Congenital heart disease (CHD) is a congenital malformation resulting from abnormal embryonic development of the heart and great vessels, accounting for approximately 25% of all congenital malformations. Children with CHD are often complicated by short stature. Although surgical treatment can improve their growth and development to a certain extent, some children still experience growth retardation after surgery. Recombinant human growth hormone (rhGH) is the main drug for treating short stature, but its efficacy and safety in the treatment of patients with concomitant CHD warrant further investigation. This article reports six cases of children with CHD and short stature who were treated with rhGH. Through a literature review, we summarize and discuss the therapeutic efficacy, follow-up experiences, and adverse reactions of rhGH treatment, aiming to provide references for clinicians in applying rhGH to treat patients with CHD and short stature.

With the widespread use of antibiotics, drug-resistant bacterial infections have become a significant threat to human health. Finding new antibacterial strategies that can effectively control drug-resistant bacterial infections has become an urgent task. Unlike small molecule drugs that target bacterial proteins, antisense oligonucleotide (ASO) can target genes related to bacterial resistance, pathogenesis, growth, reproduction and biofilm formation. By regulating the expression of these genes, ASO can inhibit or kill bacteria, providing a novel approach for the development of antibacterial drugs. To overcome the challenge of delivering antisense oligonucleotide into bacterial cells, various drug delivery systems have been applied in this field, including cell-penetrating peptides, lipid nanoparticles and inorganic nanoparticles, which have injected new momentum into the development of antisense oligonucleotide in the antibacterial realm. This review summarizes the current development of small nucleic acid drugs, the antibacterial mechanisms, targets, sequences and delivery vectors of antisense oligonucleotide, providing a reference for the research and development of antisense oligonucleotide in the treatment of bacterial infections.

Objective To evaluate the performance of the artificial intelligence (AI)-enabled snail identification system for recognition of Oncomelania hupensis robertsoni and Tricula in schistosomiasis-endemic areas of Yunnan Province. Methods Fifty O. hupensis robertsoni and 50 Tricula samples were collected from Yongbei Township, Yongsheng County, Lijiang City, a schistosomiasis-endemic area in Yunnan Province in May 2024. A total of 100 snail sample images were captured with smartphones, including front-view images of 25 O. hupensis robertsoni and 25 Tricula samples (upward shell opening) and back-view images of 25 O. hupensis robertsoni and 25 Tricula samples (downward shell opening). Snail samples were identified as O. hupensis robertsoni or Tricula by schistosomiasis control experts with a deputy senior professional title and above according to image quality and morphological characteristics. A standard dataset for snail image classification was created, and served as a gold standard for recognition of snail samples. A total of 100 snail sample images were recognized with the AI-enabled intelligent snail identification system based on a WeChat mini program in smartphones. Schistosomiasis control professionals were randomly sampled from stations of schistosomisis prevention and control and centers for disease control and prevention in 18 schistosomiasis-endemic counties (districts, cities) of Yunnan Province, for artificial identification of 100 snail sample images. All professionals are assigned to two groups according the median years of snail survey experiences, and the effect of years of snail survey experiences on O. hupensis robertsoni sample image recognition was evaluated. A receiver operating characteristic (ROC) curve was plotted, and the sensitivity, specificity, accuracy, Youden’s index and the area under the curve (AUC) of the AI-enabled intelligent snail identification system and artificial identification were calculated for recognition of snail sample images. The snail sample image recognition results of AI-enabled intelligent snail identification system and artificial identification were compared with the gold standard, and the internal consistency of artificial identification results was evaluated with the Cronbach’s coefficient alpha. Results A total of 54 schistosomiasis control professionals were sampled for artificial identification of snail sample image recognition, with a response rate of 100% (54/54), and the accuracy, sensitivity, specificity, Youden’s index, and AUC of artificial identification were 90%, 86%, 94%, 0.80 and 0.90 for recognition of snail sample images, respectively. The overall Cronbach’s coefficient alpha of artificial identification was 0.768 for recognition of snail sample images, and the Cronbach’s coefficient alpha was 0.916 for recognition of O. hupensis robertsoni snail sample images and 0.925 for recognition of Tricula snail sample images. The overall accuracy of artificial identification was 90% for recognition of snail sample images, and there was no significant difference in the accuracy of artificial identification for recognition of O. hupensis robertsoni (86%) and Tricula snail sample images (94%) (χ² = 1.778, P > 0.05). There was no significant difference in the accuracy of artificial identification for recognition of snail sample images with upward (88%) and downward shell openings (92%) (χ² = 0.444, P > 0.05), and there was a significant difference in the accuracy of artificial identification for recognition of snail sample images between schistosomiasis control professionals with snail survey experiences of 6 years and less (75%) and more than 6 years (90%) (χ² = 7.792, P < 0.05). The accuracy, sensitivity, specificity and AUC of the AI-enabled intelligent snail identification system were 88%, 100%, 76% and 0.88 for recognition of O. hupensis robertsoni snail sample images, and there was no significant difference in the accuracy of recognition of O. hupensis robertsoni snail sample images between the AI-enabled intelligent snail identification system and artificial identification (χ² = 0.204, P > 0.05). In addition, there was no significant difference in the accuracy of artificial identification for recognition of snail sample images with upward (90%) and downward shell openings (86%) (χ² = 0.379, P > 0.05), and there was a significant difference in the accuracy of artificial identification for recognition of snail sample images between schistosomiasis control professionals with snail survey experiences of 6 years and less and more than 6 years (χ² = 5.604, P_adjusted < 0.025). Conclusions The accuracy of recognition of snail sample images is comparable between the AI-enabled intelligent snail identification system and artificial identification by schistosomiasis control professionals, and the AI-enabled intelligent snail identification system is feasible for recognition of O. hupensis robertsoni and Tricula in Yunnan Province.

Objective To investigate the efficacy and safety of the Corheart 6 left ventricular assist system in patients with end-stage heart failure. Methods A retrospective study was conducted on patients with end-stage heart failure who were treated with Corheart 6 left ventricular assist system from March 2022 to June 2024 in 4 hospitals in Jiangsu Province. The efficacy of the device was evaluated by comparing changes in clinical indicators at preoperative, discharge, 3-month postoperative, and 6-month postoperative timepoints, including the New York Heart Association (NYHA) functional classification, left ventricular ejection fraction (LVEF), and left ventricular end-diastolic diameter (LVEDD). The safety of the device was assessed by analyzing the intraoperative position and orientation of the blood pump inlet cannula, as well as the incidence of adverse events. Results In this study, 39 patients were collected, including 34 males and 5 females with a mean age of (56.4±12.5) years, ranging from 20 to 75 years. There was no operative death. There was no death in postoperative 3 months with a survival rate of 100.0%. There were 3 deaths in 6 months postoperatively, with a survival rate of 92.3%. All patients had a preoperative NYHA cardiac function classification of class Ⅳ. The NYHA cardiac function class of the patients improved (P<0.05) at discharge, 3 and 6 months after surgery when compared to the preoperative period. LVEF was significantly higher at 3 months after surgery than that during the preoperative period (P<0.05). LVEDD was significantly smaller at discharge, 3 and 6 months after surgery than that during the preoperative period (P<0.05). The safety evaluation's findings demonstrated that all 39 patients' intraoperative blood pump inlet tubes were oriented correctly, the artificial blood vessel suture sites were appropriate, there were no instances of device malfunction or pump thrombosis, or instances of bleeding or hemolysis, and the rate of the remaining adverse events was low. Conclusion With a low rate of adverse events and an excellent safety profile, the Corheart 6 left ventricular assist system can efficiently enhance cardiac function in patients with end-stage heart failure. It also has considerable clinical uses.

OBJECTIVES: To investigate the effect of avitinib for suppressing NLRP3 inflammasome activation and alleviating septic shock and explore the underlying mechanism. METHODS: Mouse bone marrow-derived macrophages (BMDM), human monocytic leukemia cell line THP-1, and peripheral blood mononuclear cells (PBMC) isolated from healthy volunteers were pre-treated with avitinib, followed by activation of the canonical NLRP3 inflammasome using agonists including nigericin, monosodium urate (MSU) crystals, or adenosine triphosphate (ATP). Non-canonical NLRP3 inflammasome activation was induced via intracellular transfection of lipopolysaccharide (LPS). Western blotting was used to detect the secretory protein markers of NLRP3 inflammasome activation and assess pyroptosis, and the levels of inflammatory cytokines in cell culture supernatant were determined with ELISA. In a mouse model of LPS-induced septic shock, the effect of avitinib treatment on the levels of inflammatory cytokines in serum and peritoneal lavage fluid were examined with ELISA, and survival curves of the mice were plotted using the Kaplan-Meier method. RESULTS: Avitinib significantly inhibited NLRP3 inflammasome activation in multiple cell types, and dose-dependently reduced IL-1β secretion and caspase-1 cleavage while suppressing GSDMD-mediated pyroptosis without obviously affecting IL-6 or TNF-α levels. In the mouse models of LPS-induced septic shock, avitinib significantly lowered IL-1β levels in serum and peritoneal fluid and extended survival time of the mice. CONCLUSIONS Avitinib suppresses NLRP3 inflammasome activation and alleviates septic shock in mice.
Animals ; Shock, Septic/metabolism* ; Mice ; NLR Family, Pyrin Domain-Containing 3 Protein ; Inflammasomes/drug effects* ; Humans ; Macrophages/metabolism* ; Interleukin-1beta/metabolism* ; Lipopolysaccharides

Adenomyosis is a common gynecological disease characterized by the invasion of endometrial glands and stroma into the myometrium of uterus, the pathological mechanism of which remains unclear yet. Disturbed lipid metabolism extensively affects abnormal cell proliferation and invasion in various diseases. However, the lipidome signature of human myometrium, which could be crucial in the development of adenomyosis, remains unknown. In this study, we generated the first lipidome profiling of human myometrium using a high-coverage and quantitative lipidomics approach based on ultra-performance liquid chromatography (UPLC) coupled with triple quadrupole (QqQ)-mass spectrometry (MS). A total of 317 lipid species were successfully quantified in the myometrial tissues from women with (n = 38) or without (n = 65) adenomyosis who underwent hysterectomy at Peking Union Medical College Hospital (Bejing, China). Up to 83 lipid species showed significant alternations in content between the two groups. These lipid aberrations involved multiple metabolic pathways, and emphasized inflammation, cell migration, and immune dysregulation upon adenomyosis. Moreover, receiver operating characteristic (ROC) curve analysis found that the combination of five lipid species could accurately distinguished the myometrial samples from women with and without adenomyosis with an area under the curve (AUC) of 0.906. Desorption electrospray ionization MS imaging (MSI) further underscored the heterogeneous distributions of these lipid markers in the adenomyosis lesion and adjacent myometrial tissue. Collectively, these results extremely improved our understanding on the molecular basis of adenomyosis, and could shed light on developing potential biomarkers and new therapeutic directions for adenomyosis.

Objective Viral encephalitis is an infectious disease severely affecting human health.It is caused by a wide variety of viral pathogens,including herpes viruses,flaviviruses,enteroviruses,and other viruses.The laboratory diagnosis of viral encephalitis is a worldwide challenge.Recently,high-throughput sequencing technology has provided new tools for diagnosing central nervous system infections.Thus,In this study,we established a multipathogen detection platform for viral encephalitis based on amplicon sequencing. Methods We designed nine pairs of specific polymerase chain reaction(PCR)primers for the 12 viruses by reviewing the relevant literature.The detection ability of the primers was verified by software simulation and the detection of known positive samples.Amplicon sequencing was used to validate the samples,and consistency was compared with Sanger sequencing. Results The results showed that the target sequences of various pathogens were obtained at a coverage depth level greater than 20×,and the sequence lengths were consistent with the sizes of the predicted amplicons.The sequences were verified using the National Center for Biotechnology Information BLAST,and all results were consistent with the results of Sanger sequencing. Conclusion Amplicon-based high-throughput sequencing technology is feasible as a supplementary method for the pathogenic detection of viral encephalitis.It is also a useful tool for the high-volume screening of clinical samples.

BACKGROUND:The combination of machine learning and medical metal materials can make up for the inefficiency and high cost of traditional experiments and computational simulations,and quickly and accurately predict the characteristics of metal materials by analyzing large amounts of data,optimize material design and performance,and improve the safety and efficiency of medical applications. OBJECTIVE:To summarize the research progress and shortcomings of machine learning in the characteristics of medical materials. METHODS:The first author searched CNKI,PubMed,X-MOL,and Web of Science databases by computer to search all relevant articles from January 2013 to April 2023.The Chinese search terms were"machine learning of medical metal materials,medical titanium alloy,medical magnesium alloy,medical metal material properties".The English search terms were"machine learning medical metal materials,medical stainless steel alloy,medical cobalt-chromium alloy,medical titanium alloy,medical magnesium alloy".Finally,70 relevant articles were included for a summary. RESULTS AND CONCLUSION:(1)The introduction of machine learning as a material design methodology has opened up new paradigms for material science research as the accessibility of large amounts of data generated by traditional experimental and computational simulation methods increases.(2)The machine learning workflow is divided into four main parts:data collection and preprocessing,feature engineering,model selection and training,and model evaluation,each of which is indispensable.(3)Medical metal materials are categorized into:stainless steel co-base alloys,cobalt-chromium alloys,titanium alloys,and magnesium alloys.For stainless steel co-base alloy,machine learning predicts its mechanical properties,to improve the generalization ability of machine learning.For cobalt-chromium alloy,machine learning predicts its mechanical properties,and it can conclude that cobalt-chromium alloy is the optimal material for hip implants.For titanium alloy,machine learning predicts its mechanical properties,and it can select the implant with the best mechanical properties.For magnesium alloy,machine learning predicts its corrosion resistance and mechanical properties;the ensemble model can accurately predict the mechanical properties of magnesium alloys,and the random forest model can predict the optimal elemental contents of magnesium alloys as vascular stents.(4)Machine learning has deficiencies in the field of medical materials.For example,the model is relatively lagging;the data failed to be standardized,and the generalization is low.To solve such problems,we should make full use of deep learning and segmentation algorithm technology,use unified standard data,and improve the model to increase the generalization ability.

To reduce the subjectivity and uncertainty present in the current international methods of drug value pricing when converting value into monetary prices,based on the hedonic pricing theory,it considers the post-negotiation price between manufacturers and payers as a reasonable price reference in the value pricing of Chinese patent medicine.By constructing an indicator system for the characteristics of Chinese patent medicine,it selects and measures the value characteristic variables that affect the price of Chinese patent medicine.It serves as the theoretical foundation and research basis for establishing a Hedonic price model between characteristic price variables and negotiation prices,thereby promoting the enhancement of rationality and objectivity in value-guided pricing of Chinese patent medicine.

To reduce the subjectivity and uncertainty present in the current international methods of drug value pricing when converting value into monetary prices,based on the hedonic pricing theory,it considers the post-negotiation price between manufacturers and payers as a reasonable price reference in the value pricing of Chinese patent medicine.By constructing an indicator system for the characteristics of Chinese patent medicine,it selects and measures the value characteristic variables that affect the price of Chinese patent medicine.It serves as the theoretical foundation and research basis for establishing a Hedonic price model between characteristic price variables and negotiation prices,thereby promoting the enhancement of rationality and objectivity in value-guided pricing of Chinese patent medicine.