One female patient with cancer pain due to bone metastasis from breast cancer was initially treated with Xinhuang tablets,diclofenac sodium double-release enteric-soluble capsules,paracetamol dihydrocodeine tablets and paracetamol oxycodone tablets,before being switched to controlled-release oxycodone hydrochloride tablets.She regularly took oxycodone hydrochloride sustained-release tablets 20 mg,q12h,no abnormalities were observed,and the dosage was increased to 40 mg,q12h due to poor pain control.The patient was diagnosed with 5-hydroxytryptamine syndrome after 1 d of intermittent recurrent tremor,myotonia,scalp sweating,restlessness and elevated blood pressure.When oxycodone hydrochloride sustained release tablet was adjusted to 20 mg,q12h and gabapentin capsule was added to 0.1 g,tid,the frequency of tremor and myotonia attacks slightly decreased,and sweating and agitation symptoms were not relieved.After 14 days,oxycodone hydrochloride sustained release tablets were stopped and morphine sulfate sustained release tablets 60 mg,q12h were replaced.Three days later,the patient's symptoms disappeared.During 5-month follow-up,the patient's pain was well-contrdled,with no change in the dose of morphine sulfate sustained-release tablets,and no adverse drug reactions observed.Using the Naranjo's Assessment Scale,the association between the patient's serotonin syndrome and the suspected drug oxycodone hydrochloride sustained-release tablets was evaluated as"probable".Thiscase highlights the importance for clinicians to closely monitor adverse reactions induced by rapid opioid dose escalation to ensure medication safety in patients.

Objective To construct a quality control model for cervical liquid-based thin cell smears using two different artificial intelligence(AI)techniques and to compare the total use of the two methods to improve the level of quality control of cervical liquid-based thin cell smears through the assistance of hybrid AI.Methods In this study,105 cervical liquid-based thin cell smear samples were used.Convolutional neural network(CNN)algorithm and Transformer network algorithm were used as specific AI algorithms in the AI model.The labeled features included the number of cells in the slice,excessive red blood cells,excessive inflammatory cells,and air bubbles.The smear samples were pre-processed and digitized by smear,followed by image segmentation and feature extraction.Using the labeled feature data,machine learning models were trained and optimized.Statistical AI and physician QC results were analyzed by calculating KAPPA index,sensitivity,specificity,area under the curve(AUC),and other indexes for AI QC results.Results CNN algorithm QC results in normal smear,inflammatory background and bloody background were significantly different from the expert review QC results(P＜0.001).Transformer algorithm QC results were similar to the expert review results,with no statistical difference(P＞0.05).General practitioner QC results were statistically different from the expert review QC results in normal smear detection rate and bloody background(P＜0.001).CNN algorithm Kappa value was 0.567,which had medium consistency with expert review results.Transformer algorithm Kappa value was 0.890,with the best consistency with expert review results.General practitioner Kappa value was 0.675,which had better consistency with expert review results.Using the expert review results as a reference standard,the predictive efficacy of the Transformer algorithm and the general practitioners' QC results was evaluated,and the predictive efficacy of the Transformer algorithm was higher than that of the general practitioners in detecting hemorrhagic backgrounds and normal smears(inflammatory backgrounds:AUC=1.000;normal smears:AUC=0.768)(hemorrhagic backgrounds:AUC=0.849;normal smears:AUC=0.849;normal smear:AUC=0.500).Conclusion In this study,we found that the Transformer algorithm was effective in improving the quality control of cervical liquid-based thin-layer cell smears by assisting doctors to perform smear quality control scoring and improving the efficiency and accuracy of smear sample quality control.It can be used as a new quality control method for cervical cancer cytological screening and has potential clinical applications.

The ability of cells to sense and adapt to metabolic changes and environmental variations is essential for their functions. Recent advances in synthetic biology have uncovered increasing mechanisms through which cells detect changes in metabolism and environmental conditions, leading to broader applications. However, a systematic review on the regulation of cellular metabolism and environmental adaption is currently lacking. This article presents a comprehensive overview of this field from three perspectives. First, it introduces key transmembrane and sensor proteins involved in the cellular perception of metabolic and environmental changes. Next, it summarizes the adaptive regulation mechanisms that natural cells employ when confronted with intracellular and extracellular metabolic changes. Finally, the review explores the application scenarios based on cellular adaptive regulation in three aspects: dynamic control, rational metabolic engineering, and adaptive evolution and makes an outlook on the future development directions in this field. This review not only provides a comprehensive perspective on the mechanisms by which cells sense metabolic and environmental variations, but also lays a theoretical foundation for further innovations in the field of synthetic biology. With the continuous advancement of future technologies, a deeper understanding of cellular adaptive regulation mechanisms holds great potential to drive the development and application of novel biomanufacturing platforms.
Adaptation, Physiological ; Synthetic Biology ; Metabolic Engineering/methods* ; Environment ; Bacteria/genetics*

Due to its high sensitivity and non-destructive nature, Raman spectroscopy has become an essential analytical tool in biopharmaceutical analysis and drug development. Despite of the computational demands, data requirements, or ethical considerations, artificial intelligence (AI) and particularly deep learning algorithms has further advanced Raman spectroscopy by enhancing data processing, feature extraction, and model optimization, which not only improves the accuracy and efficiency of Raman spectroscopy detection, but also greatly expands its range of application. AI-guided Raman spectroscopy has numerous applications in biomedicine, including characterizing drug structures, analyzing drug forms, controlling drug quality, identifying components, and studying drug-biomolecule interactions. AI-guided Raman spectroscopy has also revolutionized biomedical research and clinical diagnostics, particularly in disease early diagnosis and treatment optimization. Therefore, AI methods are crucial to advancing Raman spectroscopy in biopharmaceutical research and clinical diagnostics, offering new perspectives and tools for disease treatment and pharmaceutical process control. In summary, integrating AI and Raman spectroscopy in biomedicine has significantly improved analytical capabilities, offering innovative approaches for research and clinical applications.

The classification of stoma skin barriers varies based on their specific features. The curvature design of convex skin barriers provides a secure and effective seal for patients with flat, retracted stomas or peristomal skin folds. The secure sealing ability of convex skin barriers is attributed to several critical structural components. Although convex skin barriers offer many clinical advantages, there is currently no unified standard for measuring their characteristics, resulting in confusion among healthcare professionals when selecting stoma care products. To address this issue, the 2021 International Stoma Care Expert Meeting proposed the Expert Consensus on Characteristics of Convex Skin Barriers and Clinical Application, which clearly defines five essential properties and clinical application guidelines for convex barriers. However, as most consensus contributors are from Europe and North America, its applicability in Chinese healthcare settings may be limited. Therefore, this paper provides a detailed interpretation of the five characteristics and clinical application statements of convex skin barriers, aiming to offer practical guidance to clinical nurses in selecting appropriate convex products and managing stoma-related complications.

Objective To construct a quality control model for cervical liquid-based thin cell smears using two different artificial intelligence(AI)techniques and to compare the total use of the two methods to improve the level of quality control of cervical liquid-based thin cell smears through the assistance of hybrid AI.Methods In this study,105 cervical liquid-based thin cell smear samples were used.Convolutional neural network(CNN)algorithm and Transformer network algorithm were used as specific AI algorithms in the AI model.The labeled features included the number of cells in the slice,excessive red blood cells,excessive inflammatory cells,and air bubbles.The smear samples were pre-processed and digitized by smear,followed by image segmentation and feature extraction.Using the labeled feature data,machine learning models were trained and optimized.Statistical AI and physician QC results were analyzed by calculating KAPPA index,sensitivity,specificity,area under the curve(AUC),and other indexes for AI QC results.Results CNN algorithm QC results in normal smear,inflammatory background and bloody background were significantly different from the expert review QC results(P＜0.001).Transformer algorithm QC results were similar to the expert review results,with no statistical difference(P＞0.05).General practitioner QC results were statistically different from the expert review QC results in normal smear detection rate and bloody background(P＜0.001).CNN algorithm Kappa value was 0.567,which had medium consistency with expert review results.Transformer algorithm Kappa value was 0.890,with the best consistency with expert review results.General practitioner Kappa value was 0.675,which had better consistency with expert review results.Using the expert review results as a reference standard,the predictive efficacy of the Transformer algorithm and the general practitioners' QC results was evaluated,and the predictive efficacy of the Transformer algorithm was higher than that of the general practitioners in detecting hemorrhagic backgrounds and normal smears(inflammatory backgrounds:AUC=1.000;normal smears:AUC=0.768)(hemorrhagic backgrounds:AUC=0.849;normal smears:AUC=0.849;normal smear:AUC=0.500).Conclusion In this study,we found that the Transformer algorithm was effective in improving the quality control of cervical liquid-based thin-layer cell smears by assisting doctors to perform smear quality control scoring and improving the efficiency and accuracy of smear sample quality control.It can be used as a new quality control method for cervical cancer cytological screening and has potential clinical applications.

Dyslipidemia refers to the increased level of TG and total cholesterol in plasma, and also generally refers to other forms of dyslipidemia, which is characterized by non-obvious symptoms in the early stage of the disease, and the first episode is cardiovascular disease. The prevalence of dyslipidemia varies with country, region and population, and the prevalence of dyslipidemia in China is in increase, which is influenced by various risk factors. This paper summarizes the prevalence and risk factors of dyslipidemia and introduces the emerging risk factors of dyslipidemia, such as HIV infection, systemic lupus erythematosus, sarcopenia and environmental problem.

IgA nephropathy (IgAN) is the most common primary glomerulonephritis. Mesangial hyperplasia and deposition of IgA immune complex are typical pathological changes of IgAN. Animal model is an important tool to explore the pathogenesis, diagnosis and treatment plan, and evaluate the safety and efficacy of drugs. At present, there are many kinds of IgAN animal models, including humanized animal models and non-humanized animal models, and there are great differences in the modeling principle, method, time and pathological changes. The humanized animal model is closer to the pathogenesis of IgAN in humans and has gradually become a research hotspot. In this paper, common animal models of IgAN in detail from the aspects of modeling method and time, characteristics and significance of each model, and the research progress of anthropomorphic animal models are reviewed to provide reference and inspiration for the basic research of IgAN.

Objective:To compare the cardiac size，morphology，and function between anemic and normal fetuses using a hybrid artificial intelligence（AI）model，and to evaluate the utility of AI in quantitatively assessing fetal cardiac function in cases of anemia.Methods:A retrospective study was conducted by collecting data from 2018 to 2024 at the Seventh Affiliated Hospital of Sun Yat-sen University，including 15 cases of anemic fetuses（anemia group）diagnosed through umbilical venous puncture and 32 cases of normal fetuses（control group）. Four-chamber fetal cardiac ultrasound videos and left/right ventricular segments were included，with 44 videos and 1 056 segments in the anemia group，and 46 videos and 1 104 segments in the control group. Based on dynamic four-chamber heart images，the hybrid AI model was employed to extract heart measurement parameters，including basal-apical length（BAL），transverse width（TW），global sphericity index（GSI），end-diastolic area（EDA），24-segment left and right ventricular end-diastolic diameter（LVEDD，RVEDD），segmental sphericity index（LVSI，RVSI），global longitudinal strain（LVGLS，RVGLS），fractional area change（LVFAC，RVFAC），segmental fractional shortening（LVFS，RVFS），along with their corresponding Z-scores. The differences in cardiac size，morphology，and function parameters between the two groups were compared. Pearson correlation analysis was performed for the parameters of the control group（BAL，TW，EDA，GLS，LVGLS，RVGLS，LVFAC，and RVFAC）against gestational age. The measurement consistencies of AI technology and fetal HQ technology in normal and anemia groups were evaluated.Results:No significant differences were found in BAL，TW，EDA，or GSI between groups（all P>0.05）. RVEDD in segments 3-24 was significantly larger in the anemia group（all P<0.05），with significantly higher Z-score abnormality rates for LVEDD and RVEDD across 24 segments（both P<0.001）. LVSI in segments 7-10，12，14-15 and RVSI in segments 1-23 were lower in the anemia group（all P<0.05），with significantly higher Z-score abnormality rates for LVSI and RVSI across 24 segments（both P<0.001）. The absolute values of LVGLS and LVFAC were significantly reduced in the anemia group（both P<0.05），while the absolute values of RVGLS and RVFAC showed no significant differences（both P>0.05）. Segmental LVFS values were significantly lower in the anemia group for segments 2，5-8，11-13（all P<0.05）. In the control group，BAL，TW，and EDA positively correlated with gestational age（ r=0.913，0.947，0.907；all P<0.001），while GSI，LVGLS，RVGLS，LVFAC and RVFAC showed no or weak correlations（ r=-0.221，0.353，0.515，-0.409，-0.425）. The intraclass correlation coefficient（ICC）between AI-based and conventional fetal HQ evaluations were 0.788 for the control group and 0.837 for the anemia group，indicating good consistency. Conclusions:AI offers a reliable approach for quantitatively evaluating fetal cardiac size，shape，and systolic function. Fetal anemia primarily affects right ventricular morphology and left ventricular systolic performance，characterized by spherical remodeling of the right ventricle and reductions in LVGLS，LVFAC，and segmental LVFS. The hybrid AI model holds potential value in fetal cardiac function assessment.

Dyslipidemia refers to the increased level of TG and total cholesterol in plasma, and also generally refers to other forms of dyslipidemia, which is characterized by non-obvious symptoms in the early stage of the disease, and the first episode is cardiovascular disease. The prevalence of dyslipidemia varies with country, region and population, and the prevalence of dyslipidemia in China is in increase, which is influenced by various risk factors. This paper summarizes the prevalence and risk factors of dyslipidemia and introduces the emerging risk factors of dyslipidemia, such as HIV infection, systemic lupus erythematosus, sarcopenia and environmental problem.