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.

Objective: Data on syncopal donation-related vasovagal reaction (DRVR) collected from 74 blood centers between 2020 and 2023 was statistically analyzed to provide a reference for developing preventive strategies against syncopal DRVR. Methods: Data on blood donation adverse reactions and basic information of donors from 2020 to 2023 were collected through the information management system at monitoring sentinel sites. Statistical analysis was performed on the following aspects of syncopal DRVR: characteristics of donors who experienced syncope, reported incidence, triggers, duration, presence and occurrence time of syncope-related trauma, clinical management including outpatient and inpatient treatment, and severity grading. Results: From 2020 to 2023, 45 966 donation-related adverse reactions were recorded. Of these, 1 665 (3.72%) cases were syncopal DRVR. The incidence of syncopal DRVR decreased with age, being the highest in the 18-22 age group. Incidence was significantly higher in female donors than male donors, in first-time donors than repeat donors, and in university and individual donors than group donors (all P<0.05). There was no statistically significant difference among different blood donation locations (P>0.05). The top three triggers were tension, fatigue, and needle phobia or fear of blood. Among syncopal DRVR cases, 60.36% occurred during blood collection, 87.63% lasted for less than 60 seconds, and 5.05% were accompanied by trauma. Notably, 57.14% of these traumas occurred after donor had left the blood collection site. Syncope severity was graded based on required treatment: grade 1 (fully recovered without treatment, 95.50%); grade 2 (recovered after outpatient treatment, 4.02%); and grade 3 (recovered after inpatient treatment, 0.48%). Conclusion: By analyzing the data of syncopal DRVR cases, it is possible to provide a reference for formulating blood donor safety policies.

OBJECTIVE: Based on the PTEN-induced hypothetical kinase 1 (PINK1)/Parkin pathway, the effect of acupuncture pretreatment on the expression of mitochondrial autophagy-related proteins in gastrocnemius muscle tissue of rats with exercise-induced muscle damage (EIMD) was observed, and the underlying mechanism of acupuncture pretreatment for the prevention and treatment of EIMD was explored. METHODS: Of 88 SD male rats, aged 6 weeks, 8 rats were randomly selected as a blank group, and the remaining 80 rats were randomized into a model group and an acupuncture pretreatment group, with 40 rats in each group. Either the model group or the acupuncture pretreatment group was subdivided randomly into 5 subgroups with 8 rats in each one according to the time points of sample collection, 0 h, 12 h, 24 h, 48 h and 72 h after modeling. An intermittent downhill running centrifugal exercise was carried out on an animal experimental treadmill to establish the EIMD model in the model group and the acupuncture pretreatment group. The rats in the acupuncture pretreatment group received acupuncture at "Guanyuan" (CV6) and bilateral "Zusanli" (ST36), once a day for 20 min each time, for 7 consecutive days before EIMD model preparation. Transmission electron microscopy was used to observe the ultrastructure of gastrocnemius muscle tissue in each group. The contents of malondialdehyde (MDA), superoxide dismutase (SOD) and catalase (CAT) in serum were detected by ELISA. Western blot was used to detect the protein expression of PINK1, Parkin, sequestosome 1 (p62) and microtubule-associated protein light chain 3B (LC3B) in rat gastrocnemius muscle tissue. Real-time PCR was adopted to detect the mRNA expression of PINK1, Parkin, p62 and LC3B in rat gastrocnemius muscle tissue. RESULTS: Compared with the blank group, the mitochondria of gastrocnemius muscles showed obvious swelling in the 0 h, 12 h, 24 h, and 48 h model subgroups , autophagosomes were formed in the 12 h and 24 h model subgroups, and the mitochondrial morphology returned to normal gradually in the 72 h model subgroup. The serum MDA contents of rats in 5 model subgroups increased (P<0.01, P<0.05). The contents of SOD and CAT in the subgroups of 0 h, 12 h, 24 h and 48 h decreased (P<0.05, P<0.01). The protein and mRNA expression levels of PINK1, Parkin and LC3B in gastrocnemius muscle tissue of rats in 0 h, 12 h and 24 h subgroups were elevated (P<0.01); and the protein and mRNA expression levels of p62 in the 0 h, 12 h, 24 h and 48 h subgroups were reduced (P<0.01, P<0.05). Compared with the model subgroup at the same time point, the myofibril damage and the degree of mitochondrial swelling were mild in each acupuncture pretreatment subgroup, and the numbers of autophagosomes were fewer. The contents of MDA in the acupuncture pretreatment subgroups decreased at 0 h, 12 h, 24 h, and 48 h (P<0.05, P<0.01). The contents of SOD and CAT in the 12 h acupuncture pretreatment subgroup increased (P<0.05, P<0.01). The protein and mRNA expression levels of PINK1 and Parkin in the 0 h, 12 h, and 24 h acupuncture pretreatment subgroups decreased (P<0.01, P<0.05). The protein and mRNA expression levels of LC3B in the 12 h acupuncture pretreatment subgroup decreased (P<0.01), and that of p62 in the 0 h and 24 h acupuncture pretreatment subgroups increased (P<0.01, P<0.05). CONCLUSION The intermittent downhill running centrifugal exercise induces the excessive mitochondrial autophagy. Acupuncture pretreatment may attenuate EIMD, and the underlying mechanism is related to the regulation of PINK1/Parkin signaling pathway expression, reducing oxidative stress damage in skeletal muscle cells, and inhibiting mitochondrial autophagy overactivation.
Animals ; Ubiquitin-Protein Ligases/genetics* ; Male ; Rats ; Acupuncture Therapy ; Protein Kinases/genetics* ; Rats, Sprague-Dawley ; Mitophagy ; Humans ; Muscle, Skeletal/metabolism* ; Physical Conditioning, Animal ; Muscular Diseases/physiopathology* ; Signal Transduction

BACKGROUND: Generalized pustular psoriasis (GPP), a rare and recurrent autoinflammatory disease, imposes a substantial burden on patients and society. Awareness of GPP in China remains limited. METHODS: This cross-sectional survey, conducted between September 2021 and May 2023 across 14 hospitals in China, included GPP patients of all ages and disease phases. Data collected encompassed demographics, clinical characteristics, economic impact, disease severity, quality of life, and treatment-related complications. Risk factors for GPP recurrence were analyzed. RESULTS: Among 127 patients (female/male ratio = 1.35:1), the mean age of disease onset was 25 years (1st quartile [Q1]-3rd quartile [Q3]: 11-44 years); 29.2% had experienced GPP for more than 10 years. Recurrence occurred in 75.6% of patients, and nearly half reported no identifiable triggers. Younger age at disease onset ( P  = 0.021) and transitioning to plaque psoriasis ( P  = 0.022) were associated with higher recurrence rates. The median diagnostic delay was 8 months (Q1-Q3: 2-41 months), and 32.3% of patients reported misdiagnoses. Comorbidities were present in 53.5% of patients, whereas 51.1% experienced systemic complications during treatment. Depression and anxiety affected 84.5% and 95.6% of patients, respectively. During GPP flares, the median Dermatology Life Quality Index score was 19.0 (Q1-Q3: 13.0-23.5). This score showed significant differences between patients with and without systemic symptoms; it demonstrated correlations with both depression and anxiety scores. Treatment costs caused financial hardship in 55.9% of patients, underscoring the burden associated with GPP. CONCLUSIONS The substantial disease and economic burdens among Chinese GPP patients warrant increased attention. Patients with early onset disease and those transitioning to plaque psoriasis require targeted interventions to mitigate the high recurrence risk.
Humans ; Male ; Female ; Psoriasis/pathology* ; Adult ; Cross-Sectional Studies ; Adolescent ; Child ; Young Adult ; Quality of Life ; Middle Aged ; China/epidemiology* ; Recurrence ; Risk Factors ; Surveys and Questionnaires ; East Asian People

OBJECTIVE: To investigate the variability of bone metabolism levels among different populations and its association with knee osteoarthritis (KOA) pain. METHODS: A total of 50 people (control group) who participated in physical examination from January 2023 to June 2023 were selected, including 26 males and 24 females, wtih a mean aged of (52.14±9.04) years old ranging 41 to 65 years old. The other 50 patients with knee osteoarthritis(case group) who attended the outpatient clinic of the Orthopedics and Traumatology Department in the same time period, including 19 males and 31 females, with a mean age of (53.60±7.76) years old ranging 40 to 65 years. The two groups of Western Ontario and McMaster Universities Osteoarthritis Index(WOMAC) and bone metabolism markers, such as 25-hydroxy-cholecalciferol[25(OH)D], β-isomerized typeⅠcollagen C-telopeptide breakdown products (β-CTX), total typeⅠprocollagen N-terminal propeptide (t-PINP), osteocalcin (OC), parathormone (PTH) levels were compared. Pearson correlation analysis was used to compare the correlation between two groups of bone metabolism related markers and WOMAC. RESULTS: The WOMAC score of the case group (39.90±2.34) was higher than that of the control group (3.60±0.57), with significant difference (P<0.05). There was no significant difference between the two groups of 25 (OH)D, β-CTX and PTH (P>0.05). The t-PINP and OC of the case group were (62.90±52.40) and (19.88±10.15) ng·ml-1, respectively, and those of the control group were (38.86±10.82) and (14.90±3.62) ng·ml^-1, respectively;the t-PINP and OC of the case group were higher than those of the control group, with significant difference (P<0.05). Pearson correlation analysis showed that t-PINP was positively correlated with WOMAC pain score in the case group (r²=0.045, P<0.01). CONCLUSION Bone metabolism levels in the serum of patients with knee osteoarthritis are different from those of healthy people, and the difference between OC and t-PINP is the most obvious, and the concentration of t-PINP levels is positively correlated with pain symptoms in patients with KOA. However, the specific mechanism of correlation between the bone metabolism levels of patients with KOA and their pain symptoms needs to be further elucidated by basic experimental research as well as by enlarging the samples.
Humans ; Female ; Male ; Middle Aged ; Osteoarthritis, Knee/metabolism* ; Aged ; Adult ; Bone and Bones/metabolism* ; Pain/etiology* ; Biomarkers/metabolism*

OBJECTIVES: To explore the relationship between white matter injury (WMI) and cerebral perfusion in preterm infants using arterial spin labeling (ASL). METHODS: A total of 293 preterm infants (gestational age <34 weeks) hospitalized at the Third Affiliated Hospital of Zhengzhou University between June 2022 and June 2024 were included. After achieving clinical stability, the infants underwent brain magnetic resonance imaging (MRI) and ASL. Based on MRI findings, infants were classified into WMI (n=66) and non-WMI (n=227) groups. Cerebral perfusion parameters were compared between groups, and the association between WMI and perfusion alterations was evaluated. RESULTS: The WMI group showed a higher incidence of mild intraventricular hemorrhage (IVH) than the non-WMI group (P<0.05). Significantly lower cerebral perfusion was observed in the WMI group across bilateral frontal, temporal, parietal, and occipital lobes, as well as the basal ganglia and thalamus (P<0.05). After adjusting for gestational age, corrected gestational age at ASL scan, and mild IVH, WMI remained significantly associated with reduced regional perfusion (P<0.05). CONCLUSIONS WMI in preterm infants correlates with localized cerebral hypoperfusion. ASL-detected perfusion abnormalities may provide novel insights into WMI pathogenesis.
Humans ; White Matter/blood supply* ; Infant, Newborn ; Spin Labels ; Infant, Premature ; Female ; Male ; Cerebrovascular Circulation ; Magnetic Resonance Imaging

OBJECTIVES: To study the clinical and imaging features of children with influenza-associated encephalopathy (IAE), and to investigate the influencing factors for prognosis. METHODS: A retrospective analysis was conducted on the medical data (clinical data, laboratory examinations, imaging data, and prognosis) of 23 children with IAE who were diagnosed and treated in Children's Hospital of Nanjing Medical University from May 2022 to April 2023. RESULTS: Among the 23 patients, 18 (78%) had influenza A and 5 (22%) had influenza B. All patients had fever and encephalopathy, and 20 patients (87%) had seizures, while 11 patients (48%) had persistent convulsions. There were 10 patients (43%) with an increase in alanine aminotransferase, 14 (61%) with an increase in aspartate aminotransferase, and 18 (78%) with an increase in lactate dehydrogenase. Abnormal imaging findings were observed in 20 patients (87%), among whom 10 (43%) had acute necrotizing encephalopathy. All 23 patients received peramivir or oseltamivir. Of all patients, 12 (52%) achieved complete recovery, 5 (22%) had varying degrees of neurological dysfunction, and 6 (26%) died. Compared with the good prognosis group, the poor prognosis group had significantly higher levels of alanine aminotransferase, aspartate aminotransferase, and lactate dehydrogenase (P<0.05). CONCLUSIONS Fever and convulsions are the most common symptoms of children with IAE, and acute necrotizing encephalopathy is the most common clinical imaging syndrome. Increases in alanine aminotransferase, aspartate aminotransferase, and lactate dehydrogenase have a certain value in predicting poor prognosis.
Humans ; Influenza, Human/complications* ; Male ; Prognosis ; Female ; Child, Preschool ; Retrospective Studies ; Infant ; Child ; Brain Diseases/etiology*

OBJECTIVE: To establish a genotyping method for Diego, MNS and Kell blood groups based on quantitative real-time PCR (qPCR) technology, and preliminarily apply it to the screening of rare blood groups in blood donors. METHODS: Blood group gene standards containing heterozygous and homozygous alleles were prepared by blood group serological and PCR-SBT methods. Specific amplification primers and hybridization probes were designed, and explore to establish the qPCR method for detecting Diego, MNS, and Kell blood group genotypes. Then the established qPCR method was used to identify blood group genotypes of 186 blood donor samples. RESULTS: A method based on qPCR technology was established to identify Dia/Dib, S/s and K/k blood group antigens. The genotyping results of the gene standard samples were consistent with the serological testing results and genotypes detected by PCR-SBT. qPCR testing of 186 samples identified 11 cases of DI*A/B heterozygosity and 19 cases of GYPB*S/s heterozygosity, and the rest were DI*B/B, GYPB*s/s, KEL*02/02 homozygosity. No rare blood group genotypes of DI*A/A, GYPB*S/S, KEL*01.01/01.01 were found. CONCLUSION The established qPCR method is suitable for genotyping on Diego, MNS and Kell blood group, and it can be used for batch screening of blood donors and the establishment of rare blood group bank.
Humans ; Genotype ; Genotyping Techniques/methods* ; Real-Time Polymerase Chain Reaction/methods* ; Blood Group Antigens/genetics* ; Kell Blood-Group System/genetics* ; Blood Donors ; Blood Grouping and Crossmatching/methods* ; Erythrocytes ; MNSs Blood-Group System/genetics*