With the accelerating trend of population aging, the number of elderly patients with lung cancer continues to rise, and the disease burden is becoming increasingly heavy. The clinical management of these patients faces severe challenges due to their decreased physiological reserve, complex comorbidities, and significant individual heterogeneity. Consequently, under traditional diagnosis and treatment models, doctors often struggle to identify the individualized risks of elderly patients in a timely and comprehensive manner, which can easily lead to decision biases such as undertreatment or overtreatment. In view of this, this study advocates for the establishment of an umbrella decision-making model specifically tailored for elderly lung cancer patients. Grounded in a multidisciplinary team (MDT) platform, this model deeply integrates oncological indicators with the comprehensive geriatric assessment (CGA) system. By holistically considering multidimensional variables including tumor burden, organ function, frailty index, cognitive status, and social support, the model establishes an operational mechanism characterized by "single entry, precise stratification, and targeted selection". Accordingly, patients can be scientifically triaged into distinct intervention tiers, such as active surveillance, minimally invasive surgery, drug therapy, radiotherapy, and best supportive care, thereby achieving real-time alignment between treatment intensity and patient fitness. This article elaborates on the construction logic and key operational procedures of this novel decision-making framework, aiming to guide clinical practice beyond the limitations of a tumor-centric perspective toward a holistic, dynamic, whole-course management strategy. This transition seeks to ensure optimal quality of life and clinical net benefit for elderly patients alongside survival prolongation.

Objective:To evaluate the value of a combined model based on primary lesion 18F-fluorodeoxyglucose ( 18F-FDG) PET/CT radiomics for predicting lymph node metastasis in non-small cell lung cancer (NSCLC) . Methods:A retrospective analysis was conducted on the clinical data of 203 NSCLC patients who underwent pre-treatment PET/CT imaging at Nanjing Drum Tower Hospital from June 2013 to July 2023. Patients were randomly assigned to the training set ( n=142) and the validation set ( n=61) at a ratio of 7∶3. A predictive model was developed in the training set, and its predictive performance and clinical application value were assessed in both the training and validation sets. Traditional PET/CT parameters and PET/CT radiomics features of the primary lesion were obtained by 3D-slicer software. Least absolute shrinkage and selection operator (LASSO), random forest, and extreme gradient boosting were performed to extract features. Support vector machine was used to construct a radiomics score (Radscore). Univariate and multivariate logistic regression analysis was used to predict the influencing factors of lymph node metastasis in NSCLC patients and to establish models. Predictive performance of the models was evaluated by receiver operator characteristic (ROC) curves and clinical application value was assessed by calibration curves and decision curve analysis (DCA) . Results:Among 203 NSCLC patients, 116 had lymph node metastasis, with 64 cases in the training set and 52 cases in the validation set. Three complementary classical machine learning methods were used for feature screening, and finally 10 radiomics features were obtained. The optimal threshold for Radscore-PET was 0.43 and the optimal threshold for Radscore-CT was 0.39. Univariate analysis showed that, sex ( OR=0.48, 95% CI: 0.24-0.95, P=0.036), tumor marker levels ( OR=3.81, 95% CI: 1.84-7.91, P<0.001), long diameter of tumor ( OR=2.56, 95% CI: 1.27-5.16, P=0.009), short diameter of tumor ( OR=3.73, 95% CI: 1.75-7.92, P=0.001), vacuolar sign ( OR=0.32, 95% CI: 0.12-0.86, P=0.024), ring-like metabolism ( OR=3.67, 95% CI: 1.33-10.13, P=0.012), maximum standardized uptake value (SUV max) ( OR=6.57, 95% CI: 3.03-14.25, P<0.001), metabolic tumor volume (MTV) ( OR=2.91, 95% CI: 1.43-5.92, P=0.003), total lesion glycolysis (TLG) ( OR=4.23, 95% CI: 2.08-8.59, P<0.001), Radscore-PET ( OR=21.93, 95% CI: 9.04-53.20, P<0.001) and Radscore-CT ( OR=13.72, 95% CI: 6.12-30.76, P<0.001) were all influencing factors for predicting lymph node metastasis in NSCLC patients. Multivariate analysis showed that, tumor marker levels ( OR=2.55, 95% CI: 1.11-5.90, P=0.028), vacuolar sign ( OR=0.26, 95% CI: 0.08-0.83, P=0.023), SUV max ( OR=5.94, 95% CI: 1.99-17.75, P=0.001), Radscore-PET ( OR=25.51, 95% CI: 5.92-110.22, P<0.001), and Radscore-CT ( OR=8.68, 95% CI: 2.73-27.61, P<0.001) were independent influencing factors for predicting lymph node metastasis in patients with NSCLC. Based on the above independent influencing factors, models were constructed: the traditional model (tumor marker levels, vacuolar sign, SUV max), the PET model (SUV max, Radscore-PET), the CT model (vacuolar sign, Radscore-CT), and the combined model (tumor marker levels, vacuolar sign, SUV max, Radscore-PET, Radscore-CT). ROC curve analysis showed that, the area under curve (AUC) of the traditional, PET, CT, and combined models in the training set were 0.75 (95% CI: 0.67-0.82), 0.90 (95% CI: 0.84-0.95), 0.85 (95% CI: 0.78-0.90), and 0.94 (95% CI: 0.88-0.97), respectively. The predictive value of the combined model was higher than that of the traditional model ( Z=5.01, P<0.001), the PET model ( Z=1.99, P=0.047), and the CT model ( Z=3.25, P=0.001). In the validation set, the AUCs for the traditional model, PET model, CT model, and combined model were 0.65 (95% CI: 0.52-0.77), 0.86 (95% CI: 0.74-0.93), 0.85 (95% CI: 0.73-0.93), and 0.90 (95% CI: 0.80-0.96), respectively. The predictive value of the combined model was superior to that of the traditional model ( Z=3.23, P=0.001). The sensitivity and specificity of the combined model in the training set were 84.37% and 91.03%, while in the validation set, the sensitivity and specificity were 82.61% and 94.74%, respectively. Calibration curves showed a good agreement between the predicted and actual probabilities in both the training and validation sets. DCA showed that the combined models had good discriminative ability in both the training and validation sets. Conclusions:Tumor marker levels, vacuolar sign, SUV max, Radscore-PET, and Radscore-CT are all independent influencing factors for predicting lymph node metastasis in patients with NSCLC. The combined model based on these factors demonstrates excellent predictive performance and clinical application value for predicting lymph node metastasis in NSCLC.

Objective To review the damage outcomes of patients in medical damage events and to analyze the influencing factors of medical causality determination in medical damage events in Tianjin city,with a view to reducing the occurrence of medical damage events by avoiding risk factors and standardizing the diagnostic and therapeutic activities of medical personnel.Methods 316 cases of medical damage events handled by the Medical Damage Identification Office of Tianjin Medical Association from 2017 to 2023 were selected as the research object,and descriptive analysis was used to summarize the basic situation of medical damage events,and the medical fault factors affecting the magnitude of the cause force of the medical side of medical damage events were explored through single-factor analysis and the construction of multiomal logistic regression models.Results Medical malpractice(OR=3.140)and incomplete preoperative assessment(OR=6.008)are factors that influence the determination of the liability of the medical institution in medical malpractice events(P＜0.05),and all three regression coefficients were greater than 0,all three were positively correlated with the probability that the medical cause power was judged to be greater than or equal to the equivalent cause.Conclusion The number of medical damage incidents in Tianjin shows a fluctuating downward trend.In Tianjin,the number of medical injury incidents showed a fluctuating downward trend,and the number of medical injury incidents in tertiary public general hospitals was the largest.Therefore,medical institutions should establish and improve multidisciplinary diagnosis and treatment mode to improve the level of diagnosis and treatment.Strengthen preoperative evaluation to avoid surgical risk.

Objective:To investigate the value of a cross-combination machine learning approach in constructing a PET radiomics score (RadScore) for predicting early treatment response and prognosis in patients with primary gastrointestinal diffuse large B-cell lymphoma (PGI-DLBCL).Methods:This retrospective cohort study was conducted on 108 patients (59 males and 49 females, age (55.6±12.1) years) diagnosed with PGI-DLBCL between November 2016 and December 2021 at Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University ( n=85) and West China Hospital, Sichuan University ( n=23). Patients were divided into a training set ( n=86) and a validation set ( n=22) with the ratio of 8∶2 using stratified random sampling method. Seven machine learning models were employed to generate 49 feature selection-classification candidates, and the optimal candidate was selected to construct the RadScore, with five-fold cross-validation applied to determine the best-performing model. Logistic regression analysis was performed to identify risk factors for early treatment response, and a radiomics nomogram was developed by integrating RadScore with clinical predictors. Survival results between different groups of RadScore was compared by log-rank test. Results:Nineteen predictive features were selected from 111 radiomic features to construct the RadScore. In the training set, lactate dehydrogenase (LDH) (odds ratio ( OR)=3.53, 95% CI: 1.21-10.31, P=0.021), intestinal involvement ( OR=3.04, 95% CI: 1.04-8.88, P=0.042), total lesion glycolysis (TLG; OR=6.73, 95% CI: 2.23-20.29, P<0.001) and RadScore ( OR=15.11, 95% CI: 3.95-57.80, P<0.001) were identified as independent risk factors for predicting early treatment response. The combined model integrating RadScore, LDH, intestinal involvement, and TLG demonstrated good discriminatory ability for early treatment response (AUC=0.860 in the training set; AUC=0.902 in the validation set). Significant differences were observed in progression-free survival (PFS) and overall survival (OS) between different RadScore groups ( χ2 values: 13.92 and 8.56, both P<0.01). Conclusions:The machine learning-based RadScore may effectively predict survival outcomes in patients with PGI-DLBCL. The combined model integrating RadScore, clinical factors, and metabolic indicators can predict early treatment response in PGI-DLBCL patients.

Objective To review the damage outcomes of patients in medical damage events and to analyze the influencing factors of medical causality determination in medical damage events in Tianjin city,with a view to reducing the occurrence of medical damage events by avoiding risk factors and standardizing the diagnostic and therapeutic activities of medical personnel.Methods 316 cases of medical damage events handled by the Medical Damage Identification Office of Tianjin Medical Association from 2017 to 2023 were selected as the research object,and descriptive analysis was used to summarize the basic situation of medical damage events,and the medical fault factors affecting the magnitude of the cause force of the medical side of medical damage events were explored through single-factor analysis and the construction of multiomal logistic regression models.Results Medical malpractice(OR=3.140)and incomplete preoperative assessment(OR=6.008)are factors that influence the determination of the liability of the medical institution in medical malpractice events(P＜0.05),and all three regression coefficients were greater than 0,all three were positively correlated with the probability that the medical cause power was judged to be greater than or equal to the equivalent cause.Conclusion The number of medical damage incidents in Tianjin shows a fluctuating downward trend.In Tianjin,the number of medical injury incidents showed a fluctuating downward trend,and the number of medical injury incidents in tertiary public general hospitals was the largest.Therefore,medical institutions should establish and improve multidisciplinary diagnosis and treatment mode to improve the level of diagnosis and treatment.Strengthen preoperative evaluation to avoid surgical risk.

Objective:To investigate the value of a cross-combination machine learning approach in constructing a PET radiomics score (RadScore) for predicting early treatment response and prognosis in patients with primary gastrointestinal diffuse large B-cell lymphoma (PGI-DLBCL).Methods:This retrospective cohort study was conducted on 108 patients (59 males and 49 females, age (55.6±12.1) years) diagnosed with PGI-DLBCL between November 2016 and December 2021 at Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University ( n=85) and West China Hospital, Sichuan University ( n=23). Patients were divided into a training set ( n=86) and a validation set ( n=22) with the ratio of 8∶2 using stratified random sampling method. Seven machine learning models were employed to generate 49 feature selection-classification candidates, and the optimal candidate was selected to construct the RadScore, with five-fold cross-validation applied to determine the best-performing model. Logistic regression analysis was performed to identify risk factors for early treatment response, and a radiomics nomogram was developed by integrating RadScore with clinical predictors. Survival results between different groups of RadScore was compared by log-rank test. Results:Nineteen predictive features were selected from 111 radiomic features to construct the RadScore. In the training set, lactate dehydrogenase (LDH) (odds ratio ( OR)=3.53, 95% CI: 1.21-10.31, P=0.021), intestinal involvement ( OR=3.04, 95% CI: 1.04-8.88, P=0.042), total lesion glycolysis (TLG; OR=6.73, 95% CI: 2.23-20.29, P<0.001) and RadScore ( OR=15.11, 95% CI: 3.95-57.80, P<0.001) were identified as independent risk factors for predicting early treatment response. The combined model integrating RadScore, LDH, intestinal involvement, and TLG demonstrated good discriminatory ability for early treatment response (AUC=0.860 in the training set; AUC=0.902 in the validation set). Significant differences were observed in progression-free survival (PFS) and overall survival (OS) between different RadScore groups ( χ2 values: 13.92 and 8.56, both P<0.01). Conclusions:The machine learning-based RadScore may effectively predict survival outcomes in patients with PGI-DLBCL. The combined model integrating RadScore, clinical factors, and metabolic indicators can predict early treatment response in PGI-DLBCL patients.

OBJECTIVE To explore the effects of levosimendan on cardiac function， hemodynamics and prognosis of patients with septic shock complicated with myocardial depression， and evaluate the safety of levosimendan. METHODS Patients with septic shock complicated with myocardial depression who were admitted to the Department of Critical Care Medicine of Chongqing University Three Gorges Hospital from April 2021 to August 2023， underwent adequate fluid resuscitation， had a mean arterial pressure （MAP） ≥65 mmHg， and received pulse indicator continuous cardiac output （PiCCO） monitoring were enrolled. The patients were randomly divided into dobutamine group and levosimendan group according to a random number table， with 20 patients in each group. Both groups received intravenous infusion of Norepinephrine bitartrate injection at a dose of 0.1-2.0 μg/（kg·min）. On this basis， the dobutamine group additionally received intravenous infusion of Dobutamine hydrochloride injection at a dose of 5- 10 μg/（kg·min） for 3 to 7 days， while the levosimendan group additionally received intravenous infusion of Levosimendan injection at a dose of 0.1-0.2 μg/（kg·min） for 24 hours. Heart rate （HR） and hemodynamic parameters [systolic blood pressure， diastolic blood pressure， MAP， central venous pressure （CVP）]， PiCCO monitoring parameters [cardiac function index （CFI）， cardiac index （CI）， stroke volume index （SVI）， extravascular lung water index， global end-diastolic volume index， pulmonary vascular permeability index （PVPI）， global ejection fraction （GEF）， systemic vascular resistance index， left ventricular contractility index]， and prognosis indicators [death within 3 days after administration， mechanical ventilation time，intensive care unit （ICU） stay time， 28-day mortality rate] were compared between the two groups before treatment and at 24 and 72 hours after treatment. Adverse reactions were E-mail：recorded for both groups. RESULTS Compared with before treatment in the same group， CFI， CI and GEF at 24 hours after treatment， CI and GEF at 72 hours after treatment in the dobutamine group， as well as SVI at 24 hours after treatment and SVI and GEF at 72 hours after treatment in the levosimendan group were significantly increased； PVPI at 72 hours after treatment in the dobutamine group was significantly decreased （P＜0.05）. Compared with the dobutamine group during the same period， patients in the levosimendan group had significantly lower HR and significantly higher CVP at 24 hours after treatment （P＜0.05）. Within 3 days after administration， there were no deaths in either group； there were no statistically significant differences in mechanical ventilation time， ICU stay time， 28-day mortality rate， or the incidence of adverse reactions between the two groups （P＞0.05）. CONCLUSIONS For patients with septic shock complicated with myocardial depression who have undergone adequate fluid resuscitation and have a MAP of ≥65 mmHg， levosimendan is comparable to dobutamine in improving cardiac function and hemodynamic parameters， without affecting patients’ prognosis or increasing the risk of adverse reactions such as hypotension.

Objective:To explore the effects of embodied emotion priming on attentional bias of individuals with depression tendency.Methods:From June to December 2018, a total of 91 college students with depression tendency were recruited to participate in the experiment.A 3(embodied emotion priming: positive priming, negative priming and no priming) × 2 (emotional face: happy and sad) mixed design was adopted to measure the attentional bias of individuals with depression tendency using the dot probe paradigm. SPSS 22.0 statistical software was used for repeated measurement analysis of variance.Results:In terms of attentional bias, the interaction effect between embodied emotion priming types and emotional faces was significant ( F(2, 88)=5.97, P=0.004, ηp2=0.119). Further simple effect analysis showed that, under the happy-face condition, participants' attentional bias reaction time(△RT) was significantly higher when primed with embodied positive emotion than those primed with embodied negative emotion((14.30±18.23)ms, (-6.53±38.17)ms, P<0.05). The participants' attentional bias △RT was significantly lower when primed with embodied negative emotion than participants with no priming ((-6.53±38.17)ms, (9.16±30.62)ms, P<0.05). Under the sad-face condition, the participants' attentional bias △RT was significantly higher when primed with embodied negative emotion((28.22±35.33)ms) than participants primed with embodied positive emotion((11.71±29.24)ms, P<0.05) and no priming ((7.63±30.60)ms, P<0.05). Conclusion:Embodied emotion priming can affect the attentional bias of individuals with depression tendency.

Objective:To explore the prognostic value of 18F-FDG PET-based radiomics features by machine learning in older patients(≥60 years) with diffuse large B-cell lymphoma (DLBCL). Methods:A total of 166 older patients (88 males, 78 females, age: 60-93 years) with DLBCL who underwent pre-therapy 18F-FDG PET/CT from March 2011 to November 2019 were enrolled in the retrospective study. There were 115 patients in training cohort and 51 patients in validation cohort. The lesions in PET images were manually drawn and the obtained radiomics features from patients in training cohort were selected by the least absolute shrinkage and selection operator (LASSO), random forest (RF), and extreme gradient boosting (Xgboost), and then classified by support vector machine (SVM) to build radiomics signatures (RS) for predicting overall survival (OS). A multi-parameter model was constructed by using Cox proportional hazard model and assessed by concordance index (C-index). Results:A total of 1 421 PET radiomics features were extracted and 10 features were selected to build RS. The univariate Cox regression analysis showed that RS was a predictor of OS (hazard ratio ( HR)=5.685, 95% CI: 2.955-10.939; P<0.001). The multi-parameter model that incorporated RS, metabolic metrics, and clinical risk factors, exhibited significant prognostic superiority over the clinical model, PET-based model, and the National Comprehensive Cancer Network International Prognostic Index (NCCN-IPI) in terms of OS (training cohort: C-index: 0.752 vs 0.737 vs 0.739 vs 0.688; validation cohort: C-index: 0.845 vs 0.798 vs 0.844 vs 0.775). Conclusions:RS can be used as a survival predictor for older patients(≥60 years) with DLBCL. Furthermore, the multi-parameter model incorporating RS is able to successfully predict prognosis.

Glycosite-specific antibody‒drug conjugatess (gsADCs), harnessing Asn297 N-glycan of IgG Fc as the conjugation site for drug payloads, usually require multi-step glycoengineering with two or more enzymes, which limits the substrate diversification and complicates the preparation process. Herein, we report a series of novel disaccharide-based substrates, which reprogram the IgG glycoengineering to one-step synthesis of gsADCs, catalyzed by an endo-N-acetylglucosaminidase (ENGase) of Endo-S2. IgG glycoengineering via ENGases usually has two steps: deglycosylation by wild-type (WT) ENGases and transglycosylation by mutated ENGases. But in the current method, we have found that disaccharide LacNAc oxazoline can be efficiently assembled onto IgG by WT Endo-S2 without hydrolysis of the product, which enables the one-step glycoengineering directly from native antibodies. Further studies on substrate specificity revealed that this approach has excellent tolerance on various modification of 6-Gal motif of LacNAc. Within 1 h, one-step synthesis of gsADC was achieved using the LacNAc-toxin substrates including structures free of bioorthogonal groups. These gsADCs demonstrated good homogeneity, buffer stability, in vitro and in vivo anti-tumor activity. This work presents a novel strategy using LacNAc-based substrates to reprogram the multi-step IgG glycoengineering to a one-step manner for highly efficient synthesis of gsADCs.