Objective:To investigate the survival outcomes and prognostic factors in patients undergoing radical resection for pancreatic body and tail cancer.Methods:A retrospective case series study was conducted on 992 patients who underwent radical resection for pancreatic body and tail cancer at the Pancreatic Center of the First Affiliated Hospital of Nanjing Medical University from January 2016 to June 2024. In this study, 577 (58.2%) were male and 415 (41.8%) were female,with an age of (65±9) years (range: 26 to 86 years). Follow-up continued until June 2024. Survival rates were estimated using the Kaplan-Meier method,and prognostic factors were identified using univariate and multivariate Cox proportional hazards models.Results:Among 992 patients,open surgery was the predominant approach (89.1%, 884/992), and radical antegrade modular pancreatosplenectomy (RAMPS) was performed in 317 patients (32.0%). Combined organ resection,venous resection,and arterial resection were performed in 23.5%, 9.3%,and 11.2% of patients,respectively. The rates of R0, R1-1 mm, and R1-direct resections were 49.8% (494/992),41.5% (412/992), and 8.7% (86/992),respectively. Stage ⅡB was the most common TNM stage (32.2%,319/992). A total of 801 patients (80.8%) received adjuvant chemotherapy. The median follow-up period was 32.0(8.8) months(range:3.2 to 105.3 months),during which 508 patients (51.2%) died. The overall median survival (OS) was 26.4 months,with 1-,3-, and 5-year survival rates of 79.0%,40.0%, and 29.0%, respectively. In the recent five years (from 2020 to 2024), the median OS improved significantly to 34.1 months compared to 20.0 months from 2016 to 2019 ( P<0.01). Histological subtype analysis showed that the median OS time was 26.7 months for pancreatic ductal adenocarcinoma (PDAC, n=855),58.9 months for invasive intraductal papillary mucinous carcinoma (IPMC, n=32),and 15.7 months for adenosquamous carcinoma of pancreas (ASCP, n=73) ( P=0.001). Among PDAC patients, adjuvant chemotherapy significantly improved survival (29.1 months vs. 14.4 months, P<0.01);in IPMC patients, adjuvant chemotherapy also extended survival (65.7 months vs. 58.9 months, P=0.047). Although ASCP patients receiving chemotherapy had a longer median OS time than those without (18.8 months vs. 8.9 months),the difference was not statistically significant ( P=0.151). Multivariate Cox regression analysis in PDAC patients indicated that adjuvant chemotherapy, R0 resection, T stage,N stage,and tumor differentiation were independent prognostic factors ( P<0.01). The median OS time by TNM stage was:not reached for stage ⅠA, 51.6 months for ⅠB, 25.5 months for ⅡA, 23.7 months for ⅡB, 23.0 months for Ⅲ, and 14.4 months for Ⅳ. The median OS time for R0,R1-1 mm,and R1-direct resections was 34.1,24.7,and 15.7 months,respectively ( P<0.01). Conclusion:Adjuvant chemotherapy,R0 resection,tumor stage,and differentiation are independent prognostic factors for pancreatic body and tail cancer.

Objective:To investigate the survival outcomes and prognostic factors in patients undergoing radical resection for pancreatic body and tail cancer.Methods:A retrospective case series study was conducted on 992 patients who underwent radical resection for pancreatic body and tail cancer at the Pancreatic Center of the First Affiliated Hospital of Nanjing Medical University from January 2016 to June 2024. In this study, 577 (58.2%) were male and 415 (41.8%) were female,with an age of (65±9) years (range: 26 to 86 years). Follow-up continued until June 2024. Survival rates were estimated using the Kaplan-Meier method,and prognostic factors were identified using univariate and multivariate Cox proportional hazards models.Results:Among 992 patients,open surgery was the predominant approach (89.1%, 884/992), and radical antegrade modular pancreatosplenectomy (RAMPS) was performed in 317 patients (32.0%). Combined organ resection,venous resection,and arterial resection were performed in 23.5%, 9.3%,and 11.2% of patients,respectively. The rates of R0, R1-1 mm, and R1-direct resections were 49.8% (494/992),41.5% (412/992), and 8.7% (86/992),respectively. Stage ⅡB was the most common TNM stage (32.2%,319/992). A total of 801 patients (80.8%) received adjuvant chemotherapy. The median follow-up period was 32.0(8.8) months(range:3.2 to 105.3 months),during which 508 patients (51.2%) died. The overall median survival (OS) was 26.4 months,with 1-,3-, and 5-year survival rates of 79.0%,40.0%, and 29.0%, respectively. In the recent five years (from 2020 to 2024), the median OS improved significantly to 34.1 months compared to 20.0 months from 2016 to 2019 ( P<0.01). Histological subtype analysis showed that the median OS time was 26.7 months for pancreatic ductal adenocarcinoma (PDAC, n=855),58.9 months for invasive intraductal papillary mucinous carcinoma (IPMC, n=32),and 15.7 months for adenosquamous carcinoma of pancreas (ASCP, n=73) ( P=0.001). Among PDAC patients, adjuvant chemotherapy significantly improved survival (29.1 months vs. 14.4 months, P<0.01);in IPMC patients, adjuvant chemotherapy also extended survival (65.7 months vs. 58.9 months, P=0.047). Although ASCP patients receiving chemotherapy had a longer median OS time than those without (18.8 months vs. 8.9 months),the difference was not statistically significant ( P=0.151). Multivariate Cox regression analysis in PDAC patients indicated that adjuvant chemotherapy, R0 resection, T stage,N stage,and tumor differentiation were independent prognostic factors ( P<0.01). The median OS time by TNM stage was:not reached for stage ⅠA, 51.6 months for ⅠB, 25.5 months for ⅡA, 23.7 months for ⅡB, 23.0 months for Ⅲ, and 14.4 months for Ⅳ. The median OS time for R0,R1-1 mm,and R1-direct resections was 34.1,24.7,and 15.7 months,respectively ( P<0.01). Conclusion:Adjuvant chemotherapy,R0 resection,tumor stage,and differentiation are independent prognostic factors for pancreatic body and tail cancer.

A redox-responsive hyaluronic acid-vitamin E polyethylene glycol succinate nanoparticle loaded with paclitaxel (HA-SS-TPGS@PTX) was designed to investigate its mechanism for overcoming multidrug resistance (MDR) in non-small cell lung cancer (NSCLC) in vitro. HA-SS-TPGS@PTX nanoparticles were prepared using an emulsion-ultrasonication method. Techniques such as flow cytometry and confocal laser scanning microscopy (CLSM) were employed to study their effects on apoptosis induction, mitochondrial function, and the regulation of P-glycoprotein (P-gp) expression in PTX-resistant lung cancer cells (A549/T). Results showed that HA-SS-TPGS@PTX nanoparticles significantly inhibited the proliferation of A549/T cells in vitro, with an IC50 of 1.35 μg/mL. The nanoparticles entered the cells via CD44 receptor-mediated endocytosis. The high intracellular concentration of glutathione (GSH) triggered the release of PTX and TPGS, which subsequently induced a decrease in mitochondrial membrane potential, leading to apoptosis. Meanwhile, HA-SS-TPGS@PTX also inhibited P-gp expression and ATP consumption, thereby blocking drug efflux. The design of HA-SS-TPGS@PTX provides a new strategy for overcoming MDR in NSCLC.

Fifty whitespotted bamboo sharks (Chiloscyllium playgiosum) of both sexes were used to establish a large capacity variable domain of the new antigen receptor (VNAR) library with a total capacity of over 10⁹ colony-forming units (CFU). It was applied to screen VNARs against human serum albumin (HSA) and human transcription factor EB (TFEB), respectively. Meanwhile, VNAR libraries specific to HSA and TFEB with capacities above 10⁸ CFU were obtained following conventional immunization. These two approaches were systematically studied in terms of VNAR yield and composition. By comparing the VNAR sequences obtained from naïve and antigen-immunized libraries, we found that the complementary-determining region 3 (CDR3) of the former differs in composition from that of the latter. It shares a higher degree of homology with the naïve library. Meanwhile, the binding efficiency assessed by ELISA is also different between the naïve and antigen-immunized libraries. The binding of VNARs from the TFEB-immunized library appeared to surpass that observed with the naïve libraries, whereas the performance of VNARs from the HSA-immunized library indicated that both the immunized and naïve libraries for HSA had positive binding responses in polyclonal and monoclonal ELISA. The results are useful to develop novel diagnostic and therapeutic products based on shark VNARs.

ObjectiveTo investigate the potential machanism of Xiaozhong Zhitong Mixture （消肿止痛合剂， XZM） in the treatment of diabetes foot ulcer （DFU）. MethodsFifty SD rats were randomly divided into blank group， model group， XZM group， inhibitor group， XZM plus inhibitor group （combination group）， with 10 rats in each group. Except for the blank group， rats were fed with high-sugar， high-fat， high-cholesterol diet， intraperitoneally injected with streptozotocin， and subjected to skin defect to establish DFU model. After successful modeling， the XZM group and the combination group were given 1 ml/（100 g·d）of XZM by gavage， while the blank group， model group， and inhibitor group were all given an equal volume of 0.9% sodium chloride injection by gavage. Thirty minutes later， the inhibitor group and the combination group were intraperitoneally injected with 5 mg/（kg·d） of Notch1 inhibitor DAPT. All groups were treated once a day. After 14 days of administration， the skin tissue from the dorsal foot of the blank group rats and wound tissue from the other groups were collected. The pathological changes of granulation tissue in the wound were detected using hematoxylin eosin （HE） staining. The microvascular density （MVD） in wounds was detected through immunohistochemical staining. Real time fluorescence quantitative polymerase chain reaction （RT-PCR） and western blotting were used to detect the mRNA and protein levels of Notch1 homolog （Notch1）， Delta-like ligand 4 （Dll4）， Delta-like ligand 4 （VEGF）， and angiopoietin 2 （Ang-2）， respectively. ResultsHistological results showed that the epidermal structure in the dorsal foot skin tissue of the rats in the blank group was intact. In the wound tissue of the model group， the epidermis exhibited excessive keratinization， vacuolar cytoplasm， and a large number of inflammatory cells infiltrating the tissue， while in the XZM group， a large amount of scab formation was observed in the epidermis， with no significant inflammatory cell infiltration and a noticeable increase in fibroblasts. In the combination group and the inhibitor group， partial epidermal scab formation was observed in the wound tissue with a small amount of inflammatory cell infiltration. Compared to those in the blank group， the MVD in the wound tissue increased in the model group， as well as the mRNA expression and protein levels of Notch1 and Dll4， while VEGFA and Ang-2 mRNA expression and protein levels significantly decreased （P＜0.05 or P＜0.01）. Compared to those in the model group， the MVD in the wound tissue of all medication groups significantly increased， and the mRNA and protein levels of Notch1 and Dll4 decreased， while VEGFA and Ang-2 mRNA expression and protein levels increased （P＜0.05 or P＜0.01）. Compared to the XZM group， the inhibitor group and the combination group showed decreased MVD in wound tissue， increased Notch1 and Dll4 mRNA and protein levels， and decreased expression of VEGFA and Ang-2 mRNA and proteins （P＜0.05 or P＜0.01）. ConclusionXZM can effectively promote wound healing in DFU rats， and its mechanism of action may be related to the inhibition of Dll4/Notch1 signaling pathway in the wound tissue， therey promoting angiogenesis.

Artificial intelligence methods are developing rapidly in the medical field.However,the effectiveness of model training relies heavily on the support of sufficient sample sizes.Due to various constraints such as privacy,security,ethics,and costs in the medical field,it is rather difficult to obtain a large number of labeled training samples.Problems like the scarcity of rare disease cases,the lack of biological data for drug molecule mining,and the shortage of high-quality annotations for medical images significantly reduce the ability of models to learn from observed data,which in turn leads to poor prediction performance.In this context,constructing efficient learning artificial intelligence models for small sample data is of far-reaching significance both theoretically and practically.On the one hand,it can help to explore potential patterns when samples are insufficient in the early stage of new research.On the other hand,high-quality models can effectively reduce the cost of manual annotation,shorten the research cycle,and provide opportunities for solving challenging problems in medical research where it is difficult to collect a sufficient number of samples.Driven by both the expected advantages and actual needs,the research on artificial intelligence for small sample data has gradually become a highly anticipated and important research direction.This review systematically collates and summarizes the principles,advantages,disadvantages,applicable scenarios,and principal challenges associated with six artificial intelligence methods currently employed in the context of small-sample medical data,namely generative adversarial networks,graph neural networks,transfer learning,reinforcement learning,and Meta-learning.Furthermore,the review provides an extensive outlook and in-depth contemplation on the future trajectory of artificial intelligence methodologies in the realm of small sample data in medicine.

Artificial intelligence methods are developing rapidly in the medical field.However,the effectiveness of model training relies heavily on the support of sufficient sample sizes.Due to various constraints such as privacy,security,ethics,and costs in the medical field,it is rather difficult to obtain a large number of labeled training samples.Problems like the scarcity of rare disease cases,the lack of biological data for drug molecule mining,and the shortage of high-quality annotations for medical images significantly reduce the ability of models to learn from observed data,which in turn leads to poor prediction performance.In this context,constructing efficient learning artificial intelligence models for small sample data is of far-reaching significance both theoretically and practically.On the one hand,it can help to explore potential patterns when samples are insufficient in the early stage of new research.On the other hand,high-quality models can effectively reduce the cost of manual annotation,shorten the research cycle,and provide opportunities for solving challenging problems in medical research where it is difficult to collect a sufficient number of samples.Driven by both the expected advantages and actual needs,the research on artificial intelligence for small sample data has gradually become a highly anticipated and important research direction.This review systematically collates and summarizes the principles,advantages,disadvantages,applicable scenarios,and principal challenges associated with six artificial intelligence methods currently employed in the context of small-sample medical data,namely generative adversarial networks,graph neural networks,transfer learning,reinforcement learning,and Meta-learning.Furthermore,the review provides an extensive outlook and in-depth contemplation on the future trajectory of artificial intelligence methodologies in the realm of small sample data in medicine.

Objective To observe the value of CT radiomics machine learning(ML)model for predicting stone free rate(SFR)of urinary calculi after retrograde intrarenal surgery(RIRS).Methods Totally 216 patients with urinary calculi who underwent RIRS were retrospectively enrolled and divided into residual group(n=73)and non-residual group(n=143).Univariate and multivariate logistic regression(LR)were performed to analyze clinical data and CT manifestations of stones to screen independent predictors of SFR after RIRS.Window width and window level normalization combined with max-min normalization(denoted as method a),max-min normalization(denoted as method b),window width and window level normalization(denoted as method c)and non-normalization(denoted as method d)of pre-RIRS abdominal CT were performed,respectively,and the best radiomics features of stones were extracted and screened to establish ML models,including support vector machine(SVM),LR and stochastic gradient descent(SGD)models,and the best ML model was screened.RUSS and modified S.T.O.N.E scores were evaluated based on pre-RIRS CT for predicting SFR of urinary calculi after RIRS.A combined model was then constructed with the independent predictors and the best ML model.The predictive efficacy of each model and scoring system were assessed.Results The number of stones,CT value and volume of the maximum stone were all independent predictors of SFR after RIRS(all P＜0.05).The area under the curve(AUC)of SVM model constructed with images preprocessed by method b was the highest(0.861),higher than that of the total scores of RUSS and modified S.T.O.N.E(AUC=0.750,0.759,both P＜0.05)but not different from that of combined model(AUC=0.853,P=0.775).Conclusion Radiomics SVM model based on max-min normalization preprocessed CT could effectively predict SFR of urinary calculi after RIRS.

Objective To explore the efficacy,safety and clinical application value of CT-guided percutaneous needle biopsy(PTNB)of extrapulmonary metastases in the diagnosis of lung cancer.Methods The clinical and imaging data of 62 patients with extrapulmonary metastases of lung cancer underwent CT-guided PTNB were analyzed retrospectively.The safety,success rate,accuracy and clinical value of PTNB of extrapulmonary metastases were summarized.Results Puncture biopsy was successfully completed in all 62 cases(62/62,100％).The accuracy was 91.9％(57/62),and none occurred serious complications.Conclusion The CT-guided PTNB of extrapulmonary metastases is safe,effective and reliable.It is an effective supplement for lung biopsy and is worthy of clinical application and promotion.