[Objective] To explore the clinical significance and application value of establishing a database for red blood cell alloantibody detection. [Methods] Patients who were scheduled for blood transfusion in our hospital from January 1, 2020 to May 1, 2024 were selected as the research subjects. A red blood cell alloantibody detection database was established using Microsoft Office Excel software to register the detection data of patients' alloantibodies and antibodies of undetermined specificity (AUS). A retrospective analysis was conducted on the clinical characteristics, antibody distribution, antibody decay and repeat positivity of the patients in the database. The LISS-IAT method was routinely used for antibody screening and identification. [Results] Among the alloantibodies, the Rh blood group system had the highest detection rate, followed by antibodies of the MNS blood group system and the Lewis blood group system. The predominant antibody in the Rh blood group system was anti-E. In the univariate analysis, the positivity of antibody was significantly associated with the patient's gender, age, blood transfusion history, pregnancy history and type of disease (all P<0.001). In the database, 48 patients experienced antibody decay, accounting for 15.24%(48/315), with an average time span of antibody decay ranging from 22 to 1 324 days. Six cases showed repeat positivity after decay, which were related to blood transfusions. The shortest interval between blood transfusions that led to antibody repeat positivity was 3 days, and the longest interval was 427 days. Among 58 cases with AUS, 3 converted into alloantibodies, among which 2 were anti-E and 1 was anti-Lea. [Conclusion] Establishing a red blood cell alloantibody detection database is an effective way to guide ambiguous cross-matching in clinical practice and is also an effective measure for the management of transfusion risks.

Transmembrane proteins (TMEM) are a type of membrane protein. Most proteins in this family are located in the phospholipid bilayer of the cell membrane, while a smaller portion is found in the membranes of cellular organelles. Transmembrane protein 43 (TMEM43) is a member of the TMEM protein family and is encoded by the TMEM43 gene. This protein consists of 400 amino acids and has 4 transmembrane domains and 1 membrane-associated domain. TMEM43 is localized to various biological membranes within the cell, such as the cell membrane and nuclear membrane, where it forms transmembrane channels for various ions. Additionally, TMEM43 is expressed in many species, showing high genetic similarity, especially with the four transmembrane domains being highly conserved. Current studies on the TMEM43 gene are still in its early stages, mainly focusing on its association with arrhythmogenic right ventricular cardiomyopathy (ARVC) and cancer. However, recent studies suggest that pathogenic mutations in TMEM43 may cause auditory neuropathy spectrum disorder (ANSD). Patients with TMEM43 p.Ser372Ter exhibited late-onset progressive ANSD. Impact of TMEM43 pathogenic mutations on individual hearing was likely mediated through effects on gap junction (GJ) structures on glia-like supporting cells (GLS), cell membranes. The TMEM43 p.Arg372Ter pathogenic mutation primarily affected the structure and function of TMEM43 protein, leading to premature termination of protein translation and the production of a truncated protein. Abnormal TMEM43 protein significantly reduced K⁺ influx in GLS cells, disrupting the endolymphatic K⁺ circulation and cochlear microenvironment homeostasis. When K⁺ circulation was obstructed, the endocochlear potential (EP) became abnormal, impairing the physiological function of hair cells and potentially leading to hearing impairment. However, it is important to note that studies on the mechanism is limited, and more experimental evidence is needed to confirm this hypothesis. Currently, there is a significant gap in research on TMEM43 and hearing loss, with many issues remaining unresolved. While TMEM43 has been studied in relation to hearing loss in humans, zebrafish, mice, and rats, the research is still preliminary. Detailed investigations into the molecular pathogenic mechanisms, the impact of mutations on hearing damage, and related therapeutic strategies are needed. Additionally, as a newly identified hearing loss-related gene, the mutation frequency and incidence of hearing disorders associated with TMEM43 have not been effectively quantified. For example, the ClinVar database listed 829 mutation sites for the TMEM43 gene, with only three mutations related to auditory neuropathy: c.605A>T (p.Asn202Ile), c.889T>A (p.Phe297Ile), and c.1114C>T (p.Arg372Ter). Aside from the aforementioned TMEM43 c.1114C>T (p.Arg372Ter) mutation observed in patients, the other two mutations were experimentally induced and have not been found in patients. Consequently, these mutations have been classified as unknown significance. We reviewed the current understanding of TMEM43 and hearing loss, analyzed its role in ear development and sound conduction, and explored the impact of TMEM43 gene variations on hearing loss, aiming to provide new insights for future research and precision medicine related to TMEM43.

Inflammatory diseases (IDs) are a general term of diseases characterized by chronic inflammation as the primary pathogenetic mechanism, which seriously affect the quality of patient′s life and cause significant social and medical burden. Current drugs for IDs include nonsteroidal anti-inflammatory drugs, corticosteroids, immunomodulators, biologics, and antioxidants, but these drugs may cause gastrointestinal side effects, induce or worsen infections, and cause non-response or intolerance. Given the outstanding performance of metal polyphenol network (MPN) in the fields of drug delivery, biomedical imaging, and catalytic therapy, its application in the diagnosis and treatment of IDs has attracted much attention and significant progress has been made. In this paper, we first provide an overview of the types of IDs and their generating mechanisms, then sort out and summarize the different forms of MPN in recent years, and finally discuss in detail the characteristics of MPN and their latest research progress in the diagnosis and treatment of IDs. This research may provide useful references for scientific research and clinical practice in the related fields.

Direct compression is an ideal method for tablet preparation, but it requires the powder's high functional properties. The functional properties of the powder during compression directly affect the quality of the tablet. 15 parameters such as Py, FES-8KN, FES-12KN, FES-16KN, CR-8KN, CR-12KN, and CR-16KN were used as the characteristic variables in this paper. Unsupervised learning methods like principal component analysis, cluster analysis, and factor analysis were applied to analyze and classify the compression behavior data of 36 traditional Chinese medicine powders. The results showed that both different dimensionality reduction classification methods could effectively differentiate the compression behavior characteristics of 36 traditional Chinese medicine compound powders. The hierarchical cluster analysis results showed a better agreement with the actual compression phenomena of the powders, where group 1 was high elasticity and low compressibility, group 2 was easily compressed and hard to break, group 3 was excellent compressibility and compactibility. This study is expected to provide references and ideas for predicting the behavior of traditional Chinese medicine powders and the screening of tablet formulations.

Clinical practice guidelines represent the best recommendations for patient care. They are developed through systematically reviewing currently available clinical evidence and weighing the relative benefits and risks of various interventions. However, clinical practice guidelines have to go through a long translation cycle from development and revision to clinical promotion and application, facing problems such as scattered distribution, high duplication rate, and low actual utilization. At present, the clinical practice guideline information platform can directly or indirectly solve the problems related to the lengthy revision cycles, decentralized dissemination and limited application of clinical practice guidelines. Therefore, this paper systematically examines different types of clinical practice guideline information platforms and investigates their corresponding challenges and emerging trends in platform design, data integration, and practical implementation, with the aim of clarifying the current status of this field and providing valuable reference for future research on clinical practice guideline information platforms.

Biomolecular condensates are formed through phase separation of biomacromolecules such as proteins and RNAs. These condensates exhibit liquid-like properties that can futher transition into more stable material states. They form complex internal structures via multivalent weak interactions, enabling precise spatiotemporal regulations. However, the use of inconsistent and non-standardized terminology has become increasingly problematic, hindering academic exchange and the dissemination of scientific knowledge. Therefore, it is necessary to discuss the terminology related to biomolecular condensates in order to clarify concepts, promote interdisciplinary cooperation, enhance research efficiency, and support the healthy development of this field.

A 20-year-old male patient presented to the Department of Dermatology of Peking Union Medical College Hospital with complaints of an 8-year history of facial scarring, swelling of the lower limbs, and a 4-year history of scalp thickening. Physical examination showed thickening furrowing wrinkling of the skin on the face and behind the ears, ciliary body hirsutism, blepharoptosis, and cutis verticis gyrate. Both lower limbs were swollen, especially the knees and ankles. The skin of the palms and soles of the feet was keratinized and thickened. Laboratory examination using bone and joint X-ray showed periostosis of the proximal middle phalanges and metacarpals of both hands, distal ulna and radius, tibia and fibula, distal femurs, and metatarsals.Genetic testing revealed two variants in SLCO2A1, c.1658T > A and c.96+4A > C.Through multidisciplinary consultation, we confirmed the diagnosis of pachydermoperiostosis.

ObjectiveTo evaluate the efficacy of Shexiang Baoxinwan in treating stable angina pectoris with Qi stagnation and blood stasis syndrome in patients with coronary artery disease （CAD） complicated with anxiety and depression and explore its underlying mechanisms. MethodsThis study employed a randomized， double-blind， and placebo-controlled clinical trial design. Patients admitted to the hospital were randomly assigned to the observation group and the control group， with 52 patients in each group. Patients in the observation and control groups received Shexiang Baoxinwan and placebo， respectively， both in combination with conventional Western medication. The dose was 45.0 mg， three times daily， for a total duration of eight weeks. The primary outcome was the Seattle Angina Questionnaire （SAQ） scores before and after treatment. Secondary outcomes included changes in traditional Chinese medicine （TCM） syndrome score， the patient health questionnaire-9 （PHQ-9）， generalized anxiety disorder-7 （GAD-7）， inflammatory markers ［interleukin-18 （IL-18）， interleukin-10 （IL-10）， tumor necrosis factor-alpha （TNF-α）， CD40， etc.］， monoamine neurotransmitters ［e.g.， dopamine （DA）］， vascular endothelial function markers ［e.g.， endothelin-1（ET-1）］， adipokines， and ischemia-modified albumin （IMA）. Adverse reactions were also recorded. ResultsA total of 92 patients completed the study， with 44 in the observation group and 48 in the control group. Compared with baseline， both groups showed significant decreases in PHQ-9， GAD-7， and TCM syndrome scores following treatment （P<0.05）， along with a significant increase in SAQ scores （P<0.05）. In the observation group， DA levels were significantly increased （P<0.05）， while levels of IL-18， TNF-α， CD40， ET-1， and IMA were decreased （P<0.05）. In contrast， the control group exhibited significantly increased CD40 levels （P<0.05）. Compared with the control group after treatment， the observation group showed significant improvements in the SAQ dimensions of physical limitation， angina stability， treatment satisfaction， and disease perception， as well as in TCM syndrome score， PHQ-9 score， IL-18， CD40， ET-1， and IMA （P<0.05）. No adverse reactions were observed in either group during treatment. ConclusionShexiang Baoxinwan can improve anxiety and depression， alleviate angina symptoms， and reduce TCM symptoms of Qi stagnation and blood stasis in CAD patients. The mechanism may involve anti-inflammation， improvement of vascular endothelial function， reduction of IMA， and increase of monoamine neurotransmitter levels.

ObjectiveTo evaluate the efficacy of Shexiang Baoxinwan in treating stable angina pectoris with Qi stagnation and blood stasis syndrome in patients with coronary artery disease （CAD） complicated with anxiety and depression and explore its underlying mechanisms. MethodsThis study employed a randomized， double-blind， and placebo-controlled clinical trial design. Patients admitted to the hospital were randomly assigned to the observation group and the control group， with 52 patients in each group. Patients in the observation and control groups received Shexiang Baoxinwan and placebo， respectively， both in combination with conventional Western medication. The dose was 45.0 mg， three times daily， for a total duration of eight weeks. The primary outcome was the Seattle Angina Questionnaire （SAQ） scores before and after treatment. Secondary outcomes included changes in traditional Chinese medicine （TCM） syndrome score， the patient health questionnaire-9 （PHQ-9）， generalized anxiety disorder-7 （GAD-7）， inflammatory markers ［interleukin-18 （IL-18）， interleukin-10 （IL-10）， tumor necrosis factor-alpha （TNF-α）， CD40， etc.］， monoamine neurotransmitters ［e.g.， dopamine （DA）］， vascular endothelial function markers ［e.g.， endothelin-1（ET-1）］， adipokines， and ischemia-modified albumin （IMA）. Adverse reactions were also recorded. ResultsA total of 92 patients completed the study， with 44 in the observation group and 48 in the control group. Compared with baseline， both groups showed significant decreases in PHQ-9， GAD-7， and TCM syndrome scores following treatment （P<0.05）， along with a significant increase in SAQ scores （P<0.05）. In the observation group， DA levels were significantly increased （P<0.05）， while levels of IL-18， TNF-α， CD40， ET-1， and IMA were decreased （P<0.05）. In contrast， the control group exhibited significantly increased CD40 levels （P<0.05）. Compared with the control group after treatment， the observation group showed significant improvements in the SAQ dimensions of physical limitation， angina stability， treatment satisfaction， and disease perception， as well as in TCM syndrome score， PHQ-9 score， IL-18， CD40， ET-1， and IMA （P<0.05）. No adverse reactions were observed in either group during treatment. ConclusionShexiang Baoxinwan can improve anxiety and depression， alleviate angina symptoms， and reduce TCM symptoms of Qi stagnation and blood stasis in CAD patients. The mechanism may involve anti-inflammation， improvement of vascular endothelial function， reduction of IMA， and increase of monoamine neurotransmitter levels.

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.