Objective To preliminarily investigate the safety and efficacy of donor pancreas needle biopsy in simultaneous pancreas-kidney transplantation. Methods Clinical data of 7 cases undergoing donor pancreas biopsy were collected retrospectively. All cases underwent donor pancreas biopsy before or during simultaneous pancreas-kidney transplantation. Frozen section or paraffin sectioning techniques were used for tissue preparation, and hematoxylin-eosin and Masson staining were performed to histologically evaluate the donor pancreas. The quality of donor pancreas was comprehensively assessed by combining histological findings with the donor's clinical data. Postoperative follow-up data of 5 simultaneous pancreas-kidney transplant recipients were collected to summarize the safety of donor pancreas biopsy and the prognosis of transplant recipients. Results The 7 pancreas donors were aged 28 to 62 years, with a body mass index ranging from 20.76 to 27.68 kg/m². Liver ultrasound indicated fatty liver in 3 cases, while pancreatic ultrasound did not reveal any significant abnormalities. Among them, biopsy was performed on 2 donors after completion of pancreatic procurement and processing, and the frozen section histology showed moderate acute pancreatitis changes (edema of acinar cells, necrosis and inflammatory cell infiltration). Combined with a serum amylase level elevated more than 3 times the upper limit of normal value, these two donor pancreases were finally discarded. The remaining 5 cases underwent biopsy immediately after pancreatic vascular anastomosis during simultaneous pancreas-kidney transplantation, and histological evaluation was performed on paraffin-embedded sections. No biopsy-related complications (such as bleeding, pancreatic fistula, etc.) occurred after transplantation. One recipient died of severe infection 2 months after transplantation, while the other 4 recipients were followed up for more than 5 years, with well-functioning transplant kidneys and pancreases. Conclusions Donor pancreas biopsy is relatively safe, and the risk of biopsy-related complications after transplantation is controllable. Comprehensive assessment of donor pancreas quality by combining histological evaluation with the donor's clinical indicators is conducive to improving the accuracy of donor pancreas selection and organ utilization.

Hygroscopicity research has long been a key focus and hot topic in Chinese materia medica（CMM）. Elucidating hygroscopic mechanisms plays a vital role in formulation design， process optimization， and storage condition selection. Hygroscopic models serve as essential tools for characterizing CMM hygroscopic mechanisms， with various types available. The double exponential model is a kinetic mathematical model constructed based on the law of conservation of energy and Fick's first law of diffusion， tailored to the physical properties of CMM extracts. In recent years， this model has been extensively applied to simulate the dynamic moisture absorption behavior of CMM extracts and solid dosage forms under varying humidity conditions. It has revealed the correlation between moisture absorption kinetic parameters and material properties， offering a new perspective for characterizing the moisture uptake behavior of CMM. This paper systematically reviews the application progress of this model in the field of CMM， analyzes its advantages， disadvantages， and challenges in this domain， and explores its potential application trends in other fields. It aims to provide references for elucidating the moisture absorption mechanisms of CMM and researching moisture-proofing technologies， while also offering insights for its broader application in food and polymer materials.

Tissue repair requires the reconstruction of functional vascular networks to restore oxygen and nutrient delivery, eliminate metabolic waste, and maintain tissue homeostasis. Although biochemical factors such as growth factors and cytokines have been extensively studied in vascular regeneration, increasing evidence indicates that mechanical signals from the extracellular matrix (ECM) are equally important regulators of angiogenesis. Matrix stiffness, as a major biomechanical property of the tissue microenvironment, provides critical information that influences endothelial cell (EC) behavior and vascular remodeling through mechanotransduction. However, the biological effects of matrix stiffness are not universally defined as either pro- or anti-angiogenic, but are highly dependent on tissue context, cellular composition, disease progression, and the dynamic changes occurring during tissue repair. Therefore, understanding how matrix stiffness regulates vascular regeneration is essential for developing more precise strategies for tissue engineering and regenerative medicine. In this review, we summarize recent advances in the regulation of angiogenesis by matrix stiffness during tissue repair from multiple perspectives. First, we discuss the direct effects of matrix stiffness on EC behaviors, including differentiation, morphological remodeling, migration, proliferation, barrier maintenance, and vascular network formation. ECs sense mechanical alterations in the surrounding matrix through various mechanosensitive structures, including integrins, focal adhesion complexes, and mechanosensitive ion channels such as Piezo1 and TRPV proteins. These mechanical signals are subsequently transmitted through intracellular pathways involving FAK, RhoA/ROCK, MAPK, and Hippo-YAP/TAZ signaling, leading to cytoskeletal reorganization and transcriptional regulation. Through these mechanisms, matrix stiffness regulates endothelial functional states and determines the balance between regenerative vascular formation and pathological vascular remodeling. Beyond the direct regulation of ECs, we further highlight the importance of stiffness-mediated intercellular communication within the regenerative microenvironment. Matrix stiffness can regulate the behavior and secretory profiles of vascular-associated cells, including mesenchymal stem cells and macrophages, thereby indirectly affecting endothelial function through paracrine mechanisms. Changes in matrix mechanics influence the secretion of angiogenic factors, inflammatory cytokines, and extracellular vesicles from these cells, creating a mechanical regulation network that coordinates vascular regeneration. This perspective extends the traditional concept of matrix stiffness from a physical support structure to an active regulatory signal that integrates multiple cellular responses during tissue repair. Furthermore, we discuss the tissue-specific effects of matrix stiffness in vascular regeneration across different organs, including the aorta, lung, liver, and heart. Although excessive matrix stiffening is frequently associated with fibrosis and vascular dysfunction, the consequences of mechanical changes vary considerably among tissues. Moderate stiffness alterations may support endothelial activation and vascular stabilization in certain regenerative contexts, whereas persistent pathological stiffening can promote inflammation, endothelial dysfunction, and impaired vascular repair. These findings emphasize that the biological meaning of “soft” and “stiff” microenvironments should be interpreted according to specific tissue and experimental conditions rather than as universal mechanical classifications. Finally, we summarize current challenges and future perspectives in this field. A major limitation is the lack of standardized mechanical characterization among different studies, as stiffness measurements are influenced by material properties, testing methods, and experimental conditions, limiting direct comparison across research systems. Moreover, most existing models fail to fully reproduce the dynamic and viscoelastic properties of native ECM during tissue regeneration. Future studies should combine advanced biomaterials with standardized mechanical analysis, organoid models, and multi-omics approaches to establish more accurate mechanical regulatory maps. Overall, this review proposes that matrix stiffness is not merely a structural feature of tissues, but an active biological signal that regulates vascular regeneration through coordinated mechanotransduction and multicellular interactions. A deeper understanding of stiffness-mediated vascular regulation will provide new theoretical insights and therapeutic opportunities for improving tissue repair outcomes.

Background: The hypotension prediction index (HPI) is an algorithm designed to predict hypotension. Some studies have reported that HPI-guided hemodynamic management strategies decrease intraoperative hypotension and complications; however, the effect of HPI on reducing perioperative complications is controversial. This meta-analysis aimed to assess the efficacy of the HPI in reducing major complications and intraoperative hypotension. Methods: We conducted this meta-analysis according to the PRISMA statement and Cochrane Handbook guidelines. A comprehensive literature review was conducted to identify studies focusing on the efficacy of HPI-guided management in reducing intraoperative hypotension and postoperative complications. The PubMed, Embase, Scopus, and Web of Science databases were searched, and the resulting data were combined to calculate the pooled mean differences or risk ratios (RRs) with 95% CIs of both randomized controlled trials (RCTs) and retrospective studies, as appropriate. Heterogeneity and potential publication bias were also assessed. Results: Nineteen articles (12 RCTs and 7 retrospective studies) with 2570 recruited patients were included in this meta-analysis. The critical evaluation of the study quality revealed a low risk of bias in the included RCTs. Among the non-randomized trials, one was rated 7, two were rated 8, and the remaining four were rated 9 on the Newcastle-Ottawa Scale, indicating high quality and a low risk of bias. HPI-guided management significantly reduced intraoperative hypotension and associated major complications (RR = 0.79, 95% CI [0.69–0.90], I2 = 0, P < 0.001). Blood loss and length of hospital stay were comparable between the groups. Conclusions HPI-guided management significantly reduced intraoperative hypotension and major complications.

Body weight abnormalities, including overweight, obesity, and underweight, have become a dual public health challenge in Chinese adults: overweight and obesity lead to a variety of chronic complications, while underweight increases the risks of malnutrition, sarcopenia, and organ dysfunction. To systematically address these issues, multidisciplinary experts in endocrinology, sports science, nutrition, and psychiatry from various regions have held multiple weight management seminars. Based on the latest epidemiological data and clinical evidence, they expanded the guideline to include assessment and intervention strategies for underweight, in addition to the core content of obesity management. This guideline outlines the etiological mechanisms, evaluation methods, and multidimensional management strategies for overweight and obesity, covering key areas such as diagnosis and assessment, medical nutrition therapy, exercise prescription, pharmacological intervention, and psychological support. It is intended to provide a scientific and standardized approach to weight management across the adult population, aiming to curb the rising prevalence of obesity, mitigate complications associated with abnormal body weight, and improve nutritional status and overall quality of life.

Objective:To investigate the clinical distribution characteristics changes in antimicrobial resistance, and carbapenemase resistance genes of Klebsiella pneumoniae isolated from children in Chongqing region during the period of January 2019 to December 2024, providing a basis for the rational use of antibiotics and the prevention and control of nosocomial infections.Methods:An observational study was conducted to retrospectively analyze 5 020 Klebsiella pneumoniae (KP) isolates detected in four hospitals of the Southwest Pediatric Laboratory Specialty Alliance. Antimicrobial susceptibility testing was performed by the minimum inhibitory concentration method combined with the disk diffusion method. Results were interpreted according to the 2024 Clinical and Laboratory Standards Institute (CLSI) standards. Carbapenemase resistance genes were detected by polymerase chain reaction (PCR) combined with Sanger sequencing. WHONET 5.6 was used for resistance analysis and SPSS 19.0 for statistical analysis. The chi-square test was used to assess trends in resistance rates, ESBL detection rates, and resistance rates of different CRKP carbapenemase genotypes from 2019 to 2024. Statistical significance was confirmed if the two-tailed P-value was <0.05. Results:A total of 5 020 strains were isolated, with a detection rate of 5.1% (5 020/99 063). The majority were from sputum (59.2%, 2 970/5 020), followed by pus (17.1%, 857), urine (9.7%, 487), venous blood (6.5%, 326), secretions (2.6%, 130), and other specimens (5.0%, 250).The lowest resistance rate was to amikacin (3.8%), followed by levofloxacin (10.9%), imipenem (19.1%), and meropenem (19.9%). Resistance rates to cefoperazone/sulbactam ( χ2=9.982 0, P=0.001 6), piperacillin/tazobactam ( χ2=10.110 0, P=0.001 5), ceftazidime ( χ2=3.849 0, P=0.049 8), cefotaxime ( χ2=7.605 0, P=0.005 8), cefepime ( χ2=13.510 0, P=0.000 2), aztreonam ( χ2=6.457 0, P=0.011 1), imipenem ( χ2=4.672 0, P=0.030 7), and levofloxacin ( χ2=7.555 0, P=0.006 0) showed an annual increasing trend. The main carbapenemase genes were blaNDM-5 (42.2%, 127/301), blaNDM-1 (33.9%, 102/301), and blaKPC-2 (17.3%, 52/301). Patients with KPC-2-producing strains (median age, 240 days) were older than those with NDM-1/NDM-5-producing strains (median age, 40 days) ( χ2=22.620 0, P<0.000 1). In neonatal wards, the detection rate of NDM-KP was higher than that of KPC-KP (64.6%, 148/229 vs. 26.9%, 14/52, χ2=24.680 0, P<0.000 1), whereas in ICUs, it was lower (6.1%, 14/229 vs. 26.9%, 14/52, χ2=20.450 0, P<0.000 1). Conclusion:In Chongqing region, the isolation rate of K. pneumoniae from sputum was the highest with most cases from neonatal wards. Resistance to carbapenems showed an upward trend. BlaNDM-5 was the predominant genotype in pediatric CRKP. Patients with KPC-KP were older than those with NDM-KP. NDM-KP predominated in neonatal wards, while KPC-KP predominated in ICUs, with KPC-KP showing higher antimicrobial resistance.

Objective To investigate the predictive value of combined radiomic features derived from chest CT scans with clinical characteristics for epidermal growth factor receptor(EGFR)gene mutations in non-small cell lung cancer(NSCLC).Methods A multi-center case-control study was conducted on the clinical data and CT images of 1 070 NSCLC patients from the radiology departments of the 3 medical institutions between January 2013 and October 2023.The 719 NSCLC patients from the First Affiliated Hospital of Army Medical University were randomly divided into a training set and an internal validation set in a ratio of 7∶3;The 173 patients in the Eastern Theatre General Hospital and the 178 patients in Army Medical Centre of PLA were assigned into the external validation set 1 and 2,respectively.Least absolute shrinkage and selection operator(LASSO)regression was employed to identify the optimal radiomic features,which were subsequently used to construct a radiomics model.Univariate and multivariate logistic regression analyses were applied to identify clinical features associated with EGFR mutation,thereby developing a clinical model.The radiomic and clinical features were subsequently combined to develop a comprehensive model.All the 3 classification models were built using random forest(RF)machine learning.The area under curve(AUC),accuracy,sensitivity and specificity were utilized to evaluate the predictive performance of the models.Calibration curve was plotted to assess the goodness of fit of the comprehensive model,while decision curve analysis was performed to assess the clinical utility of the model.Results The AUC value of the radiomics model was 0.762 4(95%CI:0.692 4～0.825 1),0.745 4(95%CI:0.671 1～0.814 3),and 0.724 7(95%CI:0.639 7～0.801 6),respectively,in the internal validation set,external validation set 1,and external validation set 2;The AUC value of the clinical prediction model was 0.691 7(95%CI:0.627 9～0.757 6),0.652 5(95%CI:0.576 7～0.729 1),and 0.779 2(95%CI:0.712 5～0.847 3),respectively in the above sets in turn;The comprehensive model constructed based on clinical features and radiomic features showed the best predictive efficacy,with an AUC value of 0.818 0(95%CI:0.757 7～0.874 3),0.782 4(95%CI:0.703 1～0.848 2),and 0.796 6(95%CI:0.718 1～0.868 6),respectively in the above sets.Calibration curve analysis indicated that the comprehensive model had a good fit,while decision curve analysis revealed that the model provided a favorable net benefit.Conclusion Our comprehensive model constructed based on chest CT radiomic features and clinical characteristics shows superior predictive performance for EGFR gene mutations in NSCLC across multiple center datasets,which may be helpful for clinical decision-making for treatment strategies.

Objective To evaluate the predictive value of deep learning features from chest CT images combined with clinical and radiomics features for epidermal growth factor receptor(EGFR)mutations in non-small cell lung cancer(NSCLC).Methods This case-control study retrospectively analyzed clinical and imaging data of 1 070 NSCLC patients from radiology departments at three hospitals(January 2013 to October 2023).Patients were divided into:a training set(n=502)and internal validation set(n=217)via 7∶3 randomization of 719 cases from the First Affiliated Hospital of Army Medical University;external validation set 1(n=173)from General Hospital of Eastern Theater Command;external validation set 2(n=178)from Daping Hospital of Army Medical University.Deep learning features were extracted using a 2.5D convolutional neural network(CNN)with ResNet101 backbone,radiomics features were derived from CT images,and clinical risk factors were identified to construct models.An integrated model combined deep learning,clinical,and radiomics features.All four models were developed using random forest(RF)classifiers.Calibration curves assessed goodness-of-fit,and decision curve analysis(DCA)evaluated clinical utility.Results The deep learning model achieved AUCs of 0.833 7(95%CI:0.770 6～0.884 7),0.815 1(0.741 6～0.882 8),and 0.810 1(0.745 2～0.873 6)in the internal and two external validation sets,respectively.Clinical models yielded AUCs of 0.731 0(0.660 2～0.802 1),0.746 0(0.666 4～0.824 9),and 0.813 4(0.743 1～0.883 6);radiomics models showed AUCs of 0.762 4(0.692 4～0.825 1),0.745 4(0.671 1～0.814 3),and 0.724 7(0.639 7～0.801 6).The integrated model demonstrated optimal performance with AUCs of 0.905 5(0.857 0～0.945 4),0.832 7(0.763 3～0.896 4),and 0.889 0(0.834 4～0.934 3).DCA indicated significant net benefit for EGFR prediction at threshold probabilities of 0.15～0.85 using the integrated model.Conclusion Deep learning features from CT images effectively predict EGFR mutation status in NSCLC.The integrated model combining deep learning,clinical,and radiomics features further enhances predictive performance.

Background: Nasopharyngeal carcinoma (NPC) is characterized by high programmed death-ligand 1 (PD-L1) expression and abundant infiltration of non-malignant lymphocytes, which renders patients potentially suitable candidates for immune checkpoint blockade therapies. Palate, lung, and nasal epithelium clone (PLUNC) inhibit the growth of NPC cells and enhance cellular apoptosis and differentiation. Currently, the relationship between PLUNC (as a tumor-suppressor) and PD-L1 in NPC is unclear. Methods: We collected clinical samples of NPC to verify the relationship between PLUNC and PD-L1. PLUNC plasmid was transfected into NPC cells, and the variation of PD-L1 was verified by western blot and immunofluorescence. In NPC cells, we verified the relationship of PD-L1, activating transcription factor 3 (ATF3), and β-catenin by western blot and immunofluorescence. Later, we further verified that PLUNC regulates PD-L1 through β-catenin. Finally, the effect of PLUNC on β-catenin was verified by co-immunoprecipitation (Co-IP). Results: We found that PLUNC expression was lower in NPC tissues than in paracancer tissues. PD-L1 expression was opposite to that of PLUNC. Western blot and immunofluorescence showed that β-catenin could upregulate ATF3 and PD-L1, while PLUNC could downregulate ATF3/PD-L1 by inhibiting the expression of β-catenin. PLUNC inhibits the entry of β-catenin into the nucleus. Co-IP experiments demonstrated that PLUNC inhibited the interaction of DEAD-box helicase 17 (DDX17) and β-catenin. Conclusions PLUNC downregulates the expression of PD-L1 by inhibiting the interaction of DDX17/β-catenin in NPC.

Hearing loss is the most prevalent disabling disease. Cochlear implantation（CI） serves as the primary intervention for severe to profound hearing loss. This consensus systematically explores the value of genetic diagnosis in the pre-operative assessment and efficacy prognosis for CI. Drawing upon domestic and international research and clinical experience, it proposes an evidence-based medicine three-tiered prognostic classification system（Favorable, Marginal, Poor）. The consensus focuses on common hereditary non-syndromic hearing loss（such as that caused by mutations in genes like GJB2, SLC26A4, OTOF, LOXHD1） and syndromic hereditary hearing loss（such as Jervell & Lange-Nielsen syndrome and Waardenburg syndrome）, which are closely associated with congenital hearing loss, analyzing the impact of their pathological mechanisms on CI outcomes. The consensus provides recommendations based on multiple round of expert discussion and voting. It emphasizes that genetic diagnosis can optimize patient selection, predict prognosis, guide post-operative rehabilitation, offer stratified management strategies for patients with different genotypes, and advance the application of precision medicine in the field of CI.
Humans ; Cochlear Implantation ; Prognosis ; Hearing Loss/surgery* ; Consensus ; Connexin 26 ; Mutation ; Sulfate Transporters ; Connexins/genetics*