Objective To examine the impact of extreme temperature and drought stress on the survival of Bulinus globosus, so as to provide the theoretical evidence for the genomic research of Bulinus in absence of reference genes. Methods B. globosus snail samples were collected from Kiwani Shehia in Pemba Island, Zanzibar, Tanzania, and offspring snails were obtained through laboratory breeding and reproduction. A total of 120 10-week-old B. globosus snails from the same generation were selected and randomly assigned into four groups, including the high-temperature drought (HD) group, normal temperature drought (D) group, low-temperature drought (LD) group, and the control (C) group, of 30 snails in each group. Snails in HD, D, and LD groups were placed in beakers containing dry soil at the bottom and subsequently housed in climate chambers at 35, 26 ℃, and 10 ℃, respectively, while snails in Group C were maintained in 500 mL petri dishes containing dechlorinated tap water at 26 ℃. Following 3 days of breeding, living snails in each group were collected, and soft tissues were dissected and isolated. Total RNA was extracted from snail soft tissues for library construction, followed by high-throughput sequencing on the Illumina HiSeq 4000 sequencing system. De novo transcriptome assembly was performed using the Trinity software, and the longest transcripts were selected as unigenes. Gene functional annotations of unigenes were conducted using the Diamond software against Gene Ontology (GO) knowledgebase, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database, NCBI non-redundant (NR) protein sequences database, Protein Family (Pfam) database, and UniProtKB/Swiss-Prot (Swiss-Prot) knowledgebase. GO and KEGG enrichment analyses of differentially expressed genes (DEGs) were performed using the topGO and clusterProfiler software, respectively. In addition, four relevant genes were selected for validation using a real-time quantitative PCR (qRT-PCR) assay to verify the reliability of transcriptome sequencing results. Results Following 3 days of breeding, there were 7, 20, 28, and 30 survival B. globosus snails in HD, LD, D, and C groups, with corresponding survival rates of 23.33% (7/30), 66.67% (20/30), 93.33% (28/30), and 100.00% (30/30), respectively (χ² = 52.72, P < 0.001). De novo transcriptome assembly generated 176 942 unigenes, with annotation rates of 0.98%, 13.49%, 26.46%, 12.48%, and 14.39% against GO knowledgebase, KEGG pathway database, NR protein sequences database, Pfam database, and Swiss-Prot knowledgebase, respectively. There were 33 up-regulated and 72 down-regulated genes in Group D, 483 up-regulated and 815 down-regulated genes in Group HD, and 245 up-regulated and 172 down-regulated genes in Group LD relative to in Group C. Following removal of overlapping genes across groups and unmatched genes, 11 candidate genes were identified. GO and KEGG analyses revealed 3 heat shock protein (HSP)-related DEGs in these 11 candidate genes, which were annotated as HSP12.2, HSP70, and HSP20 genes and were all significantly up-regulated in each treatment group. Three immune and nervous system-related DEGs were identified, and were all significantly down-regulated in each treatment group, which were involved in the neural cell adhesion molecule L1-like protein pathway, fibrinogen binding protein pathway, and leukocyte elastase inhibitor-like protein pathway. qRT-PCR assay quantified that the expression trends of four genes related to temperature and drought stress across different treatment groups were highly consistent with transcriptome sequencing data. Conclusion The survival rate of B. globosus significantly reduces under combined stresses of extreme temperature and drought, possibly due to an imbalance in its cellular homeostasis regulatory system.

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.

With the continuous deepening of the concept of precision diagnosis and treatment for lung cancer, how to achieve higher efficiency and accuracy in the screening, diagnosis, and treatment pathways in clinical practice has become an important issue that urgently needs to be overcome. The current clinical difficulty lies in the fact that despite continuous advancements in imaging and molecular diagnostic technologies, there are still limitations in manual efficiency and subjective experience when it comes to massive data analysis and multi-scale feature extraction. Artificial intelligence (AI), especially algorithm systems based on deep learning, is an innovative technology capable of deeply empowering medical big data. This method utilizes algorithms such as convolutional neural networks, combined with radiomics, pathomics, and multi-modal data fusion analysis, demonstrating immense potential in early precise detection and benign-malignant differentiation of pulmonary nodules, digital pathological subtype recognition and non-invasive prediction of driver genes, precise 3D surgical planning and automatic delineation of radiotherapy target volumes, as well as dynamic risk warning during follow-up. This innovative technology provides a brand-new solution for realizing intelligent and individualized lung cancer diagnosis and treatment models. This consensus, based on the latest evidence from evidence-based medicine and combined with the development trends in the AI field and real-world clinical needs, was ultimately formed by gathering the consensus opinions of multidisciplinary experts in radiology, pathology, thoracic surgery, and other fields. The main content covers the application specifications of AI in the three core scenarios of lung cancer screening, diagnosis, and treatment, the technical standards for data collection and algorithm validation, as well as the ethical and regulatory challenges faced at the current stage. It aims to clarify the applicable boundaries of AI as a clinical auxiliary decision support tool, providing scientific guidance and standardized exploration directions for peers currently engaged in or planning to carry out AI-assisted clinical diagnosis, treatment, and translation of lung cancer.

To address the growing challenge of an increasing number of elderly lung cancer patients amidst China's aging population and to fill the gap in quality control standards for surgical treatment in this special population, this study aimed to develop a standardized surgical evaluation system for elderly lung cancer patients tailored to China's national conditions. The system was established through a literature review, integrated the pathophysiological characteristics of elderly patients, and was constructed following review, feedback, and revision by experts from multiple thoracic surgery centers. Employing a 100-point scoring system, it comprises three primary domains: physical infrastructure and geriatric adaptability foundational conditions (10 points); management level and perioperative care models (20 points); and technical proficiency and clinical outcomes (70 points). The system places a strong emphasis on geriatric adaptability, proposing specific, quantifiable indicators for age-friendly facility modifications, control of elderly-specific complications, multidisciplinary collaboration, and standardized perioperative management. It provides a convenient and measurable assessment tool for quality control in the surgical treatment of elderly lung cancer in China, which is expected to promote the standardization and homogenization of diagnosis and treatment.

Objective: To explore the association between maternal infection during pregnancy and infant allergic diseases, so as to provide the evidence for stratified management of maternal infection during pregnancy and early prevention of infant allergic diseases. Methods: From January 2024 to June 2025, pregnant women who underwent prenatal examination and established files at Shiyan Maternal and Child Health Hospital were selected as study subjects. Data on pregnant women and newborns were collected through questionnaire survey. Maternal infection during pregnancy was collected through prenatal examination, review of outpatient/inpatient electronic medical record system, and regular telephone follow-up, and the types, timing, severity of maternal infection, and antibiotic usage were recorded. Infant allergic diseases within 12 months after birth were assessed by pediatricians through outpatient follow-up combined with telephone follow-up. Multivariate logistic regression model was used to analyze the association between maternal infection during pregnancy and infant allergic diseases. Results: A total of 235 pregnant women were surveyed, with a mean age of (28.47±2.96) years and pre-pregnancy body mass index of (23.22±1.63) kg/m2. There were 82 cases of maternal infection during pregnancy, accounting for 34.89%. After 12 months of follow-up, 73 infants developed allergic diseases, with an incidence of 31.06%. Among them, the incidence of allergic diseases in infants with maternal infection during pregnancy was 43.90%, and that in infants without maternal infection during pregnancy was 24.18%, with a statistically significant difference (P<0.05). Multivariable logistic regression analysis showed that after adjusting for age, pre-pregnancy body mass index, newborn gender, and birth weight, the risk of allergic diseases was higher in infants with maternal infection during pregnancy (OR=2.787, 95%CI: 1.462-5.314). Stratified analysis showed that the risk of allergic diseases was higher in infants with maternal respiratory tract infection during pregnancy (OR=2.654, 95%CI: 1.285-5.481), maternal infection during the second and third trimesters of pregnancy (second trimester, OR=2.593, 95%CI: 1.085-6.196; third trimester, OR=2.845, 95%CI: 1.298-6.236), and mild maternal infection during pregnancy (OR=2.463, 95%CI: 1.248-4.860). Conclusion Maternal infection during pregnancy can increase the risk of infant allergic diseases, and is related to the types, timing, and severity.

Background/Aims: Early detection and effective prognosis prediction in patients with hepatocellular carcinoma (HCC) provide an avenue for survival improvement, yet more effective approaches are greatly needed. We sought to develop the detection and prognosis models with ultra-sensitivity and low cost based on fragmentomic features of circulating cell free mtDNA (ccf-mtDNA). Methods: Capture-based mtDNA sequencing was carried out in plasma cell-free DNA samples from 1168 participants, including 571 patients with HCC, 301 patients with chronic hepatitis B or liver cirrhosis (CHB/LC) and 296 healthy controls (HC). Results: The systematic analysis revealed significantly aberrant fragmentomic features of ccf-mtDNA in HCC group when compared with CHB/LC and HC groups. Moreover, we constructed a random forest algorithm-based HCC detection model by utilizing ccf-mtDNA fragmentomic features. Both internal and two external validation cohorts demonstrated the excellent capacity of our model in distinguishing early HCC patients from HC and highrisk population with CHB/LC, with AUC exceeding 0.983 and 0.981, sensitivity over 89.6% and 89.61%, and specificity over 98.20% and 95.00%, respectively, greatly surpassing the performance of alpha-fetoprotein (AFP) and mtDNA copy number. We also developed an HCC prognosis prediction model by LASSO-Cox regression to select 20 fragmentomic features, which exhibited exceptional ability in predicting 1-year, 2-year and 3-year survival (AUC=0.8333, 0.8145 and 0.7958 for validation cohort, respectively). Conclusions We have developed and validated a high-performing and low-cost approach in a large clinical cohort based on aberrant ccf-mtDNA fragmentomic features with promising clinical translational application for the early detection and prognosis prediction of HCC patients.

ObjectiveWith the epidermal growth factor receptor（EGFR）/Signal Transducers and Activators of Transcription（STAT3）/Hexokinase 2（HK2） signaling pathway in atherosclerosis （AS） and ovarian cancer （OC） as the entry point， this paper discusses the molecular mechanism of Gypenoside L （Gyp-L） treating AS and OC with different diseases， provides a new perspective and theoretical basis for TCM treating AS and OC with EGFR-STAT3-HK2 pathway， and enriches the scientific connotation of the theory of "cytoskeleton in the heart". MethodsCCK-8 was used to detect the proliferation of HUVEC and OVCAR-3 cells， in order to determine the intervention concentration for subsequent experiments. The colorimetric method was used to detect the NO content in HUVEC and the contents of pyruvate and LDH in two cell lines. Cell cloning experiments and scratch experiments reflect the proliferation and migration ability of OVCAR-3 cells. Western blot was used to detect the expression levels of relevant proteins. Furthermore， two cell models overexpressing EGFR were constructed and co treated with Gyp-L. HUVEC cells were divided into control， ox-LDL， OE-NC， OE-EGFR， OE-NC+Gyp-L， and OE-EGFR+Gyp-L group. OVCAR-3 cells were divided into control， OE-NC， OE-EGFR ， OE-NC+Gyp-L， and OE-EGFR+Gyp-L group. The colorimetric method was used to detect the NO content in HUVEC and the contents of pyruvate and LDH in two cell lines. Western blot was used to detect the expression levels of EGFR-STAT3-HK2 pathway related proteins. Cell cloning experiments and scratch experiments reflect the proliferation and migration ability of OVCAR-3 cells. ResultsGyp-L can significantly reduce the NO content of HUVEC and the pyruvate and LDH content of two cell lines （P<0.05）； Inhibit the proliferation and migration ability of OVCAR-3 cells； Reduce the expression levels of EGFR/STAT3/HK2 pathway related proteins in HUVEC and OVCAR-3 cell lines （P<0.05）， and inhibit the glycolysis pathway. ConclusionGyp-L can inhibit glycolysis in HUVEC and OVCAR-3 cells through the EGFR/STAT3/HK2 pathway，thereby suppressing the occurrence and development of AS and OC.

ObjectiveWith the epidermal growth factor receptor（EGFR）/Signal Transducers and Activators of Transcription（STAT3）/Hexokinase 2（HK2） signaling pathway in atherosclerosis （AS） and ovarian cancer （OC） as the entry point， this paper discusses the molecular mechanism of Gypenoside L （Gyp-L） treating AS and OC with different diseases， provides a new perspective and theoretical basis for TCM treating AS and OC with EGFR-STAT3-HK2 pathway， and enriches the scientific connotation of the theory of "cytoskeleton in the heart". MethodsCCK-8 was used to detect the proliferation of HUVEC and OVCAR-3 cells， in order to determine the intervention concentration for subsequent experiments. The colorimetric method was used to detect the NO content in HUVEC and the contents of pyruvate and LDH in two cell lines. Cell cloning experiments and scratch experiments reflect the proliferation and migration ability of OVCAR-3 cells. Western blot was used to detect the expression levels of relevant proteins. Furthermore， two cell models overexpressing EGFR were constructed and co treated with Gyp-L. HUVEC cells were divided into control， ox-LDL， OE-NC， OE-EGFR， OE-NC+Gyp-L， and OE-EGFR+Gyp-L group. OVCAR-3 cells were divided into control， OE-NC， OE-EGFR ， OE-NC+Gyp-L， and OE-EGFR+Gyp-L group. The colorimetric method was used to detect the NO content in HUVEC and the contents of pyruvate and LDH in two cell lines. Western blot was used to detect the expression levels of EGFR-STAT3-HK2 pathway related proteins. Cell cloning experiments and scratch experiments reflect the proliferation and migration ability of OVCAR-3 cells. ResultsGyp-L can significantly reduce the NO content of HUVEC and the pyruvate and LDH content of two cell lines （P<0.05）； Inhibit the proliferation and migration ability of OVCAR-3 cells； Reduce the expression levels of EGFR/STAT3/HK2 pathway related proteins in HUVEC and OVCAR-3 cell lines （P<0.05）， and inhibit the glycolysis pathway. ConclusionGyp-L can inhibit glycolysis in HUVEC and OVCAR-3 cells through the EGFR/STAT3/HK2 pathway，thereby suppressing the occurrence and development of AS and OC.

Background/Aims: Early detection and effective prognosis prediction in patients with hepatocellular carcinoma (HCC) provide an avenue for survival improvement, yet more effective approaches are greatly needed. We sought to develop the detection and prognosis models with ultra-sensitivity and low cost based on fragmentomic features of circulating cell free mtDNA (ccf-mtDNA). Methods: Capture-based mtDNA sequencing was carried out in plasma cell-free DNA samples from 1168 participants, including 571 patients with HCC, 301 patients with chronic hepatitis B or liver cirrhosis (CHB/LC) and 296 healthy controls (HC). Results: The systematic analysis revealed significantly aberrant fragmentomic features of ccf-mtDNA in HCC group when compared with CHB/LC and HC groups. Moreover, we constructed a random forest algorithm-based HCC detection model by utilizing ccf-mtDNA fragmentomic features. Both internal and two external validation cohorts demonstrated the excellent capacity of our model in distinguishing early HCC patients from HC and highrisk population with CHB/LC, with AUC exceeding 0.983 and 0.981, sensitivity over 89.6% and 89.61%, and specificity over 98.20% and 95.00%, respectively, greatly surpassing the performance of alpha-fetoprotein (AFP) and mtDNA copy number. We also developed an HCC prognosis prediction model by LASSO-Cox regression to select 20 fragmentomic features, which exhibited exceptional ability in predicting 1-year, 2-year and 3-year survival (AUC=0.8333, 0.8145 and 0.7958 for validation cohort, respectively). Conclusions We have developed and validated a high-performing and low-cost approach in a large clinical cohort based on aberrant ccf-mtDNA fragmentomic features with promising clinical translational application for the early detection and prognosis prediction of HCC patients.

Background/Aims: Early detection and effective prognosis prediction in patients with hepatocellular carcinoma (HCC) provide an avenue for survival improvement, yet more effective approaches are greatly needed. We sought to develop the detection and prognosis models with ultra-sensitivity and low cost based on fragmentomic features of circulating cell free mtDNA (ccf-mtDNA). Methods: Capture-based mtDNA sequencing was carried out in plasma cell-free DNA samples from 1168 participants, including 571 patients with HCC, 301 patients with chronic hepatitis B or liver cirrhosis (CHB/LC) and 296 healthy controls (HC). Results: The systematic analysis revealed significantly aberrant fragmentomic features of ccf-mtDNA in HCC group when compared with CHB/LC and HC groups. Moreover, we constructed a random forest algorithm-based HCC detection model by utilizing ccf-mtDNA fragmentomic features. Both internal and two external validation cohorts demonstrated the excellent capacity of our model in distinguishing early HCC patients from HC and highrisk population with CHB/LC, with AUC exceeding 0.983 and 0.981, sensitivity over 89.6% and 89.61%, and specificity over 98.20% and 95.00%, respectively, greatly surpassing the performance of alpha-fetoprotein (AFP) and mtDNA copy number. We also developed an HCC prognosis prediction model by LASSO-Cox regression to select 20 fragmentomic features, which exhibited exceptional ability in predicting 1-year, 2-year and 3-year survival (AUC=0.8333, 0.8145 and 0.7958 for validation cohort, respectively). Conclusions We have developed and validated a high-performing and low-cost approach in a large clinical cohort based on aberrant ccf-mtDNA fragmentomic features with promising clinical translational application for the early detection and prognosis prediction of HCC patients.