Irritable bowel syndrome with diarrhea （IBS-D）， as a prototypical disorder involving the microbiota-gut-brain axis， remains poorly understood in terms of its pathogenesis， posing ongoing challenges for clinical diagnosis. Omics technologies， leveraging their high-throughput detection and systematic analysis advantages， has emerged as a critical tool for deciphering the complex mechanisms underlying traditional Chinese medicine （TCM） treatment of IBS-D. This systematic review summarizes the research progress of transcriptomics， proteomics， metabolomics， microbiomics， and multi-omics integration techniques in TCM interventions for IBS-D. In single-omics studies， transcriptomics using techniques like RNA-seq， reveals the regulatory mechanisms of TCM on IBS-related signaling pathways. Proteomics， leveraging quantitative technologies such as 2D-difference gel electrophoresis and tandem mass tag， identifies differentially expressed proteins and elucidates the action targets of TCM in treating IBS-D. Metabolomics， employing methods like UPLC-Q-TOF-MS and LC-MS/MS， discovers metabolic pathways regulated by TCM to improve metabolic disturbances in IBS-D. Microbiomics， based on 16S rRNA sequencing， confirms that TCM can reshape the gut microbiota structure and restore the intestinal microecological balance， thereby improving IBS-D. Multi-omics integration further overcomes the limitations of single-omics approaches by synthesizing information from transcriptomics， proteomics， metabolomics， and microbiomics， enabling a more comprehensive and systematic elucidation of the complex mechanisms underlying TCM treatment for IBS-D. In the future， research related to IBS-D should be advanced from three aspects： stratified clinical research based on TCM syndrome types， multi-omics integration verification mechanisms， and emerging omics to explore new mechanisms， providing more innovative ideas for the precise diagnosis and treatment of this disease.

Background: Adrenocortical hormones, particularly 11-oxygenated androgens, are pivotal in female reproductive health and fertility. Standardized detection kits and population-specific reference intervals are lacking in China, hindering related clinical applications. Methods: A HPLC-tandem mass spectrometry (HPLC-MS/MS) pipeline was developed, rigorously validated, and applied to simultaneously quantify corticosterone, cortisone, cortisol, 18-OH cortisol, androstenedione (A4), 11β-hydroxyandrostenedione (11-OH A4), dehydroepiandrosterone, and dehydroepiandrosterone sulfate in serum samples from 455 reproductive-aged women (18–45 yrs) in Guangxi, China. Age-dependent concentration trends were analyzed, and reference intervals stratified by age (2.5th to 97.5th percentiles) were established. Correlations with body-composition metrics, ethnicity, and the menstrual cycle were investigated. Results: The HPLC-MS/MS method demonstrated high precision (intra- and inter-assay CVs < 15%), accuracy, and sensitivity. All eight hormones exhibited significant age-related declines (P < 0.001 for seven hormones; P = 0.001 for 11-OH A4). Notably, 11-OH A4 levels were significantly lower in the 35–45-yr (3.05 nmol/L) and 25–34-yr (3.09 nmol/L) age groups than in the 18–24-yr (3.57 nmol/L) age group, whereas no significant difference was observed between the 35–45-yr and 25–34-yr age groups. Weak negative correlations were observed between the body mass index and corticosterone and cortisone levels, whereas ethnicity and the menstrual cycle showed no significant associations with hormone levels. Conclusions We developed an HPLC-MS/MS-based method for simultaneously quantifying eight adrenocortical hormones, including 11-OH A4, and defined age-specific reference intervals for reproductive-aged Chinese women. These findings advance the clinical utility of adrenocortical hormones in diagnosing and managing reproductive disorders.

Plant-derived extracellular vesicles (PDEVs) are nanoscale extracellular vesicles secreted by plant cells, characterized by a lipid bilayer structure. These vesicles carry a variety of bioactive molecules, including proteins, nucleic acids, and lipids, and play essential roles in intercellular communication and physiological regulation in plants. Compared to animal-derived extracellular vesicles, PDEVs offer several advantages, such as a broad range of sources, high biocompatibility, low immunogenicity, and low production costs. Furthermore, PDEVs have demonstrated remarkable potential as natural nanocarriers for drug delivery, due to their ability to efficiently traverse biological barriers, such as the blood-brain barrier, making them promising candidates for drug delivery systems. This review systematically elaborates on the complex composition of PDEVs, which consists of lipids, proteins, and nucleic acids, the typical structural characteristics of their lipid bilayers ranging from 30 to 150 nm, and their versatile loading capabilities as drug carriers, efficiently encapsulating various types of therapeutic agents such as hydrophilic small molecules, hydrophobic drugs, nucleic acids, and proteins. We systematically summarize the recent advancements in strategies for enhancing the loading efficiency of PDEVs, which include methods such as co-incubation, ultrasound-assisted loading, electroporation, freeze-thaw cycles, and microfluidic technology. These techniques are evaluated based on their underlying principles, suitable drug types, and their respective advantages. In addition to loading strategies, we focus on the engineered approaches to achieve targeted delivery using PDEVs, such as genetic engineering modifications, chemical ligand conjugation, membrane fusion technology, and polyethylene glycol (PEG) modification. We discuss the mechanisms of these strategies in enhancing targeting efficiency, prolonging in vivo circulation time, and improving therapeutic efficacy. Further, this review highlights the application of PDEVs in various disease models, including tumor, skin inflammation, metabolic disorders, and neurodegenerative diseases, showcasing their therapeutic potential as multifunctional delivery platforms. The ability of PDEVs to encapsulate diverse therapeutic agents and target specific tissues or cells opens up new avenues for the treatment of complex diseases, offering advantages over conventional drug delivery systems. However, despite the promising applications of PDEVs, several challenges remain in their development and clinical translation. These challenges include variability in source materials, standardization of preparation processes, quality control, scalability of production, and the need for clinical validation. To overcome these obstacles, the integration of advanced technologies such as artificial intelligence-assisted design and multi-omics analysis is proposed as a way to facilitate the precise development of PDEVs. These emerging technologies hold the potential to further enhance the precision and effectiveness of plant-based drug delivery systems, ultimately advancing the field of precision medicine. In conclusion, the use of PDEVs as a platform for drug delivery represents a promising area of research with the potential to revolutionize therapeutic strategies. Their ability to encapsulate and deliver a wide variety of bioactive molecules, along with their inherent advantages in biocompatibility and versatility, makes them a valuable tool in the development of more efficient and targeted therapeutic interventions. Continued research and innovation in this field will pave the way for the clinical implementation of PDEVs in the treatment of various diseases, offering new hope for more effective and sustainable therapeutic options.

Acute kidney injury (AKI) is a prevalent and life-threatening clinical syndrome characterised by a rapid decline in renal function and diverse pathological etiologies. The condition has been demonstrated to be associated with elevated mortality rates and an increased risk of progression to chronic kidney disease. At present, clinicians depend heavily on conventional functional markers, such as serum creatinine and urine output, for the diagnosis and staging of the disease. It is evident that these conventional indicators characteristically manifest a considerable temporal delay and only undergo modification subsequent to considerable tissue damage. This severely restricts the timeframe for early detection and timely therapeutic intervention. Furthermore, standard markers fail to provide specific biological information regarding the underlying cellular injury mechanisms. The utilisation of advanced probe technologies in molecular imaging offers a robust alternative to overcome these inherent diagnostic limitations.This comprehensive review systematically evaluates recent progress in the design and application of two primary categories of molecular imaging tools for acute kidney disease, specifically reactive probes and enzyme-activated probes. Reactive probes are engineered to specifically interact with redox-active chemical species, including hydrogen peroxide, peroxynitrite, hypochlorous acid, and sulfur dioxide. Because oxidative stress constitutes a primary early event in acute renal tubular damage, these probes enable researchers and clinicians to visualize early cellular injury and radical accumulation well before global renal functional decline becomes evident. We discuss the application of these reactive probes across multiple imaging modalities including fluorescence imaging, magnetic resonance imaging (MRI), positron emission tomography (PET), and photoacoustic techniques. Photoacoustic imaging combines high spatial resolution with deep tissue penetration and has successfully demonstrated the ability to provide diagnostic alerts up to 12 h before any detectable rise in serum creatinine levels. Additionally, specific reactive probes have shown promising translational potential when tested by high-throughput screening in clinical human urine samples. Enzyme-activated probes target the specific catalytic activity of disease-relevant enzymes. These include well-documented renal tubular structural biomarkers such as NAG, GGT, and ALP, along with apoptosis-related caspases and specific nitroreductases. By responding only to enzymatic cleavage, these tools provide highly specific and pathology-directed imaging readouts. Recent structural design strategies in this field have advanced significantly beyond single-enzyme detection. Researchers are now focusing on sophisticated dual-target recognition to minimize background noise, multimodal integration to cross-validate imaging signals, and theranostic applications where probes simultaneously deliver diagnostic feedback and therapeutic agents to injured tissues. Nanotechnology serves as a fundamental enabler for realizing these advanced probe functions. By precisely optimizing nanoparticle parameters such as hydrodynamic size, surface charge, and targeting ligands, researchers can achieve amplified signal output, highly precise kidney delivery, and protection against premature degradation in the systemic circulation. For example, modifying surface charges can significantly enhance the active uptake of nanoprobes by damaged renal tubular epithelial cells.While preclinical probe development has progressed rapidly, moving these technologies into routine clinical practice remains a major challenge. We analyze the translational feasibility and current obstacles from biological, technological, and regulatory perspectives. Although biological targets such as KIM-1, FAP, and ALP have been validated in extensive patient cohorts, practical barriers severely limit their immediate clinical application. These obstacles involve complex changes in in vivo pharmacokinetics. During an acute injury episode, the extreme drop in the glomerular filtration rate alters probe clearance and can cause unwanted systemic accumulation or confusing background imaging signals. Other major hurdles include a lack of comprehensive long-term toxicity data and the absence of standardized manufacturing protocols to ensure batch-to-batch consistency. Future successful translation will require rigorous multi-center clinical studies to confirm the true diagnostic value of these probes over traditional markers. Researchers must also establish strict standardization of imaging procedures and comprehensive safety evaluations. Ultimately, this review provides a thorough reference framework for designing clinically translatable molecular probes and building a precision diagnostic imaging system for acute kidney injury.

Objective To investigate the current status of work readiness and its influencing factors among patients returning to work after lung cancer surgery. Methods A retrospective study was conducted on young and middle-aged postoperative lung cancer patients who were treated at the Department of Thoracic Surgery, West China Hospital, Sichuan University from March to September 2023 and returned to their jobs. Data were collected through a general information questionnaire, readiness for return-to-work scale (RRTW), general self-efficacy scale (GSES), and simplified coping style questionnaire (SCSQ). Univariate and multivariate logistic regression analyses were used to explore the factors affecting the work readiness of patients returning to work. Results A total of 219 patients were included, with 59 males and 160 females aged 18-60 years. Among the postoperative lung cancer patients returning to work, 73.1% were in the active maintenance stage of return-to-work readiness with a RRTW score of (17.59±1.48) points, and 26.9% were in the uncertain maintenance stage with a RRTW score of (16.22±1.50) points. Multivariate logistic regression analysis showed that patients aged≤30 years (OR=52.381), employees of enterprises and institutions (OR=7.682), agricultural, pastoral, fishery, forestry laborers (OR=15.665), positive coping (OR=5.043), those with higher self-efficacy (OR=1.157) and engaged in physically demanding work (OR=2.449) had higher return-to-work readiness, while patients with≥2 children (OR=0.055), self-payment (OR=0.044), and those working more than 40 hours per week (OR=0.122) had lower return-to-work readiness. Conclusion The return-to-work readiness of young and middle-aged postoperative lung cancer patients needs to be improved, and occupation, job nature, main coping styles, general self-efficacy and other factors are associated with return-to-work readiness.

In recent years, the application of artificial intelligence (AI) in the field of biology has witnessed remarkable advancements. Among these, the most notable achievements have emerged in the domain of protein structure prediction and design, with AlphaFold and related innovations earning the 2024 Nobel Prize in Chemistry. These breakthroughs have transformed our ability to understand protein folding and molecular interactions, marking a pivotal milestone in computational biology. Looking ahead, it is foreseeable that the accurate prediction of various physicochemical properties of proteins—beyond static structure—will become the next critical frontier in this rapidly evolving field. One of the most important protein properties is thermodynamic stability, which refers to a protein’s ability to maintain its native conformation under physiological or stress conditions. Accurate prediction of protein stability, especially upon single-point mutations, plays a vital role in numerous scientific and industrial domains. These include understanding the molecular basis of disease, rational drug design, development of therapeutic proteins, design of more robust industrial enzymes, and engineering of biosensors. Consequently, the ability to reliably forecast the stability changes caused by mutations has broad and transformative implications across biomedical and biotechnological applications. Historically, protein stability was assessed via experimental methods such as differential scanning calorimetry (DSC) and circular dichroism (CD), which, while precise, are time-consuming and resource-intensive. This prompted the development of computational approaches, including empirical energy functions and physics-based simulations. However, these traditional models often fall short in capturing the complex, high-dimensional nature of protein conformational landscapes and mutational effects. Recent advances in machine learning (ML) have significantly improved predictive performance in this area. Early ML models used handcrafted features derived from sequence and structure, whereas modern deep learning models leverage massive datasets and learn representations directly from data. Deep neural networks (DNNs), graph neural networks (GNNs), and attention-based architectures such as transformers have shown particular promise. GNNs, in particular, excel at modeling spatial and topological relationships in molecular structures, making them well-suited for protein modeling tasks. Furthermore, attention mechanisms enable models to dynamically weigh the contribution of specific residues or regions, capturing long-range interactions and allosteric effects. Nevertheless, several key challenges remain. These include the imbalance and scarcity of high-quality experimental datasets, particularly for rare or functionally significant mutations, which can lead to biased or overfitted models. Additionally, the inherently dynamic nature of proteins—their conformational flexibility and context-dependent behavior—is difficult to encode in static structural representations. Current models often rely on a single structure or average conformation, which may overlook important aspects of stability modulation. Efforts are ongoing to incorporate multi-conformational ensembles, molecular dynamics simulations, and physics-informed learning frameworks into predictive models. This paper presents a comprehensive review of the evolution of protein thermodynamic stability prediction techniques, with emphasis on the recent progress enabled by machine learning. It highlights representative datasets, modeling strategies, evaluation benchmarks, and the integration of structural and biochemical features. The aim is to provide researchers with a structured and up-to-date reference, guiding the development of more robust, generalizable, and interpretable models for predicting protein stability changes upon mutation. As the field moves forward, the synergy between data-driven AI methods and domain-specific biological knowledge will be key to unlocking deeper understanding and broader applications of protein engineering.

AIM: To investigate how angles kappa and alpha affect postoperative visual quality in patients with multifocal intraocular lens(mIOLs)implantation.METHODS: Retrospective cases series. A total of 46 patients(46 eyes)who underwent phacoemulsification were subsumed. The correlation between Preoperative angles kappa and alpha, wave-front aberrations and objective visual quality of cornea, internal, and total eye after surgery were analyzed using iTrace.RESULTS: The magnitude of angle kappa was negatively correlated with internal and total modulation transfer function(MTF)at 3 mm; the magnitude of angle kappa was positively correlated with astigmatism, trefoil, higher-order aberrations(HOAs)of both internal and total eye at 3 mm. The magnitude of angle alpha was negatively correlated with total MTF and total Strehl ratio at 3 mm. The magnitude of angle alpha was positively correlated with corneal coma at 5 mm, internal astigmatism at both 3 mm and 5 mm, and total spherical aberration(SA)at 3 mm. Multivariate linear regression analysis showed that, among candidate independent variables(kappa, alpha, astigmatism, SA, coma, trefoil, and HOAs), astigmatism is the only independent factor for altering corneal MTF at 3 mm and 5 mm; astigmatism and HOAs emerged as independent factors for altering internal MTF at 3 mm and 5 mm, and total MTF at 3 mm; astigmatism, SA and HOAs emerged as independent factors for altering total MTF at 5 mm.CONCLUSION: With greater preoperative angle kappa or angle alpha, patients who accept mIOL implantation tend to have larger internal astigmatism and HOAs, which resulting in poor visual quality, especially those with small pupil size.

Objective To compare the clinical efficacy of typeⅡhybrid surgery versus Sun’s surgery in treating acute Stanford A aortic dissection. Methods A retrospective analysis was conducted on the clinical data of patients with acute Stanford A aortic dissection who were treated at the Central Hospital of Wuhan affiliated to Tongji Medical College, Huazhong University of Science and Technology from 2016 to 2022. According to the surgical method, patients were divided into a typeⅡhybrid group and a Sun’s surgery group, and the clinical efficacy of the two groups was compared. Results A total of 52 patients were included, with 22 in the typeⅡhybrid surgery group and 30 in the Sun’s surgery group. The typeⅡhybrid group consisted of 18 males and 4 females, with an average age of (58.18±6.00) years, while the Sun’s surgery group consisted of 22 males and 8 females, with an average age of (53.03±11.89) years. All surgeries were successfully completed. There were 4 (13.3%) perioperative deaths in the Sun’s surgery group, including 2 patients of multiple organ failure, 1 patient of paraplegia, and 1 patient of uncontrollable postoperative bleeding. There was 1 (4.5%) perioperative death in the typeⅡhybrid surgery group, who was suspected of acute coronary syndrome and took a loading dose of dual antiplatelet drugs preoperatively. The patient underwent secondary thoracotomy for hemostasis, was re-cannulated during the operation, and finally died of circulatory failure after implantation of intra-aortic balloon pumping. There was no statistical difference in perioperative mortality between the two groups (P=0.381). Compared with the Sun’s surgery group, the typeⅡhybrid surgery group had shorter cardiopulmonary bypass time [153.00 (135.00, 185.25) min vs. 182.50 (166.50, 196.75) min, P=0.013], aortic cross-clamping time [77.00 (70.50, 92.00) min vs. 102.50 (93.50, 109.75) min, P<0.001], postoperative ICU stay [4.00 (2.83, 6.00) days vs. 8.00 (6.38, 11.78) days, P<0.001], postoperative ventilator support time [72.00 (29.50, 93.25) h vs. 87.65 (39.13, 139.13) h, P=0.138], less intraoperative blood loss [(1586.82±209.41) mL vs. (1 806.00±292.62) mL, P=0.004], postoperative 24 h drainage volume [612.50 (507.50, 762.50) mL vs. 687.50 (518.75, 993.75) mL, P=0.409], and shorter postoperative hospital stay [18.00 (13.00, 20.25) days vs. 22.00 (17.00, 29.25) days, P=0.013]. There was no statistically significant difference in the incidence of other early postoperative complications such as secondary thoracotomy for hemostasis, tracheotomy, renal dysfunction requiring dialysis, stroke, and paraplegia between the two groups (P>0.05). Conclusion For patients with acute Stanford A aortic dissection, typeⅡhybrid surgery is safe and effective; compared with traditional Sun’s surgery, typeⅡhybrid surgery has relatively less trauma, lower incidence of complications, satisfactory short-term results, and further research is needed on long-term prognosis.

ObjectiveTo investigate and analyze an outbreak of rotavirus infectious diarrhea in a prison in Chongqing Municipality, to provide a basis for adult rotavirus surveillance and prevention, and to explore the public health problems in special settings. MethodsA retrospective survey was conducted to collect and analyze data on individual cases with diarrheal disease on-site. The clinical characteristics, as well as the temporal, spatial and geographical distribution patterns of the epidemic were described. Multi-pathogen detection tests were conducted both on diarrhea cases and environmental samples, with viral genotyping performed on positive samples. A case-control analysis was performed to identify the causes of the outbreak, and an SEIR model was adopted to predict the outbreak trend and evaluate the effectiveness of interventions. ResultsA total of 65 cases were found among the inmates, with an attack rate of 2.03%. The predominant clinical manifestations included diarrhea (89.23%), watery stool (73.85%), and dehydration (18.46%). The epidemic curve indicated a “human-to-human” transmission pattern, with an average incubation period of 5‒6 days. The attack rates among chefs in the main canteen (80.00%, 8/10) and caterers (28.33%, 17/60) were significantly higher than those of other inmates （P<0.05）. Multi-pathogen polymerase chain reaction (PCR) testing detected positive for group A rotavirus, with the viral genotyping identified as G9P [8] strain. Factors such as unprotected "bare-handed" food distribution among cases with diarrhea (OR=9.512, 95%CI: 4.261‒21.234) and close contact with diarrhea cases (OR=3.656, 95%CI: 1.719‒7.778) were the possible cause of the outbreak. The SEIR model (r₀=5, α=0.3, β₁=0.08, β₂=0.04) was constructed using prison inmates as susceptible population, aiming at fitting the initial transmission trend of the outbreak, and the epidemic rate declined rapidly after intervention measures were implemented (r_t≈0). ConclusionThis rare rotavirus infection diarrhea outbreak among adults in confined settings suggests that the construction of public health prevention and control systems in prison may be overlooked. Cross infection during meal processing and distribution in the canteens of such settings is likely to be the cause of the outbreak. Given the potential neglect of public heath system construction in special settings, it is imperative to enhance the surveillance and monitoring of rotavirus and other intestinal multi-pathogens among adults, as well as the construction of public health prevention and control systems in these special settings.