Sepsis, a life-threatening syndrome characterized by acute organ dysfunction due to a dysregulated host response to infection, remains a significant global health challenge. Despite the identification of numerous biomarkers, their clinical translation remains challenging due to various limitations. In this article, we review the historical evolution of sepsis biomarker research, tracing the progression from early inflammatory marker research to the advent of multi-omics technologies and artificial intelligence-driven precision medicine. We summarize the current clinical applications of biomarkers in early diagnosis, pathogen identification, and precision treatment. Furthermore, we discuss key barriers including the complex pathophysiology of sepsis, insufficient real-world studies, and the lack of standardized evaluation frameworks in advancing biomarker research and utilization. Looking ahead, leveraging advanced approaches such as dynamic monitoring and precision stratification, combined with multidisciplinary collaboration, is expected to facilitate biomarker clinical translation and support the development of precision medicine for sepsis.

Objective:To evaluate the clinical outcomes of endoscopic-assisted polyether ether ketone (PEEK) patient-specific implant (PSI) revision for over-resected mandibles caused by the mandibular angle osteotomy.Methods:A retrospective analysis was conducted on 24 patients [8 males, 16 females, aged 19-57 (32.5±9.5) years] with 39 over-resected mandibles that underwent PEEK-PSI mandibular angle revision surgery at the Affiliated Friendship Plastic Surgery Hospital of Nanjing Medical University from January 2019 to December 2023. Preoperative cone-beam computed tomography (CBCT) data were used to design and fabricate customized PEEK PSIs based on individual anatomical requirements. An intraoral incision approach with endoscopic assistance was employed to meticulously dissect soft tissue attachment around the angle region, followed by the implantation of a customized PEEK PSI. Postoperative CBCT scans were performed for 3D reconstruction, with root mean square error (RMSE) and maximum deviation (MaxD) as accuracy metrics. Patients′ satisfaction was assessed preoperatively and ≥6 months postoperatively using the face questionnaire (FACE-Q) scores, which included overall facial appearance, lower face and jawline, appearance distress, psychological health and social function.Results:All 24 patients achieved satisfactory recovery with primary healing of intraoral incisions. No complications such as infection, nerve injury, or implant rejection occurred during follow-up period. Patients′ facial appearance and jaw line contouring were significantly improved. Fine anatomical fitting between PEEK-PSI and defect areas was observed: RMSE ranged from 0.117 to 0.315 mm, and MaxD was (5.485±1.300) mm. FACE-Q scores demonstrated significant improvements after surgery in overall facial appearance [(49.8±5.4) vs (65.0±5.3) scores], lower face and jawline [(42.5±5.3) vs (56.1±4.6) scores], appearance distress [(60.0±6.9) vs (70.6±6.5) scores], psychological health [(62.0±5.0) vs (70.8±5.3) scores], and social function [(60.3±4.3) vs (69.3±5.8) scores] (all P<0.001). Conclusion:Endoscopic-assisted PEEK-PSI revision for over-resected mandibles following mandibular angle osteotomy exhibits high surgical precision and safety, effectively restoring mandibular contour and significantly enhancing patients′ satisfaction.

Objective:To develop a preoperative differentiation model for colorectal mucinous adenocarcinoma and non-mucinous adenocarcinoma using a combination of contrast-enhanced CT radiomics and deep learning methods.Methods:This is a retrospective cohort study. Clinical data of colorectal cancer patients confirmed by postoperative pathological examination were retrospectively collected from January 2016 to December 2023 at Shanghai General Hospital Affiliated to Shanghai Jiao Tong University School of Medicine (Center 1, n=220) and the First Affiliated Hospital of Bengbu Medical University (Center 2, n=51). Among them, there were 108 patients diagnosed with mucinous adenocarcinoma, including 55 males and 53 females, with an age of (68.4±12.2) years (range: 38 to 96 years); and 163 patients diagnosed with non-mucinous adenocarcinoma, including 96 males and 67 females, with an age of (67.9±11.0) years (range: 43 to 94 years). The cases from Center 1 were divided into a training set ( n=156) and an internal validation set ( n=64) using stratified random sampling in a 7∶3 ratio, and the cases from Center 2 were used as an independent external validation set ( n=51). Three-dimensional tumor volume of interest was manually segmented on venous-phase contrast-enhanced CT images. Radiomics features were extracted using PyRadiomics, and deep learning features were extracted using the ResNet-18 network. The two sets of features were then combined to form a joint feature set. The consistency of manual segmentation was assessed using the intraclass correlation coefficient. Feature dimensionality reduction was performed using the Mann-Whitney U test and the least absolute shrinkage and selection operator regression. Six machine learning algorithms were used to construct models based on radiomics features, deep learning features, and combined features, including support vector machine, logistic regression, random forest, extreme gradient boosting, k-nearest neighbors, and decision tree. The discriminative performance of each model was evaluated using receiver operating characteristic curves, the area under the curve (AUC), DeLong test, and decision curve analysis. Results:After feature selection, 22 features with the most discriminative value were finally retained, among which 12 were traditional radiomics features and 10 were deep learning features. In the internal validation set, the Random Forest algorithm based on the combined features model achieved the best performance (AUC=0.938, 95% CI: 0.875 to 0.984), which was superior to the single-modality radiomics feature model (AUC=0.817, 95% CI: 0.702 to 0.913, P=0.048) and the deep learning feature model (AUC=0.832, 95% CI: 0.727 to 0.926, P=0.087); in the independent external validation set, the Random Forest algorithm with the combined features model maintained the highest discriminative performance (AUC=0.891, 95% CI: 0.791 to 0.969), which was superior to the single-modality radiomics feature model (AUC=0.770, 95% CI: 0.636 to 0.890, P=0.045) and the deep learning feature model (AUC=0.799, 95% CI: 0.652 to 0.911, P=0.169). Conclusion:The combined model based on radiomics and deep learning features from venous-phase enhanced CT demonstrates good performance in the preoperative differentiation of colorectal mucinous from non-mucinous adenocarcinoma.

Objective To investigate the serum amino acid levels in patients with acute schizophrenia(SCZ)and their predictive effect on the improvement of cognitive function after treatment,so as to provide new insights into the clinical intervention of cognitive impairment in SCZ patients.Methods A total of 66 patients with acute SCZ were enrolled(case group-baseline period).Among them,36 cases completed the follow-up after 3 months of standardized treatment(case group-follow-up period);52 healthy controls(HCs)were included.The MATRICS Consensus Cognitive Battery(MCCB)was used to assess the cognitive function of all the participants.Liquid chromatography-tandem mass spectrometry(LC-MS)was employed to detect the concentrations of 18 amino acids in fasting serum of the case group-baseline period and the control group.Independent samples t-test was used to compare serum amino acid levels and cognitive function between the case group-baseline period and the control group.Paired t-test was used to compare the differences in cognitive function between the baseline period and the follow-up period of the case group.Spearman correlation analysis and multivariate linear regression model were used to investigate the correlation between serum amino acid levels at baseline in the case group and the improvement of cognitive function after 3 months of treatment.Results Compared with the control group,the cognitive function of SCZ patients in multiple dimensions at baseline was significantly reduced(P＜0.05).After treatment,the scores of Trail Making Test(TMT),Brief Assessment of Cognition in Schizophrenia:Symbol Coding(BACS),Wechsler Memory Scale-Ⅲ(WMS),and Brief Visuospatial Memory Test-Revised(BVMT)in patients were significantly improved(all P＜0.05).In addition,the levels of proline,methionine,histidine,phenylalanine,arginine,tyrosine,aspartic acid,tryptophan,lysine,and glutamic acid were significantly lower in the case group at baseline than in the control group(all P＜0.05).Among them,the baseline tyrosine level had a significant predictive value for the improvement of TMT(R2=0.136,P=0.029),Neuropsychological Assessment Battery(NAB)(R2=0.339,P＜0.001),and Mayer-Salovey-Caruso Emotional Intelligence Test(MSCEIT)test(R2=0.165,P=0.015).The baseline arginine level had a significant predictive value for the improvement rate of Fluency test(R2=0.113,P=0.048).Conclusion There is a decrease in various amino acid levels in patients with SCZ,and some amino acids can effectively predict the improvement of cognitive function after treatment.

Objective To investigate the serum amino acid levels in patients with acute schizophrenia(SCZ)and their predictive effect on the improvement of cognitive function after treatment,so as to provide new insights into the clinical intervention of cognitive impairment in SCZ patients.Methods A total of 66 patients with acute SCZ were enrolled(case group-baseline period).Among them,36 cases completed the follow-up after 3 months of standardized treatment(case group-follow-up period);52 healthy controls(HCs)were included.The MATRICS Consensus Cognitive Battery(MCCB)was used to assess the cognitive function of all the participants.Liquid chromatography-tandem mass spectrometry(LC-MS)was employed to detect the concentrations of 18 amino acids in fasting serum of the case group-baseline period and the control group.Independent samples t-test was used to compare serum amino acid levels and cognitive function between the case group-baseline period and the control group.Paired t-test was used to compare the differences in cognitive function between the baseline period and the follow-up period of the case group.Spearman correlation analysis and multivariate linear regression model were used to investigate the correlation between serum amino acid levels at baseline in the case group and the improvement of cognitive function after 3 months of treatment.Results Compared with the control group,the cognitive function of SCZ patients in multiple dimensions at baseline was significantly reduced(P＜0.05).After treatment,the scores of Trail Making Test(TMT),Brief Assessment of Cognition in Schizophrenia:Symbol Coding(BACS),Wechsler Memory Scale-Ⅲ(WMS),and Brief Visuospatial Memory Test-Revised(BVMT)in patients were significantly improved(all P＜0.05).In addition,the levels of proline,methionine,histidine,phenylalanine,arginine,tyrosine,aspartic acid,tryptophan,lysine,and glutamic acid were significantly lower in the case group at baseline than in the control group(all P＜0.05).Among them,the baseline tyrosine level had a significant predictive value for the improvement of TMT(R2=0.136,P=0.029),Neuropsychological Assessment Battery(NAB)(R2=0.339,P＜0.001),and Mayer-Salovey-Caruso Emotional Intelligence Test(MSCEIT)test(R2=0.165,P=0.015).The baseline arginine level had a significant predictive value for the improvement rate of Fluency test(R2=0.113,P=0.048).Conclusion There is a decrease in various amino acid levels in patients with SCZ,and some amino acids can effectively predict the improvement of cognitive function after treatment.

Sepsis, a life-threatening syndrome characterized by acute organ dysfunction due to a dysregulated host response to infection, remains a significant global health challenge. Despite the identification of numerous biomarkers, their clinical translation remains challenging due to various limitations. In this article, we review the historical evolution of sepsis biomarker research, tracing the progression from early inflammatory marker research to the advent of multi-omics technologies and artificial intelligence-driven precision medicine. We summarize the current clinical applications of biomarkers in early diagnosis, pathogen identification, and precision treatment. Furthermore, we discuss key barriers including the complex pathophysiology of sepsis, insufficient real-world studies, and the lack of standardized evaluation frameworks in advancing biomarker research and utilization. Looking ahead, leveraging advanced approaches such as dynamic monitoring and precision stratification, combined with multidisciplinary collaboration, is expected to facilitate biomarker clinical translation and support the development of precision medicine for sepsis.

Objective:To evaluate the clinical outcomes of endoscopic-assisted polyether ether ketone (PEEK) patient-specific implant (PSI) revision for over-resected mandibles caused by the mandibular angle osteotomy.Methods:A retrospective analysis was conducted on 24 patients [8 males, 16 females, aged 19-57 (32.5±9.5) years] with 39 over-resected mandibles that underwent PEEK-PSI mandibular angle revision surgery at the Affiliated Friendship Plastic Surgery Hospital of Nanjing Medical University from January 2019 to December 2023. Preoperative cone-beam computed tomography (CBCT) data were used to design and fabricate customized PEEK PSIs based on individual anatomical requirements. An intraoral incision approach with endoscopic assistance was employed to meticulously dissect soft tissue attachment around the angle region, followed by the implantation of a customized PEEK PSI. Postoperative CBCT scans were performed for 3D reconstruction, with root mean square error (RMSE) and maximum deviation (MaxD) as accuracy metrics. Patients′ satisfaction was assessed preoperatively and ≥6 months postoperatively using the face questionnaire (FACE-Q) scores, which included overall facial appearance, lower face and jawline, appearance distress, psychological health and social function.Results:All 24 patients achieved satisfactory recovery with primary healing of intraoral incisions. No complications such as infection, nerve injury, or implant rejection occurred during follow-up period. Patients′ facial appearance and jaw line contouring were significantly improved. Fine anatomical fitting between PEEK-PSI and defect areas was observed: RMSE ranged from 0.117 to 0.315 mm, and MaxD was (5.485±1.300) mm. FACE-Q scores demonstrated significant improvements after surgery in overall facial appearance [(49.8±5.4) vs (65.0±5.3) scores], lower face and jawline [(42.5±5.3) vs (56.1±4.6) scores], appearance distress [(60.0±6.9) vs (70.6±6.5) scores], psychological health [(62.0±5.0) vs (70.8±5.3) scores], and social function [(60.3±4.3) vs (69.3±5.8) scores] (all P<0.001). Conclusion:Endoscopic-assisted PEEK-PSI revision for over-resected mandibles following mandibular angle osteotomy exhibits high surgical precision and safety, effectively restoring mandibular contour and significantly enhancing patients′ satisfaction.

Objective:To develop a preoperative differentiation model for colorectal mucinous adenocarcinoma and non-mucinous adenocarcinoma using a combination of contrast-enhanced CT radiomics and deep learning methods.Methods:This is a retrospective cohort study. Clinical data of colorectal cancer patients confirmed by postoperative pathological examination were retrospectively collected from January 2016 to December 2023 at Shanghai General Hospital Affiliated to Shanghai Jiao Tong University School of Medicine (Center 1, n=220) and the First Affiliated Hospital of Bengbu Medical University (Center 2, n=51). Among them, there were 108 patients diagnosed with mucinous adenocarcinoma, including 55 males and 53 females, with an age of (68.4±12.2) years (range: 38 to 96 years); and 163 patients diagnosed with non-mucinous adenocarcinoma, including 96 males and 67 females, with an age of (67.9±11.0) years (range: 43 to 94 years). The cases from Center 1 were divided into a training set ( n=156) and an internal validation set ( n=64) using stratified random sampling in a 7∶3 ratio, and the cases from Center 2 were used as an independent external validation set ( n=51). Three-dimensional tumor volume of interest was manually segmented on venous-phase contrast-enhanced CT images. Radiomics features were extracted using PyRadiomics, and deep learning features were extracted using the ResNet-18 network. The two sets of features were then combined to form a joint feature set. The consistency of manual segmentation was assessed using the intraclass correlation coefficient. Feature dimensionality reduction was performed using the Mann-Whitney U test and the least absolute shrinkage and selection operator regression. Six machine learning algorithms were used to construct models based on radiomics features, deep learning features, and combined features, including support vector machine, logistic regression, random forest, extreme gradient boosting, k-nearest neighbors, and decision tree. The discriminative performance of each model was evaluated using receiver operating characteristic curves, the area under the curve (AUC), DeLong test, and decision curve analysis. Results:After feature selection, 22 features with the most discriminative value were finally retained, among which 12 were traditional radiomics features and 10 were deep learning features. In the internal validation set, the Random Forest algorithm based on the combined features model achieved the best performance (AUC=0.938, 95% CI: 0.875 to 0.984), which was superior to the single-modality radiomics feature model (AUC=0.817, 95% CI: 0.702 to 0.913, P=0.048) and the deep learning feature model (AUC=0.832, 95% CI: 0.727 to 0.926, P=0.087); in the independent external validation set, the Random Forest algorithm with the combined features model maintained the highest discriminative performance (AUC=0.891, 95% CI: 0.791 to 0.969), which was superior to the single-modality radiomics feature model (AUC=0.770, 95% CI: 0.636 to 0.890, P=0.045) and the deep learning feature model (AUC=0.799, 95% CI: 0.652 to 0.911, P=0.169). Conclusion:The combined model based on radiomics and deep learning features from venous-phase enhanced CT demonstrates good performance in the preoperative differentiation of colorectal mucinous from non-mucinous adenocarcinoma.

As artificial intelligence technology rapidly advances, its deployment within the medical sector presents substantial ethical challenges. Consequently, it becomes crucial to create a standardized, transparent, and secure framework for processing medical data. This includes setting the ethical boundaries for medical artificial intelligence and safeguarding both patient rights and data integrity. This consensus governs every facet of medical data handling through artificial intelligence, encompassing data gathering, processing, storage, transmission, utilization, and sharing. Its purpose is to ensure the management of medical data adheres to ethical standards and legal requirements, while safeguarding patient privacy and data security. Concurrently, the principles of compliance with the law, patient privacy respect, patient interest protection, and safety and reliability are underscored. Key issues such as informed consent, data usage, intellectual property protection, conflict of interest, and benefit sharing are examined in depth. The enactment of this expert consensus is intended to foster the profound integration and sustainable advancement of artificial intelligence within the medical domain, while simultaneously ensuring that artificial intelligence adheres strictly to the relevant ethical norms and legal frameworks during the processing of medical data.
Artificial Intelligence/legislation & jurisprudence* ; Humans ; Consensus ; Computer Security/standards* ; Confidentiality/ethics* ; Informed Consent/ethics*

Objective:To evaluate the effectiveness of correcting breast ptosis using the rotational filling of an inferolaterally pedicled tongue-shaped parenchymal flap via a double periareolar incision.Methods:A retrospective study was conducted on 34 female patients (68 breasts) with breast ptosis treated at the Department of Plastic Surgery at the Affiliated Friendship Plastic Surgery Hospital of Nanjing Medical University from January 2016 to June 2023. The patients＇ ages ranged from 23 to 53 years, with an average of (37.4±7.9) years. All patients underwent correction of breast ptosis using an inferolaterally pedicled tongue-shaped parenchymal flap via a double periareolar incision. Pre-operative and post-operative measurements were taken to assess the corrective outcomes, including mammary base width (MBW), areola diameter (AD), nipple-to-nipple distance (NN), suprasternal notch-to-nipple distance (SSN-N), nipple-to-inframammary fold distance (N-IMF), and thoracic circumference at the nipple level (TN). Patients＇ satisfaction was assessed using the BREAST-questionnaire (BREAST-Q) module.Results:Postoperative first-stage wound healing was achieved in all 34 patients without complications such as hematoma, infection, or nipple necrosis. The follow-up period ranged from 6 to 60 months, with a median of 12 months. Early postoperative observations showed periareolar folds, which disappeared within 3 to 6 months in 31 cases, while mild periareolar folds, persisted for up to 12 months in 3 cases. Four patients reported reduced nipple sensation postoperatively. Measurements showed a statistically significant increase in TN and a reduction in NN, SSN-N, AD, N-IMF, and MBW compared to preoperative values (all P<0.001). BREAST-Q scores indicated significant improvements in satisfaction with breasts, psychosocial well-being and physical well-being, with preoperative scores of (21.6±8.9), (42.4±11.9) and (49.7±12.9) scores increasing to (82.3±8.5), (81.9±8.1) and (81.2±8.3) scores, respectively (all P<0.001). Conclusions:The double periareolar incision technique with rotational filling of an inferolaterally pedicled tongue-shaped glandular flap is a safe and effective method for correcting breast ptosis.