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.

ObjectiveTo investigate the association of the serum levels of Golgi protein 73 （GP73）， hypoxia-inducible factor-1α （HIF-1α）， and matrix metalloproteinase-9 （MMP-9） before and after transcatheter arterial chemoembolization （TACE） with the treatment outcome of patients with hepatitis B virus （HBV）-related hepatocellular carcinoma （HCC）. MethodsA total of 135 patients with HBV-related HCC who received TACE for the first time in The First Affiliated Hospital of Xinjiang Medical University from January 2023 to October 2025 were enrolled as subjects， and the serum levels of GP73， HIF-1α， and MMP-9 were measured before TACE and at 1 month after TACE. According to the treatment outcome after TACE， the patients were divided into remission group and non-remission group. The two groups were compared in terms of general information and the serum levels of GP73， HIF-1α， and MMP-9 before and after treatment. The Logistic regression analysis was used to investigate the influencing factors for the efficacy of TACE， and the receiver operating characteristic （ROC） curve was used to assess the value of the serum levels of GP73， HIF-1α， and MMP-9 before treatment used alone or in combination in predicting the efficacy of TACE. The independent-samples t test was used for comparison of continuous data between two groups， and the chi-square test was used for comparison of categorical data between two groups. ResultsAmong the 135 patients after treatment， 78 （57.78%） were enrolled in the remission group and 57 （42.22%） were enrolled in the non-remission group. Before treatment， the remission group had significantly lower serum levels of GP73， HIF-1α， and MMP-9 than the non-remission group （all P<0.05）； after treatment， both groups had reductions in the serum levels of GP73， HIF-1α， and MMP-9， and the remission group had significantly lower levels than the non-remission group （all P<0.05）. The binary Logistic regression analysis showed that increases in the serum levels of GP73， HIF-1α， and MMP-9 before treatment were independent influencing factors for the efficacy of TACE （all P<0.05）. The ROC curve analysis showed that the serum levels of GP73， HIF-1α， and MMP-9 before treatment used alone had an area under the ROC curve （AUC） of 0.799， 0.835， and 0.777， respectively， in predicting the efficacy of TACE， among which HIF-1α showed the highest predictive value， and the combination of these three indicators had an AUC of 0.950， a Youden index of 0.787， an optimal cut-off value of 0.39， a sensitivity of 87.72%， and a specificity of 91.03%. ConclusionTACE can reduce the serum levels of GP73， HIF-1α， and MMP-9 in patients with HBV-related HCC， and patients with a good outcome tend to have greater reductions. The high serum levels of GP73， HIF-1α， and MMP-9 before treatment are independent risk factors for poor short-term efficacy of TACE， and the combination of these three indicators has a relatively high value in predicting the efficacy of TACE.

Objective: To preliminarily explore the microbiome information of apheresis platelets from healthy blood donors in Nanjing, Jiangsu, and to lay the groundwork for further studies on the microbial community composition and diversity in this region. Methods: Thirteen fresh apheresis platelet samples (5 mL each) were collected from voluntary blood donors at the Nanjing Red Cross Blood Center. After centrifugation and removal of the supernatant, DNA was extracted from the pellet and libraries were constructed. High-throughput sequencing was performed on the Illumina NovaSeq X Plus platform, followed by in-depth bioinformatics analysis. Metagenomic analysis software, including fastp, Bowtie 2, MEGAHIT, Meta-GeneMark, DIAMOND, and Micro_NR, were used to analyze microbial abundance in the apheresis platelet samples. Results: Using Krona for visualization of species annotation and metagenomic sequencing analysis, after removing the human genome background, a total of 71 phyla, 54 classes, 102 orders, 181 families, 421 genera, and 1 340 species of microbes were identified. Starting from relative abundance tables at different taxonomic levels, at the family level, the top 10 most abundant families were: Flavobacteriaceae; Moraxellaceae; Rhizobiaceae; Sphingomonadaceae; Comamonadaceae; Caulobacteraceae; Burkholderiaceae; Acidimicrobiaceae; Streptomycetaceae; Streptosporangiaceae. At the genus level, the top 10 most abundant genera were: Wenyingzhuangia, Acinetobacter, Aquabacterium, Rhizobium, Brevundimonas, Novosphingobium, Astrithrvirus, Unclassified Rhizobiaceae, Acidiferrimicrobium, and Limnobacter. Conclusion: Next-generation sequencing (NGS) was used to comprehensively annotate the metagenome of apheresis platelets from voluntary blood donors in Nanjing, Jiangsu, enabling rapid acquisition of extensive microbial data. Exploratory analysis of the blood microbiome in healthy populations provides valuable guidance for developing context-specific measures to prevent platelet bacterial contamination, thereby enhancing blood safety.

Objective To analyze the proportion and clinicopathological characteristics of HER2-positive breast cancer patients with BRCA1/2 pathogenic variants, and their response to neoadjuvant anti-HER2 targeted therapy. Methods The clinicopathological data of 531 breast cancer patients with germline BRCA1/2 pathogenic variants (201 with BRCA1 variants and 330 with BRCA2 variants) were analyzed. Results Among the 201 BRCA1 and 330 BRCA2 variants, 17 (8.5%) and 42 (12.7%) HER2-positive breast cancer cases were identified, respectively, accounting for 11.1% of all BRCA1/2-mutated breast cancers. Compared with BRCA1/2-mutated HR-positive/HER2-negative patients, HER2-positive patients did not present any significant differences in clinicopathological features; however, compared with triple-negative breast cancer patients, HER2-positive patients had a later onset age and lower tumor grade. Among the 17 patients who received neoadjuvant anti-HER2 targeted therapy, 10 cases achieved pCR (58.8%), whereas 7 cases did not (41.2%). Conclusion HER2-positive breast cancer accounts for more than 10% of BRCA1/2-mutated patients. Approximately 40% of these patients fail to achieve pCR after neoadjuvant targeted therapy. This phenomenon highlights the possibility of combining anti-HER2 targeted agents with poly (adenosine diphosphate-ribose) polymerase inhibitors.

Methods: Seventy-five patients with atlas fracture were included from January 2015 to December 2020. Based on the anatomy of the fracture line, atlas fractures were divided into three types. Each type was divided into two subtypes according to the fracture displacement. Unweighted Cohen kappa coefficients were applied to evaluate the reliability and reproducibility. Results: According to the new classification, 17 cases of type A1, 12 of type A2, seven of type B1, 13 of type B2, 12 of type C1, and 14 of type C2 were identified. The K-values of the interobserver and intraobserver reliability were 0.846 and 0.912, respectively, for the new classification. The K-values of interobserver reliability for types A, B, and C were 0.843, 0.799, and 0.898, respectively. The K-values of intraobserver reliability for types A, B, and C were 0.888, 0.910, and 0.935, respectively. The mean K-values of the interobserver and intraobserver reliability for subtypes were 0.687 and 0.829, respectively. Conclusions The new classification of atlas fractures can cover nearly all atlas fractures. This system is the first to evaluate the severity of fractures based on the C1 articular facet and fracture displacement and strengthen the anatomy ring of the atlas. It is concise, easy to remember, reliable, and reproducible.

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.

ObjectiveTo investigate and analyze an outbreak of rotavirus infectious diarrhea in a prison in Chongqing Municipality, to provide a basis for adult rotavirus surveillance and prevention, and to explore the public health problems in special settings. MethodsA retrospective survey was conducted to collect and analyze data on individual cases with diarrheal disease on-site. The clinical characteristics, as well as the temporal, spatial and geographical distribution patterns of the epidemic were described. Multi-pathogen detection tests were conducted both on diarrhea cases and environmental samples, with viral genotyping performed on positive samples. A case-control analysis was performed to identify the causes of the outbreak, and an SEIR model was adopted to predict the outbreak trend and evaluate the effectiveness of interventions. ResultsA total of 65 cases were found among the inmates, with an attack rate of 2.03%. The predominant clinical manifestations included diarrhea (89.23%), watery stool (73.85%), and dehydration (18.46%). The epidemic curve indicated a “human-to-human” transmission pattern, with an average incubation period of 5‒6 days. The attack rates among chefs in the main canteen (80.00%, 8/10) and caterers (28.33%, 17/60) were significantly higher than those of other inmates （P<0.05）. Multi-pathogen polymerase chain reaction (PCR) testing detected positive for group A rotavirus, with the viral genotyping identified as G9P [8] strain. Factors such as unprotected "bare-handed" food distribution among cases with diarrhea (OR=9.512, 95%CI: 4.261‒21.234) and close contact with diarrhea cases (OR=3.656, 95%CI: 1.719‒7.778) were the possible cause of the outbreak. The SEIR model (r₀=5, α=0.3, β₁=0.08, β₂=0.04) was constructed using prison inmates as susceptible population, aiming at fitting the initial transmission trend of the outbreak, and the epidemic rate declined rapidly after intervention measures were implemented (r_t≈0). ConclusionThis rare rotavirus infection diarrhea outbreak among adults in confined settings suggests that the construction of public health prevention and control systems in prison may be overlooked. Cross infection during meal processing and distribution in the canteens of such settings is likely to be the cause of the outbreak. Given the potential neglect of public heath system construction in special settings, it is imperative to enhance the surveillance and monitoring of rotavirus and other intestinal multi-pathogens among adults, as well as the construction of public health prevention and control systems in these special settings.

Methods: Seventy-five patients with atlas fracture were included from January 2015 to December 2020. Based on the anatomy of the fracture line, atlas fractures were divided into three types. Each type was divided into two subtypes according to the fracture displacement. Unweighted Cohen kappa coefficients were applied to evaluate the reliability and reproducibility. Results: According to the new classification, 17 cases of type A1, 12 of type A2, seven of type B1, 13 of type B2, 12 of type C1, and 14 of type C2 were identified. The K-values of the interobserver and intraobserver reliability were 0.846 and 0.912, respectively, for the new classification. The K-values of interobserver reliability for types A, B, and C were 0.843, 0.799, and 0.898, respectively. The K-values of intraobserver reliability for types A, B, and C were 0.888, 0.910, and 0.935, respectively. The mean K-values of the interobserver and intraobserver reliability for subtypes were 0.687 and 0.829, respectively. Conclusions The new classification of atlas fractures can cover nearly all atlas fractures. This system is the first to evaluate the severity of fractures based on the C1 articular facet and fracture displacement and strengthen the anatomy ring of the atlas. It is concise, easy to remember, reliable, and reproducible.

ObjectiveTo investigate the improvement effect of Qibai Pingfei capsules on pulmonary vascular remodeling in rats at different stages of chronic obstructive pulmonary disease （COPD） and to analyze its possible mechanism of action. MethodsMale Sprague-Dawley （SD） rats were randomly divided into a normal group， an early COPD model group， an advanced COPD model group， an early-intervention high-dose group， a late-intervention high-dose group， an early-intervention low-dose group， a late-intervention low-dose group， an early-intervention pyrrolidine dithiocarbamate （PDTC） group， and a late-intervention PDTC group， with 15 rats in each group. A rat model of early COPD was constructed by using cigarette smoke combined with airway infusion using lipopolysaccharide（LPS）， and a rat model of advanced COPD was constructed by using airway infusion with LPS， cigarette smoke， and hypoxia. All groups except the normal group were given LPS airway drops on days 1 and 14 of the experiment， smoked for 1 h per day， and administered the drug once a day for 40 weeks from day 15 onward. In the high- and low-dose groups， rats were given 1 g·kg^-1 and 250 mg·kg^-1 Qibai Pingfei capsules， respectively by gavage， and in PDTC groups， rats were given 100 mg·kg^-1 of PDTC by intraperitoneal injection. The advanced COPD model group underwent 6 h of hypoxia per day in weeks 5-6. Lung function and mean pulmonary artery pressure were tested in rats. Morphologic changes in lung tissues were detected by hematoxylin-eosin（HE）staining. Collagen deposition in lung tissues was examined by Masson staining， and the levels of inflammatory factors including interleukin-1β（IL-1β）， interleukin-6（IL-6）， and tumor necrosis factor-α（TNF-α）in lung tissues were detected by enzyme-linked immunosorbent assay （ELISA）. The number of inflammatory cells in the alveolar lavage fluid of rats in each group was detected by Giemsa staining， and the protein expression of Toll-like receptor 4（TLR4）， myeloid differentiation factor 88（MyD88）， nuclear factor-κB（NF-κB）， TNF-α， vascular endothelial-cadherin（VE-cadherin）， α-smooth muscle actin（α-SMA）， and platelet endothelial cell adhesion molecule-1（CD31） was detected by Western blot in the lung tissues of rats. ResultsCompared with the normal group， the model group showed significantly decreased forced expiratory volume in 0.3 s （FEV_0.3）， forced vital capacity （FVC）， and FEV_0.3/FVC ratio related to lung function （P<0.05）， thickening of pulmonary vasculature， increased collagen deposition in the lungs， and enhanced mean pulmonary arterial pressure and expression levels of IL-6， IL-1β， and TNF-α （P<0.05）. Additionally， the model group also exhibited increased numbers of macrophages， lymphocytes， and neutrophils （P<0.05）， significantly higher protein expression of TLR4， MyD88， NF-κB， TNF-α， and α-SMA （P<0.05）， and significantly lower protein expression of VE-cadherin and CD31 （P<0.05）. Lung function was significantly improved in the Qibai Pingfei capsules groups compared with the model group （P<0.05）， with mean pulmonary arterial pressure reduced and pulmonary vascular thickening and collagen deposition in the lungs ameliorated. The Qibai Pingfei capsules groups also showed reduced expression levels of IL-6， IL-1β， and TNF-α （P<0.05） and decreased numbers of macrophages， lymphocytes， and neutrophils （P<0.05）， as well as reduced protein expression of TLR4， MyD88， NF-κB， TNF-α， and α-SMA （P<0.05） and elevated protein expression of VE-cadherin and CD31 （P<0.05） in rat lung tissues. ConclusionQibai Pingfei capsules inhibits inflammatory response and endothelial-to-mesenchymal transition probably by regulating the TLR4/NF-κB signaling pathway， thus improving pulmonary vascular remodeling in COPD model rats and showing therapeutic effects in the early stage of COPD.