Applying GC-MS based serum metabolomic profiling to characterize two traditional Chinese medicine subtypes of diabetic foot gangrene.

Traditional Chinese medicine (TCM) has a long history and particular advantages in the diagnosis and treatment of diabetic foot gangrene (DFG). Patients with DFG are mainly divided into two subtypes, tendon lesion with edema (GT) and ischemic lesion without edema (GI), which are suitable for different medical strategies. Metabolomics has special significance in unravelling the complexities of multifactorial and multisystemic disorders. This study acquired the serum metabolomic profiles of two traditional Chinese medicine subtypes of DFG to explore potential molecular evidence for subtype characterization, which may contribute to the personalized treatment of DFG. A total of 70 participants were recruited, including 20 with DM and 50 with DFG (20 with GI and 30 with GT). Conventional gas chromatography-mass spectrometry (GC-MS) followed by orthogonal partial least-squares discriminant analysis (OPLS-DA) were used as untargeted metabolomics approaches to explore the serum metabolomic profiles. Kyoto encyclopedia of genes and genomes (KEGG) and MetaboAnalyst were used to identify the related metabolic pathways. Compared with DM patients, the levels of 14 metabolites were altered in the DFG group, which were also belonged to the differential metabolites of GI (13) and GT (7) subtypes, respectively. Among these, urea, α-D-mannose, cadaverine, glutamine, L-asparagine, D-gluconic acid, and indole could be regarded as specific potential metabolic markers for GI, as well as L-leucine for GT. In the GI subtype, D-gluconic acid and L-asparagine are positively correlated with activated partial thromboplastin time (APTT) and fibrinogen (FIB). In the GT subtype, L-leucine is positively correlated with the inflammatory marker C-reactive protein (CRP). Arginine and proline metabolism, glycine, serine and threonine metabolism, phenylalanine, tyrosine and tryptophan biosynthesis are the most important metabolic pathways associated with GI. The main metabolic pathways related to GT include pyrimidine metabolism, glutathione metabolism, biosynthesis of valine, leucine, and isoleucine, as well as valine, serine, and isoleucine with metabolites. The results of this study indicate that patients with different DFG subtypes have distinct metabolic profiles, which reflect the pathological characteristics of each subtype respectively. These findings will help us explore therapeutic targets for DFG and develop precise treatment strategies.

Limited value of platelet-related markers in diagnosing periprosthetic joint infection.

OBJECTIVE: To evaluate the diagnostic values of serum platelet count (PC), mean platelet volume ratio (MPV), platelet count to mean platelet volume ratio (PVR), platelet to lymphocyte ratio (PLR), platelet to neutrophil ratio (PNR), PC/Albumin-globulin ratio (PC/AGR), and PC/C-reactive protein (PC/ CRP) in the diagnosis of periprosthetic joint infection (PJI). METHODS: The medical records were retrospectively analyzed of the 158 patients who had undergone hip or knee revisions from January 2018 to May 2022. Of them, 79 cases were diagnosed with PJI and 79 with aseptic loosening (AL). PJI was defined using the Musculoskeletal Infection Society criteria. The plasma levels of CRP, the erythrocyte sedimentation rate (ESR), PC, MPV, PVR, PLR, PNR, PC/AGR, and PC/CRP in the 2 groups were recorded and analyzed. In addition, tests were performed according to different joint types. The receiver operating characteristic curve was used to calculate the sensitivity and specificity of each indicator. The diagnostic value for each indicator was calculated according to the area under the curve (AUC). RESULTS: The PC, PVR, PLR and PC/AGR levels in the PJI group were significantly higher than those in the AL group, while PC/CRP levels were significantly lower (P < 0.001). The AUC for PC/CRP, and PC/AGR was 0.804 and 0.802, respectively, which were slightly lower than that of CRP (0.826) and ESR (0.846). ROC analysis for PC/CRP, and PC/AGR revealed a cut-off value of 37.80 and 160.63, respectively, which provided a sensitivity of 73.42% and 84.81% and a specificity of 75.95% and 65.82% for PJI. The area under the curve of PLR and PC was 0.738 and 0.702. The area under the curve values for PVR, PNR, and MPV were 0.672, 0.553, and 0.544, respectively. CONCLUSIONS: The results of this study suggest that PC, PLR, PC/CRP, and PC/AGR values do not offer significant advantages over ESR or CRP values when employed for the diagnosis of PJI. PVR, PNR, and MPV were not reliable in the diagnosis of PJI.

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.

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.

Constructing a nomogram based on the distribution of thyroid nodules and suspicious lateral cervical lymph nodes in fine-needle aspiration biopsies to predict metastasis in papillary thyroid carcinoma.

Purpose: Elevated concentrations of thyroglobulin eluent is a risk factor for lateral cervical lymph node metastasis (LLNM) in patients with papillary thyroid cancer (PTC). We aimed to develop a practical nomogram based on the distribution of thyroid nodules and the presence of suspicious lateral cervical lymph nodes in fine-needle aspiration biopsies (LN-FNABs), including the cytopathology and the suspicious lateral cervical lymph node (LLN) thyroglobulin eluent (Tg), to predict the possibility of LLNM preoperatively in patients with PTC. Methods: The clinical data of PTC patients who were admitted to the Third Affiliated Hospital of Soochow University from January 2022 to May 2023 to undergo fine-needle aspiration biopsy (FNAB) were included in this study. A total of 208 patients in 2022 served as the training set (70%), and 89 patients in 2023 served as the validation set (30%). The clinical characteristics and LN-FNAB results were collected to determine the risk factors of LLNM. A preoperative nomogram was developed for predicting LLNM based on the results of the univariate and multivariate analyses. Internal calibration, external calibration, and decision curve analysis (DCA) were performed for these models. Results: The multivariate logistic regression analysis showed that the maximum thyroid nodule diameter (Odds Ratio (OR) 2.323, 95% CI 1.383 to 3.904; p = 0.001), Tg level (OR 1.007, 95% CI 1.005 to 1.009; p = 0.000), Tg divided by serum thyroglobulin, (Tg/sTg) [odds ratio (OR) 1.005, 95% CI 1.001 to 1.008; p = 0.009], and cytopathology (OR 9.738, 95% CI 3.678 to 25.783; p = 0.000) (all p < 0.05) had a significant impact on the LLNM of patients with suspicious LLNs. The nomogram showed a better predictive value in both the training cohort [area under the curve, (AUC) 0.937, 95% CI 0.895 to 0.966] and the validation cohort (AUC 0.957, 95% CI 0.892 to 0.989). The nomogram also showed excellent internal and external calibration in predicting LLNM. According to the DCA, the diagnostic performance of this model was dependent on the following variables: maximum thyroid nodule diameter, Tg level, Tg/sTg, and cytopathology. Conclusion: Based on the aforementioned risk factors, we believe that it is necessary to establish a personalized LLNM model for patients with PTC. Using this practical nomogram, which combines clinical and Tg risk factors, surgeons could accurately predict the possibility of LLNM preoperatively. The nomogram will also help surgeons to establish personalized treatment plans before surgery.

Energy and Arginine Density in the Diets of Arbor Acre Hens from 40 to 50 Weeks of Age: Effects on Development and Lipid Metabolism of Embryos.

The effects of maternal dietary energy and arginine level on embryonic development and serum lipid metabolism were investigated in this study. A 2 × 3 factorial experiment was conducted with six treatments represented by 10 replicates of eight Arbor Acre broiler breeder hens each. Diets fed from 40 to 50 weeks of age were formulated to contain two digestible arginine levels (9.6 g/kg and 14.5 g/kg) and three metabolic energy levels (10.08 MJ ME/kg, 11.88 MJ ME/kg, and 13.68 MJ ME/kg). Artificial insemination was used, and eggs collected from 50 weeks of hens' age were hatched. Embryonic growth, biochemical and endocrine indexes of embryonic serum and allantoic fluid were measured on different embryonic days (E). The results were as follows: Egg weight (E0, E11, E13) and embryonic weight (E12, E15) in the high-energy group (13.68 MJ ME/kg) were significantly decreased (p < 0.01), as were embryonic breast rate (E13, E15, E21), thigh rate (E13-E21) and liver rate (E15-E21). The reciprocal effects of arginine and energy were significant on breast rate (E11, E13, E17), thigh rate (E19, E21) and liver rate (E11, E19) of the embryo (p < 0.05). CHO (E13-E19), high-density lipoprotein (E13, E15, E21), low-density lipoprotein (E15, E19, E21), and blood glucose (E13) levels in embryonic serum decreased with the increase in maternal dietary energy level (p < 0.05), but triglyceride levels (E19, E21) showed the opposite result (p < 0.05). The levels of cholesterol and blood glucose in embryonic serum at E11 and urea nitrogen in allantoic fluid at E11-E15 were significantly decreased in the 14.5 g/kg arginine group (p < 0.01). With the increase in maternal dietary energy and arginine levels, embryonic serum nitric oxide synthases levels (E11, E15, E19) increased significantly (p < 0.01). The reciprocal effect of arginine and energy in maternal diets was significant on the embryonic serum high-density lipoprotein level at E21 (p < 0.05). Embryonic serum insulin levels at E13 were significantly elevated in the high-energy group (13.68 MJ ME/kg). The reciprocal effect of arginine and energy was significant on the embryonic serum growth hormone level (p < 0.01). Embryonic serum growth hormone levels were significantly reduced in the 14.5 g/kg arginine and 13.68 MJ/kg metabolic energy group (p < 0.01). In conclusion, maternal restricted feeding improved embryonic development and regulated lipid metabolism-related indices in embryonic serum. Maternal dietary addition of digestible arginine had a significant effect on lipid metabolism indices in embryos. There was a maternal effect of maternal dietary energy and arginine levels on embryo growth and development. The deposition of maternal nutrients affects the development of embryos.

Exosomes: Potential key players towards novel therapeutic options in diabetic wounds.

Diabetic wounds are usually difficult to heal, and wounds in foot in particular are often aggravated by infection, trauma, diabetic neuropathy, peripheral vascular disease and other factors, resulting in serious foot ulcers. The pathogenesis and clinical manifestations of diabetic wounds are complicated, and there is still a lack of objective and in-depth laboratory diagnosis and classification standards. Exosomes are nanoscale vesicles containing DNA, mRNA, microRNA, cyclic RNA, metabolites, lipids, cytoplasm and cell surface proteins, etc., which are involved in intercellular communication and play a crucial role in vascular regeneration, tissue repair and inflammation regulation in the process of diabetic wound healing. Here, we discussed exosomes of different cellular origins, such as diabetic wound-related fibroblasts (DWAF), adipose stem cells (ASCs), mesenchymal stem cells (MSCs), immune cells, platelets, human amniotic epithelial cells (hAECs), epidermal stem cells (ESCs), and their various molecular components. They exhibit multiple therapeutic effects during diabetic wound healing, including promoting cell proliferation and migration associated with wound healing, regulating macrophage polarization to inhibit inflammatory responses, promoting nerve repair, and promoting vascular renewal and accelerating wound vascularization. In addition, exosomes can be designed to deliver different therapeutic loads and have the ability to deliver them to the desired target. Therefore, exosomes may become an innovative target for precision therapeutics in diabetic wounds. In this review, we summarize the latest research on the role of exosomes in the healing of diabetic wound by regulating the pathogenesis of diabetic wounds, and discuss their potential applications in the precision treatment of diabetic wounds.

An Exciting Performance of Established and Novel Biomarkers in Diagnosing Periprosthetic Joint Infections: A Single-center Retrospective Cohort Study.

OBJECTIVE: Significant progress has been made in recent years in the diagnosis of periprosthetic joint infections (PJI). However, the lack of a gold standard test for the diagnosis of PJI remains a challenge.The aim of this study was to evaluate the diagnostic values of the albumin/fibrinogen ratio (AFR), the C-reactive protein/albumin ratio (CAR), and the levels of fibrinogen (FIB) and albumin (ALB) in the diagnosis of PJI. METHODS: The medical records of 158 patients who had undergone hip or knee revisions from January 2018 to May 2022 were retrospectively analyzed. Of these patients, 79 were diagnosed with PJI, while 79 were diagnosed with aseptic loosening (AL). PJI was defined using the Musculoskeletal Infection Society criteria. The plasma levels of C-reactive protein (CRP), ALB, and FIB; the erythrocyte sedimentation rate (ESR); and the AFR and CAR in the two groups were recorded and analyzed. The receiver operating characteristic curve was used to calculate the sensitivity and specificity of each indicator; the diagnostic value for each indicator was calculated as the area under the curve (AUC). RESULTS: The ESR, CRP, FIB, and CAR values in the PJI group were significantly higher than those in the AL group, and the ALB and AFR values were significantly lower than those in the AL group (p < 0.001). The AUC values of AFR and fibrinogen were 0.851 and 0.848, respectively, which were slightly higher than those of CRP (0.826) and ESR (0.846). The AUC of CAR was 0.831 which was slightly lower than that of CRP (0.846). ALB had an AUC of 0.727. The optimal threshold, sensitivity, and specificity, respectively, were 10.05, 84.81%, and 82.28% for AFR; 4.03 µg/mL, 77.22%, and 86.08% for FIB; 0.23, 72.15%, and 82.28% for CAR; and 37.30 g/L, 65.82%, and 73.42% for ALB. CONCLUSIONS: AFR, CAR, and FIB are good new auxiliary diagnostic indicators of PJI, while ALB is of fair value for the diagnosis of PJI.

iDRPro-SC: identifying DNA-binding proteins and RNA-binding proteins based on subfunction classifiers.

Nucleic acid-binding proteins are proteins that interact with DNA and RNA to regulate gene expression and transcriptional control. The pathogenesis of many human diseases is related to abnormal gene expression. Therefore, recognizing nucleic acid-binding proteins accurately and efficiently has important implications for disease research. To address this question, some scientists have proposed the method of using sequence information to identify nucleic acid-binding proteins. However, different types of nucleic acid-binding proteins have different subfunctions, and these methods ignore their internal differences, so the performance of the predictor can be further improved. In this study, we proposed a new method, called iDRPro-SC, to predict the type of nucleic acid-binding proteins based on the sequence information. iDRPro-SC considers the internal differences of nucleic acid-binding proteins and combines their subfunctions to build a complete dataset. Additionally, we used an ensemble learning to characterize and predict nucleic acid-binding proteins. The results of the test dataset showed that iDRPro-SC achieved the best prediction performance and was superior to the other existing nucleic acid-binding protein prediction methods. We have established a web server that can be accessed online: http://bliulab.net/iDRPro-SC.

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.

Wounds under diabetic milieu: The role of immune cellar components and signaling pathways.

A major challenge in the field of diabetic wound healing is to confirm the body's intrinsic mechanism that could sense the immune system damage promptly and protect the wound from non-healing. Accumulating literature indicates that macrophage, a contributor to prolonged inflammation occurring at the wound site, might play such a role in hindering wound healing. Likewise, other immune cell dysfunctions, such as persistent neutrophils and T cell infection, may also lead to persistent oxidative stress and inflammatory reaction during diabetic wound healing. In this article, we discuss recent advances in the immune cellular components in wounds under the diabetic milieu, and the role of key signaling mechanisms that compromise the function of immune cells leading to persistent wound non-healing.

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.

Nomograms for the prediction of lateral lymph node metastasis in papillary thyroid carcinoma: Stratification by size.

Background: Lateral lymph node metastasis (LLNM) is a risk factor of poor prognosis in papillary thyroid cancer (PTC). We aimed to determine predictive factors and develop the nomograms for LLNM in patients with papillary thyroid microcarcinoma (PTMC) and macro-PTC. Methods: We reviewed the medical records of 1,106 patients who underwent surgery between January 2019 and January 2022. Patients were divided into a PTMC and a macro-PTC group. We developed preoperative and postoperative nomograms for predicting LLNM based on results of multivariate analysis. Internal calibration was performed for these models. Results: The number of metastatic lymph nodes in lateral compartment was higher in macro-PTC patients. LLNM was independently associated with gender, the number of foci, location, shape, and central lymph node metastasis (CLNM) in PTMC patients. For macro-PTC patients, chronic lymphocytic thyroiditis, the number of foci, location, margin, CLNM, and central lymph node ratio were all independent predictors for LLNM. All the above factors were incorporated into nomograms, which showed the perfect discriminative ability. Conclusion: The diameter of the tumor has an impact on the rate of LLNM. Separate predictive systems should be used for PTMC and macro-PTC patients for more accurate clinical assessment of lateral lymph node status. Through these nomograms, we can not only detect high-risk patients with occult LLNM preoperatively, but also form appropriate treatment protocols for postoperative management of PTC patients with different risks.

Recent insights into the role of defensins in diabetic wound healing.

Diabetic wound, one of the most common serious complications of diabetic patients, is an important factor in disability and death. Much of the research on the pathophysiology of diabetic wound healing has long focused on mechanisms mediated by hyperglycemia, chronic inflammation, microcirculatory and macrocirculatory dysfunction. However, recent evidence suggests that defensins may play a crucial role in the development and perpetuation of diabetic wound healing. The available findings suggest that defensins exert a beneficial influence on diabetic wound healing through antimicrobial, immunomodulatory, angiogenic, tissue regenerator effects, and insulin resistance improvement. Therefore, summarizing the existing research progress on defensins in the diabetic wound may present a promising strategy for diabetic patients.

iDRBP-ECHF: Identifying DNA- and RNA-binding proteins based on extensible cubic hybrid framework.

Proteins interact with nucleic acids to regulate the life activities of organisms. Therefore, how to accurately and efficiently identify nucleic acid-binding proteins (NABPs) is particularly significant. Some sequence-based computational methods have been proposed to identify DNA- and RNA-binding proteins in previous studies. However, the benchmark datasets used by these methods ignore the proportion of NABPs in the real world, and some integration methods only integrate traditional machine learning algorithms, resulting in limited prediction performance. In this study, we proposed a sequence-based method called iDRBP-ECHF to predict the DNA-binding proteins (DBPs) and RNA-binding proteins (RBPs). We constructed a benchmark dataset by considering the proportion of positive and negative samples in the real world, and used down-sampling to generate three relatively balanced datasets to train the iDRBP-ECHF. In addition, we incorporated the deep learning algorithms into the framework to obtain a more compact high-level feature representation of the input data. The results on two independent datasets show that it achieves the most advanced performance and is superior to the other existing sequence-based DBP and RBP prediction methods. In addition, we set up a webserver iDRBP-ECHF, which can be accessed at http://bliulab.net/iDRBP-ECHF.

Oxidative Stress and Lipid Peroxidation: Prospective Associations Between Ferroptosis and Delayed Wound Healing in Diabetic Ulcers.

Diabetic ulcers are one of the major complications of diabetes, and patients usually suffer from amputation and death due to delayed ulcer wound healing. Persistent inflammation and oxidative stress at the wound site are the main manifestations of delayed wound healing in diabetic ulcers. In addition, chronic hyperglycemia in patients can lead to circulatory accumulation of lipid peroxidation products and impaired iron metabolism pathways leading to the presence of multiple free irons in plasma. Ferroptosis, a newly discovered form of cell death, is characterized by intracellular iron overload and accumulation of iron-dependent lipid peroxides. These indicate that ferroptosis is one of the potential mechanisms of delayed wound healing in diabetic ulcers and will hopefully be a novel therapeutic target for delayed wound healing in diabetic patients. This review explored the pathogenesis of diabetic ulcer wound healing, reveals that oxidative stress and lipid peroxidation are common pathological mechanisms of ferroptosis and delayed wound healing in diabetic ulcers. Based on strong evidence, it is speculated that ferroptosis and diabetic ulcers are closely related, and have value of in-depth research. We attempted to clarify prospective associations between ferroptosis and diabetic ulcers in terms of GPX4, iron overload, ferroptosis inhibitors, AGEs, and HO-1, to provide new ideas for exploring the clinical treatment of diabetic ulcers.