Incretin-based drugs have demonstrated significant clinical value in weight management and the treatment of metabolic diseases by mimicking or enhancing endogenous incretin signaling. These agents primarily target multiple receptors including glucagon-like peptide-1， glucose-dependent insulinotropic polypeptide， and glucagon， exerting weight-reducing effects through multiple mechanisms such as central appetite regulation， gastrointestinal function modulation， and peripheral metabolic improvement， while further ameliorating blood glucose， blood lipids， fatty liver， and cardiorenal metabolic risks. This review focuses on incretin-based drugs， systematically summarizing the key clinical application points and relevant research progress of domestically marketed and investigational candidate drugs in the field of weight reduction. Available evidence indicates that semaglutide and tirzepatide demonstrate substantial weight reduction with multiple metabolic benefits； domestic innovative drugs represented by mazdutide and ecnoglutide also exhibit favorable weight-reducing effects and safety profiles. Regarding safety， these agents are generally well-tolerated， with the most common adverse reactions being mild-to-moderate gastrointestinal events， while potential risks including gallbladder events， pancreatitis， and mood changes require attention. In special populations， individualized assessment is warranted for use in children， elderly people， and pregnant women. Future research should focus on the screening of predictive biomarkers for efficacy， the development of long-acting oral formulations， and the accumulation of long-term safety follow-up data.

Purpose: The genomic characteristics of uterine sarcomas have not been fully elucidated. This study aimed to explore the genomic landscape of the uterine sarcomas (USs). Materials and Methods: Comprehensive genomic analysis through RNA-sequencing was conducted. Gene fusion, differentially expressed genes (DEGs), signaling pathway enrichment, immune cell infiltration, and prognosis were analyzed. A deep learning model was constructed to predict the survival of US patients. Results: A total of 71 US samples were examined, including 47 endometrial stromal sarcomas (ESS), 18 uterine leiomyosarcomas (uLMS), three adenosarcomas, two carcinosarcomas, and one uterine tumor resembling an ovarian sex-cord tumor. ESS (including high-grade ESS [HGESS] and low-grade ESS [LGESS]) and uLMS showed distinct gene fusion signatures; a novel gene fusion site, MRPS18A–PDC-AS1 could be a potential diagnostic marker for the pathology differential diagnosis of uLMS and ESS; 797 and 477 uterine sarcoma DEGs (uDEGs) were identified in the ESS vs. uLMS and HGESS vs. LGESS groups, respectively. The uDEGs were enriched in multiple pathways. Fifteen genes including LAMB4 were confirmed with prognostic value in USs; immune infiltration analysis revealed the prognositic value of myeloid dendritic cells, plasmacytoid dendritic cells, natural killer cells, macrophage M1, monocytes and hematopoietic stem cells in USs; the deep learning model named Max-Mean Non-Local multi-instance learning (MMN-MIL) showed satisfactory performance in predicting the survival of US patients, with the area under the receiver operating curve curve reached 0.909 and accuracy achieved 0.804. Conclusion USs harbored distinct gene fusion characteristics and gene expression features between HGESS, LGESS, and uLMS. The MMN-MIL model could effectively predict the survival of US patients.

Purpose: The genomic characteristics of uterine sarcomas have not been fully elucidated. This study aimed to explore the genomic landscape of the uterine sarcomas (USs). Materials and Methods: Comprehensive genomic analysis through RNA-sequencing was conducted. Gene fusion, differentially expressed genes (DEGs), signaling pathway enrichment, immune cell infiltration, and prognosis were analyzed. A deep learning model was constructed to predict the survival of US patients. Results: A total of 71 US samples were examined, including 47 endometrial stromal sarcomas (ESS), 18 uterine leiomyosarcomas (uLMS), three adenosarcomas, two carcinosarcomas, and one uterine tumor resembling an ovarian sex-cord tumor. ESS (including high-grade ESS [HGESS] and low-grade ESS [LGESS]) and uLMS showed distinct gene fusion signatures; a novel gene fusion site, MRPS18A–PDC-AS1 could be a potential diagnostic marker for the pathology differential diagnosis of uLMS and ESS; 797 and 477 uterine sarcoma DEGs (uDEGs) were identified in the ESS vs. uLMS and HGESS vs. LGESS groups, respectively. The uDEGs were enriched in multiple pathways. Fifteen genes including LAMB4 were confirmed with prognostic value in USs; immune infiltration analysis revealed the prognositic value of myeloid dendritic cells, plasmacytoid dendritic cells, natural killer cells, macrophage M1, monocytes and hematopoietic stem cells in USs; the deep learning model named Max-Mean Non-Local multi-instance learning (MMN-MIL) showed satisfactory performance in predicting the survival of US patients, with the area under the receiver operating curve curve reached 0.909 and accuracy achieved 0.804. Conclusion USs harbored distinct gene fusion characteristics and gene expression features between HGESS, LGESS, and uLMS. The MMN-MIL model could effectively predict the survival of US patients.

Purpose: The genomic characteristics of uterine sarcomas have not been fully elucidated. This study aimed to explore the genomic landscape of the uterine sarcomas (USs). Materials and Methods: Comprehensive genomic analysis through RNA-sequencing was conducted. Gene fusion, differentially expressed genes (DEGs), signaling pathway enrichment, immune cell infiltration, and prognosis were analyzed. A deep learning model was constructed to predict the survival of US patients. Results: A total of 71 US samples were examined, including 47 endometrial stromal sarcomas (ESS), 18 uterine leiomyosarcomas (uLMS), three adenosarcomas, two carcinosarcomas, and one uterine tumor resembling an ovarian sex-cord tumor. ESS (including high-grade ESS [HGESS] and low-grade ESS [LGESS]) and uLMS showed distinct gene fusion signatures; a novel gene fusion site, MRPS18A–PDC-AS1 could be a potential diagnostic marker for the pathology differential diagnosis of uLMS and ESS; 797 and 477 uterine sarcoma DEGs (uDEGs) were identified in the ESS vs. uLMS and HGESS vs. LGESS groups, respectively. The uDEGs were enriched in multiple pathways. Fifteen genes including LAMB4 were confirmed with prognostic value in USs; immune infiltration analysis revealed the prognositic value of myeloid dendritic cells, plasmacytoid dendritic cells, natural killer cells, macrophage M1, monocytes and hematopoietic stem cells in USs; the deep learning model named Max-Mean Non-Local multi-instance learning (MMN-MIL) showed satisfactory performance in predicting the survival of US patients, with the area under the receiver operating curve curve reached 0.909 and accuracy achieved 0.804. Conclusion USs harbored distinct gene fusion characteristics and gene expression features between HGESS, LGESS, and uLMS. The MMN-MIL model could effectively predict the survival of US patients.

With the development of society and changes in people's lifestyles, diabetes mellitus (DM) has become a major risk factor threatening human health, with its incidence rate increasing year by year. Based on the theory of "excessive fire consumes qi while mild fire generates qi", combined with the pathological mechanism and symptom characteristics of DM complicated with heart failure (HF), it is considered that deficiency and insufficiency of "less fire" and excessive "strong fire" consume qi are the fundamental pathogenesis of the occurrence and development of the two diseases. Phlegm, dampness, blood stasis and water are all from this, which further aggravates the disease. The basic treatment principle of "tonifying qi, activating yang, supporting Shao fire, clearing heat, detoxifying and strengthening fire" was proposed. Combined with the different emphasis of patients' syndromes, the treatment methods of promoting blood circulation and removing blood stasis, diuresis and phlegm were supplemented, in order to improve the clinical symptoms of patients and improve the survival rate.

Artificial intelligence （AI） and population pharmacokinetics （PPK） technologies have demonstrated significant potential in the personalized medication of immunosuppressants after organ transplantation, enabling precise prediction of drug dosages. This article provides a comprehensive review of the application status of AI and PPK in the individualized administration of immunosuppressants after organ transplantation， focuses on monitoring blood drug concentration， predicting efficacy/adverse reactions， and establishing individualized dosing models for organ transplant recipients after immunosuppressant administration， and analyzes and compares the application characteristics of different methods in different organ transplant patients as well as the integration and future development of AI and PPK technologies. AI and PPK technologies can not only significantly reduce the dependence on human resources， but also greatly improve the level of individualized treatment of immunosuppressants after organ transplantation， and reduce the discomfort and burden caused by frequent blood concentration monitoring to patients.

Objective To compare the efficacy and safety of different cyclin-dependent kinase 4/6 inhibitors (CDK4/6i) combined with endocrine therapy (ET) for the treatment of hormone receptor-positive (HR+)/human epidermal growth factor receptor 2-negative (HER2−) advanced or metastatic breast cancer. Methods Randomized controlled trials (RCTs) on CDK4/6i for the treatment of HR+/HER2− metastatic or advanced breast cancer were retrieved from databases including PubMed, EMbase, Web of Science, The Cochrane Library, CNKI, Wanfang, VIP, and SinoMed, with the search period ranging from database inception to August 2023. Bayesian network meta-analysis was conducted using R 4.2.0 software. Results A total of 18 RCTs from 25 articles, involving 8 031 patients and 11 treatment regimens, were included. There was no significant difference in progression-free survival (PFS) or overall survival (OS) among different CDK4/6i+ET combinations. The highest cumulative probability for PFS was observed with dalpiciclib (DAL)+fulvestrant (FUL), while ribociclib (RIB)+FUL ranked first for OS. In terms of efficacy, abemaciclib (ABE)+aromatase inhibitors (AI) and ABE+FUL ranked first in objective response rate and clinical benefit rate, respectively. Regarding safety, statistically significant difference in grade 3-4 adverse events was observed among certain types of CDK4/6i (P<0.05). Conclusion Current evidence suggests that CDK4/6i+ET is superior to ET alone for the treatment of HR+/HER2− advanced/metastatic breast cancer. Different CDK4/6i+ET combinations demonstrate comparable or similar efficacy; however, the incidence of adverse reactions is higher with combination therapy. Treatment regimens should be selected based on individual conditions.

Glucagon-like peptide-1 （GLP-1） receptor agonists are a novel class of antidiabetic drugs that also possess lipid- lowering and cardiovascular protective effects， with liraglutide and semaglutide being their representative medications. Based on a systematic literature search， this review summarizes the lipid-lowering mechanisms by which liraglutide and semaglutide exert direct effects on the liver and kidney （regulating autophagy， key lipid metabolism pathways， reverse cholesterol transport， etc.）， direct actions on adipose tissue （affecting adipocyte proliferation and differentiation， expression of lipid metabolism proteins， and gene transcription）， activation of sympathetic pathways through the central nervous system， and modulation of the gut microbiota. Additionally， it summarizes the clinical evidence of their lipid-lowering effects in populations with type 2 diabetes mellitus， overweight individuals， and others. These findings indicate that GLP-1 receptor agonists exert lipid-lowering effects by acting on multiple tissues or systems， providing crucial evidence for further elucidating the molecular mechanisms of these drugs in lipid regulation and exploring potential new ideas for their clinical applications.

Objective To explore the expert consensus on perinatal care management of infants with congenital heart disease(hereinafter referred to as"Consensus")in order to promote the standardization of integrated nursing.Methods The literature was systematically searched and several discussions were organized within the group to compile the first draft of the Consensus.From January to March 2024,20 experts in the clinical nursing,nursing management,clinical medicine and other fields of congenital heart disease were solicited through 2 rounds of Delphi,and 8 experts were invited to conduct a validation to revise the items to form the final Consensus.Results The recovery rates of the 2 rounds of questionnaires were 100％;the experts'authority coefficient was 0.89;the Kendall's W were 0.172,0.211,with statistical significance(P＜0.05).The Consensus included 8 first-level subjects,namely prenatal examination and consultation,postpartum screening,standardized referral,preoperative nursing,intraoperative nursing,postoperative nursing,other disease screening,health education and discharge follow-up.Conclusion The Consensus is scientific and rigorous,and it can provide a reference basis for clinical nursing staff to carry out the care and management of newborns with congenital heart disease.

Objective:To investigate the impact of different target sites, number of treatments, and age on setup errors in dual-isocenter radiotherapy for breast cancer, and to provide a basis for planning target volume (PTV) margin expansion.Methods:A retrospective analysis was conducted on data from 15 patients with left-sided breast cancer who underwent dual-isocenter breath-hold radiotherapy in the Department of Radiotherapy Oncology at the Second Hospital of Hebei Medical University from May 2021 to May 2023. Setup errors were acquired using a Varian TrueBeam STX linear accelerator. Patients were grouped by target site (supraclavicular/chest wall), treatment phase (early/late), and age (younger/older). Non-parametric tests were used to analyze differences in setup errors in : vertical (Vrt), longitudinal (Lng), lateral (Lat) directions, and pitch, roll, and rotation (Rtn) angles. The formula proposed by van Herk was applied to calculate PTV margins.Results:The Vrt direction setup error in the supraclavicular region (0.2 cm) was smaller than that in the chest wall region (0.26 cm), but errors and margin expansions in other directions were larger ( P<0.05 for Lng and Lat directions). No significant correlation was observed in Vrt direction errors between the two sites ( P=0.062), while significant correlations were found in the other directions and angles (all P<0.05). As treatment progressed, setup errors increased in the Vrt and Rtn directions for the supraclavicular region, and in the Vrt, Lng, Lat directions and Rtn angle for the chest wall region. Among these, only the increase in Lat direction error for the chest wall region was statistically significant ( P=0.028). The PTV margins in the late phase group (except for the Lat direction of the supraclavicular region) were greater than or equal to those in the early phase group. Elderly patients had significantly larger setup errors than younger patients in Vrt, Lng, and Lat directions for the supraclavicular region, as well as in Vrt and Lat directions for the chest wall region (all P<0.05). Conclusions:In dual-isocenter radiotherapy for breast cancer, the supraclavicular region requires larger PTV margins than the chest wall region, and elderly patients require greater margins overall. Mid-course rescanning is recommended. If cone-beam CT guidance cannot be ensured for every session, expansion of PTV margins should be considered for the supraclavicular region and elderly patients to reduce the risk of geographic miss.