Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

In recent years, the application of artificial intelligence (AI) in the field of biology has witnessed remarkable advancements. Among these, the most notable achievements have emerged in the domain of protein structure prediction and design, with AlphaFold and related innovations earning the 2024 Nobel Prize in Chemistry. These breakthroughs have transformed our ability to understand protein folding and molecular interactions, marking a pivotal milestone in computational biology. Looking ahead, it is foreseeable that the accurate prediction of various physicochemical properties of proteins—beyond static structure—will become the next critical frontier in this rapidly evolving field. One of the most important protein properties is thermodynamic stability, which refers to a protein’s ability to maintain its native conformation under physiological or stress conditions. Accurate prediction of protein stability, especially upon single-point mutations, plays a vital role in numerous scientific and industrial domains. These include understanding the molecular basis of disease, rational drug design, development of therapeutic proteins, design of more robust industrial enzymes, and engineering of biosensors. Consequently, the ability to reliably forecast the stability changes caused by mutations has broad and transformative implications across biomedical and biotechnological applications. Historically, protein stability was assessed via experimental methods such as differential scanning calorimetry (DSC) and circular dichroism (CD), which, while precise, are time-consuming and resource-intensive. This prompted the development of computational approaches, including empirical energy functions and physics-based simulations. However, these traditional models often fall short in capturing the complex, high-dimensional nature of protein conformational landscapes and mutational effects. Recent advances in machine learning (ML) have significantly improved predictive performance in this area. Early ML models used handcrafted features derived from sequence and structure, whereas modern deep learning models leverage massive datasets and learn representations directly from data. Deep neural networks (DNNs), graph neural networks (GNNs), and attention-based architectures such as transformers have shown particular promise. GNNs, in particular, excel at modeling spatial and topological relationships in molecular structures, making them well-suited for protein modeling tasks. Furthermore, attention mechanisms enable models to dynamically weigh the contribution of specific residues or regions, capturing long-range interactions and allosteric effects. Nevertheless, several key challenges remain. These include the imbalance and scarcity of high-quality experimental datasets, particularly for rare or functionally significant mutations, which can lead to biased or overfitted models. Additionally, the inherently dynamic nature of proteins—their conformational flexibility and context-dependent behavior—is difficult to encode in static structural representations. Current models often rely on a single structure or average conformation, which may overlook important aspects of stability modulation. Efforts are ongoing to incorporate multi-conformational ensembles, molecular dynamics simulations, and physics-informed learning frameworks into predictive models. This paper presents a comprehensive review of the evolution of protein thermodynamic stability prediction techniques, with emphasis on the recent progress enabled by machine learning. It highlights representative datasets, modeling strategies, evaluation benchmarks, and the integration of structural and biochemical features. The aim is to provide researchers with a structured and up-to-date reference, guiding the development of more robust, generalizable, and interpretable models for predicting protein stability changes upon mutation. As the field moves forward, the synergy between data-driven AI methods and domain-specific biological knowledge will be key to unlocking deeper understanding and broader applications of protein engineering.

Objective: To explore the association between processed food consumption and anxiety symptoms among college students in Yunnan Province, so as to provide a reference for the prevention and treatment of anxiety symptoms in this population. Methods: A cluster random sample of 2 515 first year students from two universities in Yunnan Province was selected to carry out a longitudinal investigation which included a baseline survey (November 2021, T1) and three follow up visits (June 2022, T2; November 2022, T3; June 2023, T4). The food frequency questionnaire was administered to assess processed food consumption, and the Depression Anxiety Stress Scale-21 (DASS-21, Chinese version) was used to evaluate anxiety symptoms. A generalized estimation equation model was used to analyze the relationship between processed food consumption and anxiety symptoms. Results: The detection rates of T1-T4 anxiety symptoms among college students in Yunnan Province were 29.70%, 36.70%, 37.69% and 38.73 %, respectively, and the corresponding anxiety symptom scores were 4(0,8), 4(0,10), 4(0,12), 2(0,14). After controlling for demographic variables and confounding factors in the generalized estimation equation model, a statistically significant association was found between consumption of carbonated beverages ( β=0.06, 95%CI =0.03-0.08), and other processed snacks ( β= 0.04 , 95%CI =0.01-0.07) ( P <0.05). The stratified analysis by gender showed that the consumption of carbonated beverages ( β=0.08, 95%CI =0.05-0.12) and fast food ( β=0.03, 95%CI =0.00-0.06) was significantly associated with anxiety symptoms in female college students ( P <0.05). There was no significant association between processed food consumption and anxiety symptoms in male college students ( P >0.05). Conclusions Processed food consumption by college students in Yunnan Province may increase the risk of anxiety symptoms, particularly among female students. There is a need to strengthen guidance in respect to processed food consumption, so as to prevent and treat anxiety symptoms.

Objective: To analyze the longitudinal association between mobile phone dependence and depressive symptoms in college students, so as to provide a theoretical basis for psychological health education among college students. Methods: From November 2021 to June 2023, 2 515 first year students from 2 universities in Yunnan Province were surveyed with a questionnaire by a cluster random sampling method, including baseline survey (November 2021, T1) and three follow up visits (June 2022, T2; November 2022, T3; June 2023, T4). The Self rating Questionnaire for Adolescent Problematic Mobile Phone Use and the Depression Anxiety Stress Scales-21 (DASS-21) were used to evaluate mobile phone dependence and depressive symptoms of college students. The χ 2 test was used to analyze the difference in depressive symptoms among different demographic groups, and a generalized estimation equation model was established to analyze the association between mobile phone dependence symptoms and depressive symptoms. Results: The detection rates of depressive symptoms among university students in Yunnan Province at time points T1, T2, T3, and T4 were 23.02%, 33.36%, 34.79% and 35.51%, respectively. There were statistically significant differences in the detection rates of depressive symptoms among college students with different sacademic burden (T1, T2, T3, T4), different number of close friends (T1, T2, T3), as well as their father s educational level (T1), mothers educational level (T2, T4), gender (T4), major (T3, T4), education (T2, T3, T4), family residency (T1, T2), and family economic conditions (T1, T2, T4) ( χ 2= 59.68 , 49.38, 16.70, 39.31; 55.35, 26.01, 16.69; 10.22; 14.87, 11.51; 14.90; 27.81, 50.28; 9.75, 7.42, 24.76; 6.06, 4.47 ; 15.88, 14.58, 15.85, P < 0.05 ). After controlling for demographic variables and confounding factors in the generalized estimation equation model, mobile phone dependence ( β =0.11), withdrawal symptoms of mobile phone dependence ( β =0.14), and the physical and mental effects of mobile phone dependence ( β =0.14) were all positively correlated with depressive symptoms ( P <0.01). Further gender analysis showed that depressive symptoms in both boys ( β =0.13, 0.13, 0.18) and girls ( β =0.10, 0.13, 0.13 ) were associated with mobile phone dependence, withdrawal symptoms of mobile phone dependence and the physical and mental effects of mobile phone dependence ( P <0.01). Conclusions Depressive symptoms of college students are positively correlated with mobile phone dependence, and family economic conditions, academic burden and number of close friends are factors that continued to affect depressive symptoms. College students should be guided to pay attention to the impact of excessive use of mobile phones on their physical and mental health, use mobile phones reasonably to reduce the incidence of depressive symptoms among college students.

Objective To conduct an epidemiological survey of common congenital heart disease(CHD)among children in ethnic minority areas in southeastern Guizhou and to explore the influencing factors of delayed medical treatment.Methods From January 2019 to July 2022,18 850 children aged 3 months to 14 years in Qiandongnan Miao and Dong Autonomous Prefecture were selected;105 children with CHD were included in the training set,and they were divided into delayed group(80 cases)and non-delayed group(25 cases)according to whether or not to delay medical treatment.In addition,children with CHD(35 cases)from July 2022 to December 2022 were included in the validation set.The general data of the subjects in the two groups were compared and ana-lyzed.Multivariate logistic regression was performed and risk scoring model was constructed.Results The preva-lence of CHD in 18 850 children was 5.57‰(105/18 850),with the highest prevalence in Liping County,and the lowest in Huangping County.The proportion of children with secondary atrial septal defect was the highest,and that of the aortic valve malformation was the lowest.Among the complex cases of CHD,the proportion of children with single type was the highest,and that of children with three or more types were the lowest.Among children with CHD,the rate of delayed medical treatment was 76.19% (80/105).The median delay in medical treatment was 12 months,with an average of(18.78±4.77)months.Multifactor logistic regression analysis showed that heart murmur(level 2～3),less-educated(primary and secondary school)guardian,family per capita income<2 000 yuan,and frequent drinking of the guardian were independent risk factors for delayed medical treatment(P<0.05),and commercial settlement of medical expenses was independent protective factor(P<0.05).Risk scoring model divided the children into three groups:low risk(≤80 points),medium risk(>80 points and≤134 points)and high(>134 points)risk group.The evaluation of the model show that it was accurate,effective,safe,and reliable.Conclusion The highest prevalence is observed in Liping County.The proportion of children with secondary atrial septal defect and the proportion of children with single type are the highest.Delayed medical treat-ment is found in most of the children with CHD.Cardiac murmur,education background of the guardian,per capita family income,guardian alcohol consumption,and medical expense settlement method are all independent influencing factors for delayed medical treatment.

BACKGROUND:After the treatment of femoral shaft fracture with the intramedullary nail,the third fracture open reduction indications are controversial.Some scholars believe that limited open reduction can achieve anatomical reduction,conducive to fracture healing;but some scholars believe that no open reduction of the third fracture still has a high fracture healing rate. OBJECTIVE:To investigate the effect of the circumference and displacement of the third fragment on fracture healing after intramedullary nailing of femoral shaft fractures with the third fragment. METHODS:A retrospective cohort study was conducted to analyze the clinical data of 142 patients suffered a femoral shaft fracture with a third fragment admitted to the Affiliated Lianyungang Hospital of Xuzhou Medical University from February 2016 to December 2021.The fracture were classified into three types according to the circumference of the third fracture with reference to the diaphyseal circumference at the fracture site:type 1 in 71 cases,type 2 in 52 cases,and type 3 in 19 cases.Referring to the diaphyseal diameter,the fractures were classified into three degrees according to the degree of the third fragment displacement:degree I in 95 cases,degree II in 31 cases,and degree III in 16 cases.All patients were treated with femoral interlocking intramedullary nails,and no intervention was performed for the displaced third fragment during the operation.Postoperative follow-up was performed to compare the fracture healing rate,healing time,and the modified Radiographic Union Scale for Tibia at month 9 after surgery in each group.The effect of third fracture fragment circumference and degree of displacement on fracture healing was assessed. RESULTS AND CONCLUSION:(1)All 142 patients were followed up for at least 12 months,with a mean of(14.7±4.1)months,and the overall healing rate was 73.4%.(2)When the third fragment was displaced by degree I,the healing rate,healing time,and modified Radiographic Union Scale for Tibia score at month 9 were not statistically significant among the three sub-groups of circumference classification.(3)When the third fragments were displaced by degree II or III,the healing rate and healing time were not statistically significant among the three subgroups of circumference classification;the modified Radiographic Union Scale for Tibia score at month 9 in the type 1 group was higher than that in the type 2 and 3 groups(P = 0.017).(4)Logistic regression analysis showed that a greater third fragment displacement and circumference were associated with lower fracture healing rates(P<0.05).(5)These findings indicate that in the treatment of femoral shaft fractures with third fragment by intramedullary nails,when the fracture fragment is displaced to degree I,the circumference size has little effect on fracture healing,and no intervention is required during surgery.When the third fragment is displaced to degree II or III and the circumference of which is type 1,a higher modified Radiographic Union Scale for Tibia score can still be obtained with no intervention of the third fragment.However,when the circumference is of type 2 or type 3,it significantly affects the fracture healing.Consequently,intraoperative intervention to reduce the distance of displacement of the fragment is required to lower the incidence of nonunion.The displacement of the third fracture fragments has a greater impact on fracture healing than their circumference.