RANK and RANKL, respectively. These BIF peptides, with distinct hydrophilic and hydrophobic characteristics, assemble into nanoparticles (NPs) in aqueous solution. Through specific ligand-receptor interactions, these NPs efficiently target and bind to specific proteins, resulting in the formation of fibrous networks that effectively inhibit the RANK-RANKL associations. Experiments have confirmed the potent inhibitory effects of peptides on both osteoclast differentiation and function. Compared with the +RANKL controls, BIFP and BIFY demonstrated a more remarkable reduction in tartrate resistant acid phosphatase (TRAP)-positive cells, achieving an impressive decline of 82.8% and 70.7%, respectively. Remarkably, the administration of BIFP led to a substantial reduction in bone resorption pit area by 17.4%, compared to a significant increase of 92.4% in the +RANKL groups. In vivo experiments on an ovariectomized mouse model demonstrated that the BIFP treated group exhibited an impressive 2.6-fold elevation in bone mineral density and an astounding 4.0-fold enhancement in bone volume/total volume as against those of the PBS-treated group. Overall, BIF peptides demonstrate remarkable abilities to impede osteoclast differentiation, presenting promising prospects for the treatment of osteoporosis.

Prediction model of lymph node metastasis posterior to the right recurrent laryngeal nerve.

BACKGROUND: At present, it is still controversial whether lymph nodes posterior to the right recurrent laryngeal nerve (LN-prRLN) in papillary thyroid carcinoma (PTC) patients should be dissected. Failure to dissect metastatic lymph nodes results in continued metastasis from the positive lymph nodes to other regions. Our study aimed to establish a predictive model and predict the probability of metastasis of the lymph nodes posterior to the right recurrent laryngeal nerve (LNM-prRLN) in patients. METHODS: A total of 309 patients underwent surgery for thyroid cancer between May 2019 and September 2022. The risk factors were identified by univariate and multivariate analyses, and statistically significant risk factors identified in the multivariate analysis were included in the nomogram. We used the calibration curve and the receiver operating characteristic (ROC) curve to verify the accuracy of the prediction model. RESULTS: Multivariate analysis showed that irregular tumor margins (OR: 3.549, 95% CI 1.294-9.733, P = 0.014), extrathyroidal extension (OR: 4.507, 95% CI 1.694-11.993, P = 0.003), maximum tumor diameter > 1 cm (OR: 5.729, 95% CI 2.617-12.542, P < 0.001), overweight status (OR: 2.296, 95% CI 1.057-4.987, P = 0.036), high total cholesterol level (OR: 5.238, 95% CI 2.304-11.909, P < 0.001), and multifocality (OR: 11.954, 95% CI 5.233-27.305, P < 0.001) were independent risk factors for LNM-prRLN. The area under the ROC curve was 0.927. The calibration curve showed good agreement between the predicted and observed rates of LNM-prRLN. CONCLUSION: The probability of LNM-prRLN could be predicted by a nomogram based on the statistically significant risk factors identified in the multivariate analysis. This nomogram can guide clinicians when preoperatively evaluating the status of the LN-prRLN with regard to LNM-prRLN in PTC patients. For patients at high risk for LNM-prRLN, the preventive dissection of LN-prRLNs can be considered.

Nomograms for Prediction of High-Volume Lymph Node Metastasis in Papillary Thyroid Carcinoma Patients.

OBJECTIVE: The coexistence rate between chronic lymphocytic thyroiditis (CLT) and papillary thyroid carcinoma (PTC) is quite high. Whether CLT influences metastatic lymph nodes remains uncertain. High-volume lymph node metastasis is recommended as an unfavorable pathological feature. We aimed to investigate risk factors for high-volume central lymph node metastasis (CLNM) and lateral lymph node metastasis (LLNM) in PTC patients. STUDY DESIGN: Retrospective cohort study. SETTING: Changzhou First People's Hospital. METHODS: Clinicopathological characteristics of 1094 PTC patients who underwent surgery in our center from January 2019 to November 2021 were analyzed. RESULTS: The number of metastatic lymph nodes in the central compartment and lateral compartment were lower in the CLT group. We demonstrated that age, BRAF V600E, shape, and the number of foci were risk factors for high-volume CLNM in patients with CLT. For patients without CLT, sex, age, tumor size, number of foci, and margin were risk factors for high-volume CLNM. Tumor size, number of foci, location, and CLNM were all risk factors for high-volume LLNM in patients with or without CLT. Body mass index was only associated with high-volume LLNM in CLT patients. All the above factors were incorporated into nomograms, which showed perfect discriminative ability. CONCLUSION: Separate predictive systems should be used for CLT and non-CLT patients for a more accurate clinical assessment of lymph node status. Our nomograms of predicting high-volume CLNM and LLNM could facilitate risk-stratified management of PTC recurrence and treatment decisions.

Construction of prediction models for determining the risk of lateral lymph node metastasis in patients with thyroid papillary carcinoma based on gender stratification.

BACKGROUND: Lateral lymph node metastasis (LLNM) is associated with poor prognosis in patients with papillary thyroid cancer (PTC). The purpose of this study was to determine the risk factors for LLNM and establish prediction models that could individually assessed the risk of LLNM. METHODS: A total of 619 PTC patients were retrospectively analyzed in our study. Univariate and multivariate analysis were performed for male and female patients, respectively, to assess relationships between clinicopathological features and LLNM. By integrating independent predictors selected by binary logistic regression modeling, preoperative and postoperative nomograms were developed to estimate the risk of LLNM. RESULTS: LLNM was detected in 80 of 216 male patients. Of 403 female patients, 114 had LLNM. The preoperative nomogram of male patients included three clinical variables: the number of foci, tuner size, and echogenic foci. In addition to the above three variables, the postoperative nomogram of male patients included extrathyroidal extension (ETE) detected in surgery, central lymph node metastasis (CLNM) and high-volume CLNM. The preoperative nomogram of female patients included the following variables: age, chronic lymphocytic thyroiditis (CLT), BRAF V600E, the number of foci, tumor size and echogenic foci. Variables such as CLT, BRAF V600E, the number of foci, tumor size, ETE detected in surgery, CLNM, high-volume CLNM and central lymph node ratio were included in the postoperative nomogram. Above Nomograms show good discrimination. CONCLUSIONS: Considering the difference in the incidence rate of LLNM between men and women, a separate prediction system should be established for patients of different genders. These nomograms are helpful in promoting the risk stratification of PTC treatment decision-making and postoperative management.

An intelligent peptide recognizes and traps Mycobacterium tuberculosis to inhibit macrophage phagocytosis.

Tuberculosis is a major public health concern worldwide, and it is a serious threat to human health for a long period. Macrophage phagocytosis of Mycobacterium tuberculosis (M. tuberculosis) is a crucial process for granuloma formation, which shelters the bacteria and gives them an opportunity for re-activation and spread. Herein, we report an intelligent anti-microbial peptide that can recognize and trap the M. tuberculosis, inhibiting the macrophage phagocytosis process. The peptide (Bis-Pyrene-KLVFF-WHSGTPH, in abbreviation as BFH) first self-assembles into nanoparticles, and then forms nanofibers upon recognizing and binding M. tuberculosis. Subsequently, BFH traps M. tuberculosis by the in situ formed nanofibrous networks and the trapped M. tuberculosis are unable to invade host cells (macrophages). The intelligent anti-microbial peptide can significantly inhibit the phagocytosis of M. tuberculosis by macrophages, thereby providing a favorable theoretical basis for inhibiting the formation of tuberculosis granulomas.

LASSO-based machine learning models for the prediction of central lymph node metastasis in clinically negative patients with papillary thyroid carcinoma.

Background: The presence of central lymph node metastasis (CLNM) is crucial for surgical decision-making in clinical N0 (cN0) papillary thyroid carcinoma (PTC) patients. We aimed to develop and validate machine learning (ML) algorithms-based models for predicting the risk of CLNM in cN0 patients. Methods: A total of 1099 PTC patients with cN0 central neck from July 2019 to March 2022 at our institution were retrospectively analyzed. All patients were randomly split into the training dataset (70%) and the validation dataset (30%). Eight ML algorithms, including the Logistic Regression, Gradient Boosting Machine, Extreme Gradient Boosting (XGB), Random Forest (RF), Decision Tree, Neural Network, Support Vector Machine and Bayesian Network were used to evaluate the risk of CLNM. The performance of ML models was evaluated by the area under curve (AUC), sensitivity, specificity, and decision curve analysis (DCA). Results: We firstly used the LASSO Logistic regression method to select the most relevant factors for predicting CLNM. The AUC of XGB was slightly higher than RF (0.907 and 0.902, respectively). According to DCA, RF model significantly outperformed XGB model at most threshold points and was therefore used to develop the predictive model. The diagnostic performance of RF algorithm was dependent on the following nine top-rank variables: size, margin, extrathyroidal extension, sex, echogenic foci, shape, number, lateral lymph node metastasis and chronic lymphocytic thyroiditis. Conclusion: By incorporating clinicopathological and sonographic characteristics, we developed ML-based models, suggesting that this non-invasive method can be applied to facilitate individualized prediction of occult CLNM in cN0 central neck PTC patients.

A comparative analysis of eight machine learning models for the prediction of lateral lymph node metastasis in patients with papillary thyroid carcinoma.

Background: Lateral lymph node metastasis (LLNM) is a contributor for poor prognosis in papillary thyroid cancer (PTC). We aimed to develop and validate machine learning (ML) algorithms-based models for predicting the risk of LLNM in these patients. Methods: This is retrospective study comprising 1236 patients who underwent initial thyroid resection at our institution between January 2019 and March 2022. All patients were randomly split into the training dataset (70%) and the validation dataset (30%). Eight ML algorithms, including the Logistic Regression, Gradient Boosting Machine, Extreme Gradient Boosting, Random Forest (RF), Decision Tree, Neural Network, Support Vector Machine and Bayesian Network were used to evaluate the risk of LLNM. The performance of ML models was evaluated by the area under curve (AUC), sensitivity, specificity, and decision curve analysis. Results: Among the eight ML algorithms, RF had the highest AUC (0.975), with sensitivity and specificity of 0.903 and 0.959, respectively. It was therefore used to develop as prediction model. The diagnostic performance of RF algorithm was dependent on the following nine top-rank variables: central lymph node ratio, size, central lymph node metastasis, number of foci, location, body mass index, aspect ratio, sex and extrathyroidal extension. Conclusion: By combining clinical and sonographic characteristics, ML algorithms can achieve acceptable prediction of LLNM, of which the RF model performs best. ML algorithms can help clinicians to identify the risk probability of LLNM in PTC patients.

Influence of Obesity Parameters on Different Regional Patterns of Lymph Node Metastasis in Papillary Thyroid Cancer.

Objective: Obesity increases risk of thyroid cancer. However, the association between obesity and the progression of papillary thyroid cancer (PTC) remains controversial. This retrospective study aimed to explore the relationship between obesity and regional patterns of lymph node metastasis (LNM) in PTC. Methods: We retrospectively reviewed data from 1015 patients with PTC. We calculated obese parameters, such as body mass index (BMI), body fat percentage (BFP), and body surface area (BSA). Logistic regression models were used to assess associations between obese parameters and the rate of lymph node metastasis (LNM), number of LNM, pattern of LNM, and lymph node ratio (LNR). Results: Higher BMI was not associated with different regional patterns of LNM in PTC. In men with PTC, high BFP was an independent predictor of total LNM, central lymph node metastasis (CLNM), total lateral lymph node metastasis (LLNM), multiple lateral lymph node metastasis, and simultaneous metastasis in lateral compartment. In addition, male patients with high BFP had higher central LNR and higher number of CLNM. For women, high BSA was an independent predictor of LLNM and level IV metastasis. Female patients with high BSA had higher number of CLNM. Conclusion: BFP and BSA, possibly influenced by gender, were positively associated with the number and risk of LNM in different regions of PTC patients. However, BMI was not the predictor for aggressiveness of PTC in terms of LNM. Clinical decision-making for regional LNM in PTC patients should consider the factor of obesity.

Biomimetic peptide nanoparticles participate in natural coagulation for hemostasis and wound healing.

Uncontrolled hemorrhage is a major problem both in surgical intervention and after trauma. Herein, we design an in situ constructable peptide network, mimicking and participating in the native coagulation process for enhanced hemostasis and wound healing. The network consists of two peptides including C6KL, mimicking platelets and C6KG, mimicking fibrin. The C6KL nanoparticles could bind to the collagen at the wound site and transform into C6KL nanofibers. The C6KG nanoparticles could bind to GPIIb/IIIa receptors on the surface of activated platelets and transform into C6KG nanofibers. The in situ formed peptide network could interwind platelets, fibrin and red blood cells, causing embolism at the wound site. In a lethal femoral artery, vein, and nerve cut model of rats, the amount of bleeding was reduced to 32.8% by C6KL and C6KG with chitosan/alginate. The biomimetic peptides show great clinical potential as trauma hemostatic agents.