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 investigate the relationship between prolactin and mild cognitive impairment(MCI)in postmenopausal women with type 2 diabetes mellitus(T2DM).Methods A total of 319 postmenopausal women with T2DM who were hospitalized in the Department of Endocrinology,Drum Tower Hospital,Affiliated Hospital of Medical School,Nanjing University were enrolled in this study from August 2016 to October 2023.All the patients were divided into two groups according whether they had MCI:T2DM group(n=161)and MCI group(n=158).Differences in clinical characteristics were compared between the two groups.Pearson correlation was used to analyze the correlation between sex hormones and cognitive domains,and Logistic regression analysis was used to evaluate the influencing factors for MCI development.Results Serum prolactin levels were significantly lower in the MCI group than in the T2DM group[(5.5±2.1)vs(7.2±2.9)μg/L,P<0.05].Serum prolactin level was positively correlated with mini-mental state examination score,Montreal cognitive assessment score,immediate memory score,visuopatial constructional score,attention score and hippocampal volume(P<0.05),and negatively correlated with processing speed test(time)and executive function test(time)(P<0.05).Logistic regression analysis demonstrated that serum prolactin level was an influencing factor for the risk of MCI in postmenopausal women with T2DM(OR 0.715,95%CI 0.605～0.845,P<0.01).Conclusions The decrease of serum prolactin level is associated with an increased risk of MCI in postmenopausal women with T2DM.

Objective:To develop PixelSIFT-UNet, a novel semantic segmentation model that integrates deep learning with scale-invariant feature transform (SIFT) algorithm to improve the segmentation accuracy of oral mucosal lesions.Methods:This investigation utilized 838 standard clinical white light images of oral mucosal diseases acquired from January 2020 to December 2022 at the Stomatology Hospital Zhejiang University School of Medicine. Randomization was achieved through Python′s random.seed function implementation. The random sample function was subsequently applied for sampling distribution. The dataset was stratified into three subsets with a 6∶2∶2 ratio: training ( n=506), validation ( n=166), and testing ( n=166). Lesion boundaries were annotated using Labelme software, and a PixelSIFT-UNet-based deep learning model was developed with VGG-16 and ResNet-50 backbone networks. Model parameters were optimized using the validation set, and performance metrics [including Dice coefficient, mean intersection over union (mIoU), mean pixel accuracy (mPA), and Precision] were assessed on the test set. The model′s performance was benchmarked against conventional semantic segmentation frameworks (U-Net and PSPNet). Results:The developed PixelSIFT-UNet model could achieve precise segmentation of three common oral mucosal lesions: oral lichen planus, oral leukoplakia, and oral submucous fibrosis. Utilizing VGG-16 as the backbone network, the model achieved Dice coefficient, mIoU, mPA, and Precision values of 0.642, 0.699, 0.836, and 0.792, respectively. Implementation with ResNet-50 backbone network yielded metrics of 0.668, 0.733, 0.872 and 0.817, demonstrating significant improvements across all performance indicators compared to conventional U-Net model (relevant metrics: 0.662, 0.717, 0.861 and 0.809) and PSPNet model (relevant metrics: 0.671, 0.721, 0.858 and 0.813).Conclusions:The proposed PixelSIFT-UNet architecture demonstrates superior performance in oral mucosal lesion segmentation tasks, surpassing conventional semantic segmentation models and providing robust quantitative improvements in segmentation accuracy.

Objective To investigate the relationship between prolactin and mild cognitive impairment(MCI)in postmenopausal women with type 2 diabetes mellitus(T2DM).Methods A total of 319 postmenopausal women with T2DM who were hospitalized in the Department of Endocrinology,Drum Tower Hospital,Affiliated Hospital of Medical School,Nanjing University were enrolled in this study from August 2016 to October 2023.All the patients were divided into two groups according whether they had MCI:T2DM group(n=161)and MCI group(n=158).Differences in clinical characteristics were compared between the two groups.Pearson correlation was used to analyze the correlation between sex hormones and cognitive domains,and Logistic regression analysis was used to evaluate the influencing factors for MCI development.Results Serum prolactin levels were significantly lower in the MCI group than in the T2DM group[(5.5±2.1)vs(7.2±2.9)μg/L,P<0.05].Serum prolactin level was positively correlated with mini-mental state examination score,Montreal cognitive assessment score,immediate memory score,visuopatial constructional score,attention score and hippocampal volume(P<0.05),and negatively correlated with processing speed test(time)and executive function test(time)(P<0.05).Logistic regression analysis demonstrated that serum prolactin level was an influencing factor for the risk of MCI in postmenopausal women with T2DM(OR 0.715,95%CI 0.605～0.845,P<0.01).Conclusions The decrease of serum prolactin level is associated with an increased risk of MCI in postmenopausal women with T2DM.

AIM To investigate the effects of Wenfei Jiangzhuo Formula on mitochondrial function of Aβ25-35-induced BV-2 cells.METHODS In the establishment of cell model of Alzheimer's disease(AD)using Aβ25-35 on the BV-2 cells,the optimal concentration and time point of Aβ25-35 intervention were determined;and the groups for the intervention of LFHP-1c group(inhibitor)or the serum containing Wenfei Jiangzhuo Formula were set up.The detection of the optimal intervention concentration and time point by CCK-8 assay;the observation of cell migration and apoptosis by Transwell assay and Hoechst 33342 staining;the detection of the positive expressions of PGAM5 and Drp1 by immunofluorescence;and the detection of cellular PGAM5,Drp1,OPA1,and Mfn1/2 mRNA and protein expressions by RT-qPCR and Western blot were conducted.RESULTS The best AD cell model was established by 48 h exposure to 5 μmol/L of Aβ25-35,and most active cell viability was achieved with the 48 h use of serum containing 20％Wenfei Jiangzhuo Formula.Compared with the control group,the model group displayed decreased number of cell migration,more bright blue positive apoptotic cells,increased number of PGAM5 and Drp1 positive cells and their mRNA and protein expressions(P＜0.05);and decreased mRNA and protein expressions of OPA1 and Mfn1/2(P＜0.05).Compared with the model group,the groups intervened with the medicines shared increased number of cell migration,less bright blue positive apoptotic cells,decreased number of PGAM5 and Drp1 positive cells and their mRNA and protein expressions(P＜0.05);and elevated OPA1 and Mfn1/2 mRNA and protein expressions(P＜0.05).CONCLUSION Wenfei Jiangzhuo Formula exerts cerebroprotective effects to improve cognition by reducing cell damage and improving the balance of mitochondrial homeostasis through PGAM5-Drp1 axis in AD model.

Spine fracture and dislocation are common traumatic spinal conditions that often require surgical intervention due to compromised spinal stability. Surgical approaches include anterior, posterior, and combined anterior-posterior spinal procedures. According to the specific surgical requirements, patients may be placed in the prone position or repositioned between prone and supine positions during surgery. Intraoperative repositioning has become an essential step in patient positioning. However, during repositioning, patients with spinal fracture and dislocation are at increased risk for complications such as hemodynamic instability, nerve injury, and pressure injuries to the skin and soft tissue. Notably, due to the instability of the spinal cord, even minor manipulations can further exacerbate the damage, potentially leading to severe outcomes like paraplegia. Although the current clinical guidelines provide instructive recommendations for standard position, there remains no specific protocols for intraoperative repositioning in patients with spine fracture and dislocation. With a concern for the lack of clinical studies on positioning techniques, risk prevention, and operational norms for special patients, no applicable guidelines or standards are available. A consensus was required to provide clinical reference, meet the requirements of surgical treatment, and minimize the safety risks of patients caused by improper placement of positions. Professional Committee of Operating Room Nursing of Shaanxi Nursing Association organized experts in nursing management and operating room nursing from major hospitals across China to formulate Expert consensus on intraoperative repositioning for patients with spinal fracture and dislocation ( version 2025). The consensus provides 11 recommendations covering pre-repositioning preparation, intraoperative maneuvers, and post-repositioning observation, aiming to provide references for clinical standardization of the intraoperative repositioning process and protection of patients′ safety.

Objective:To develop PixelSIFT-UNet, a novel semantic segmentation model that integrates deep learning with scale-invariant feature transform (SIFT) algorithm to improve the segmentation accuracy of oral mucosal lesions.Methods:This investigation utilized 838 standard clinical white light images of oral mucosal diseases acquired from January 2020 to December 2022 at the Stomatology Hospital Zhejiang University School of Medicine. Randomization was achieved through Python′s random.seed function implementation. The random sample function was subsequently applied for sampling distribution. The dataset was stratified into three subsets with a 6∶2∶2 ratio: training ( n=506), validation ( n=166), and testing ( n=166). Lesion boundaries were annotated using Labelme software, and a PixelSIFT-UNet-based deep learning model was developed with VGG-16 and ResNet-50 backbone networks. Model parameters were optimized using the validation set, and performance metrics [including Dice coefficient, mean intersection over union (mIoU), mean pixel accuracy (mPA), and Precision] were assessed on the test set. The model′s performance was benchmarked against conventional semantic segmentation frameworks (U-Net and PSPNet). Results:The developed PixelSIFT-UNet model could achieve precise segmentation of three common oral mucosal lesions: oral lichen planus, oral leukoplakia, and oral submucous fibrosis. Utilizing VGG-16 as the backbone network, the model achieved Dice coefficient, mIoU, mPA, and Precision values of 0.642, 0.699, 0.836, and 0.792, respectively. Implementation with ResNet-50 backbone network yielded metrics of 0.668, 0.733, 0.872 and 0.817, demonstrating significant improvements across all performance indicators compared to conventional U-Net model (relevant metrics: 0.662, 0.717, 0.861 and 0.809) and PSPNet model (relevant metrics: 0.671, 0.721, 0.858 and 0.813).Conclusions:The proposed PixelSIFT-UNet architecture demonstrates superior performance in oral mucosal lesion segmentation tasks, surpassing conventional semantic segmentation models and providing robust quantitative improvements in segmentation accuracy.

Objective:To evaluate the effect of acupuncture on postoperative delirium (POD) in diabetic patients undergoing surgery under general anesthesia.Methods:In this randomized controlled trial, 92 diabetic patients of either sex, aged 30-80 yr, with a body mass index of 18-28 kg/m 2, of American Society of Anesthesiologists Physical Status classification Ⅱ or Ⅲ, scheduled for elective surgery under general anesthesia, were divided into 2 groups ( n=46 each) using a table of random numbers: control group (group C) and acupuncture group (group A). Group A received acupuncture at the Baihui (GV20), Shenting (GV24) and Sishencong (EX-HN1) acupoints before anesthesia. The needles were retained for 30 min, with manual stimulation applied every 10 min for 10 s each time. After 4 stimulations, routine anesthesia was carried out. Group C received routine anesthesia only. Regional cerebral oxygen saturation was recorded on admission to the operating room (T 0), after anesthesia induction (T 1), at the start of surgery (T 2), at the end of surgery (T 3), and immediately after tracheal extubation (T 4). The POD developed within 3 days after surgery was assessed. The occurrence of needle-related adverse effects such as fainting, subcutaneous bleeding, and local paresthesia was recorded. Results:Compared with group C, the incidence of POD was significantly reduced, and the regional cerebral oxygen saturation was increased at T 1, 4 in group A ( P<0.05). Conclusions:Acupuncture can decrease the development of POD in diabetic patients undergoing surgery under general anesthesia, which is related to an increase in regional cerebral oxygen saturation.

We reported the clinical,imaging,and treatment process of a patient with severe cranial penetrating injury caused by a nail from a nail gun that accidentally shot into the eye and penetrated into the skull while working.The patient was admitted to the Qingpu Branch of Zhongshan Hospital,Fudan University after being injured by the nail gun.The patient was admitted to the hospital 1 hour after injury with coma.After admission,the foreign body was completely removed and part of the hematoma was cleared through emergency surgery.After surgery,there were high-risk complications such as cerebral edema peak,intracranial hypertension,intracranial infection,cerebrospinal fluid leakage,and intracranial pseudoaneurysm.The patient eventually regained consciousness and was transferred to rehabilitation treatment.

Objective:To evaluate the effect of acupuncture on postoperative delirium (POD) in diabetic patients undergoing surgery under general anesthesia.Methods:In this randomized controlled trial, 92 diabetic patients of either sex, aged 30-80 yr, with a body mass index of 18-28 kg/m 2, of American Society of Anesthesiologists Physical Status classification Ⅱ or Ⅲ, scheduled for elective surgery under general anesthesia, were divided into 2 groups ( n=46 each) using a table of random numbers: control group (group C) and acupuncture group (group A). Group A received acupuncture at the Baihui (GV20), Shenting (GV24) and Sishencong (EX-HN1) acupoints before anesthesia. The needles were retained for 30 min, with manual stimulation applied every 10 min for 10 s each time. After 4 stimulations, routine anesthesia was carried out. Group C received routine anesthesia only. Regional cerebral oxygen saturation was recorded on admission to the operating room (T 0), after anesthesia induction (T 1), at the start of surgery (T 2), at the end of surgery (T 3), and immediately after tracheal extubation (T 4). The POD developed within 3 days after surgery was assessed. The occurrence of needle-related adverse effects such as fainting, subcutaneous bleeding, and local paresthesia was recorded. Results:Compared with group C, the incidence of POD was significantly reduced, and the regional cerebral oxygen saturation was increased at T 1, 4 in group A ( P<0.05). Conclusions:Acupuncture can decrease the development of POD in diabetic patients undergoing surgery under general anesthesia, which is related to an increase in regional cerebral oxygen saturation.