OBJECTIVE:Patients with osteoporotic vertebral compression fractures still have residual back pain after vertebral augmentation.The current research is characterized by limited sample size,complex confounding factors,and inconsistent research results.To gain a deeper understanding of this phenomenon,the aim of this study was to identify and evaluate the risk factors for residual back pain after surgery through a systematic review and meta-analysis.METHODS:A comprehensive search was conducted in CNKI,VIP,WanFang,CBMdisc,PubMed,The Cochrane Library,Embase,and Web of Science for case-control studies on residual back pain after vertebral body augmentation for osteoporotic vertebral compression fractures from database inception to July 2024.The search terms were a combination of subject terms and free terms.The basic information,patient characteristics,surgical-related indicators,and risk factors for surgical back pain of the included studies were extracted.After evaluating the bias risk of all included studies,a meta-analysis was conducted using Stata 14.0 software on the relevant indicators.RESULTS:(1)21 case-control studies with a total of 8 043 patients were included.Among them,965 patients developed back pain.The quality score of all 21 studies was ≥7.(2)The meta-analysis results showed that age(WMD=0.98,95％CI:0.40-1.56,P=0.010),bone mineral density(WMD=-0.28,95％CI:-0.34 to-0.21,P=0.000),the number of vertebral fractures(OR=3.50,95％CI:2.65-4.62,P=0.000),thoracolumbar fracture index(OR=3.65,95％CI:2.61-5.11,P=0.000),cement volume(OR=6.89,95％CI:2.62-18.17,P=0.000),and cement distribution(OR=2.38,95％CI:1.93-2.93,P=0.000)were risk factors for the development of back pain after vertebral body augmentation in patients with osteoporotic vertebral compression fractures.CONCLUSION:Current evidence indicates that age,bone mineral density,the number of vertebral fractures,thoracolumbar fracture index,bone cement injection volume,and the distribution of bone cement are risk factors for low back pain.Specifically,bone mineral density,the number of vertebral fractures,thoracolumbar fracture index,and non-uniform distribution of bone cement are identified as independent risk factors for low back pain.Patients exhibiting these high-risk factors require vigilant monitoring and prompt intervention to mitigate the occurrence of clinical low back pain,thereby enhancing patient outcomes and quality of life.

OBJECTIVE:Patients with osteoporotic vertebral compression fractures still have residual back pain after vertebral augmentation.The current research is characterized by limited sample size,complex confounding factors,and inconsistent research results.To gain a deeper understanding of this phenomenon,the aim of this study was to identify and evaluate the risk factors for residual back pain after surgery through a systematic review and meta-analysis.METHODS:A comprehensive search was conducted in CNKI,VIP,WanFang,CBMdisc,PubMed,The Cochrane Library,Embase,and Web of Science for case-control studies on residual back pain after vertebral body augmentation for osteoporotic vertebral compression fractures from database inception to July 2024.The search terms were a combination of subject terms and free terms.The basic information,patient characteristics,surgical-related indicators,and risk factors for surgical back pain of the included studies were extracted.After evaluating the bias risk of all included studies,a meta-analysis was conducted using Stata 14.0 software on the relevant indicators.RESULTS:(1)21 case-control studies with a total of 8 043 patients were included.Among them,965 patients developed back pain.The quality score of all 21 studies was ≥7.(2)The meta-analysis results showed that age(WMD=0.98,95％CI:0.40-1.56,P=0.010),bone mineral density(WMD=-0.28,95％CI:-0.34 to-0.21,P=0.000),the number of vertebral fractures(OR=3.50,95％CI:2.65-4.62,P=0.000),thoracolumbar fracture index(OR=3.65,95％CI:2.61-5.11,P=0.000),cement volume(OR=6.89,95％CI:2.62-18.17,P=0.000),and cement distribution(OR=2.38,95％CI:1.93-2.93,P=0.000)were risk factors for the development of back pain after vertebral body augmentation in patients with osteoporotic vertebral compression fractures.CONCLUSION:Current evidence indicates that age,bone mineral density,the number of vertebral fractures,thoracolumbar fracture index,bone cement injection volume,and the distribution of bone cement are risk factors for low back pain.Specifically,bone mineral density,the number of vertebral fractures,thoracolumbar fracture index,and non-uniform distribution of bone cement are identified as independent risk factors for low back pain.Patients exhibiting these high-risk factors require vigilant monitoring and prompt intervention to mitigate the occurrence of clinical low back pain,thereby enhancing patient outcomes and quality of life.

ObjectiveTo investigate the ability of clinically isolated， Helicobacter pylori （H. pylori）-specific gene polymerase chain reaction （PCR）-positive gastric， vaginal， and fecal Candida to release H. pylori. MethodsResuscitate 4 strains of H. pylori -specific 16S rDNA and ureA gene PCR-positive Candida strains isolated in laboratory from clinical sources， including 1 strain of gastric Candida， 1 strain of fecal Candida， 2 strains of vaginal Candida and the standard Candida albicans strain ATCC10231 （Ca10231）. The presence of H. pylori-specific ureA in the 5 strains of Candida isolates was confirmed by PCR. The aforementioned strains of Candida and H.pylori were inoculated into urea medium and cultured in a constant temperature incubator at 37 ℃. The color change of the medium was observed daily. A change in the medium's color from yellow to red indicated the presence of urease activity. Then， the five strains of Candida and H. pylori were co-incubated with the magnetic beads coated with H. pylori antibodies respectively. Scanning electron microscopy （SEM） was employed to observe the presence of bacilli adsorbed on the surface of the magnetic beads. PCR was used to detect the presence of H.pylori-specific 16S rDNA and ureA genes on magnetic beads. ResultsThe PCR analysis of the ureA gene in the four Candida isolates was positive， whereas the Ca10231 strain tested negative. Upon culturing the four Candida isolates on urea medium， the medium color changed from yellow to red which was determined to be urease positive， while the medium containing Ca10231 remained unchanged， which was urease negative. SEM revealed that bacilli could be observed on the surface of magnetic beads co-incubated with the 4 strains of Candida of clinical origin and H.pylori isolate. Specifically， PCR testing of the magnetic beads co-incubated with one vaginal Candida， one gastric Candida and H.pylori isolate showed positive results for the 16S rDNA and ureA genes of H. pylori； however， the PCR tests for the two genes were negative for the magnetic beads co-incubated with the other two Candida isolate. ConclusionThis study demonstrates that H. pylori-specific genes Candida can release H. pylori.

ObjectiveAutoimmune thyroiditis （AIT） is a complex and immune-mediated disorder， with no established treatment protocol. Both Western and traditional Chinese medicine （TCM） focus on the pathogenesis and treatment of AIT. This study evaluated the clinical consistency of existing AIT animal models based on the diagnostic criteria of both Western and TCM， using a novel evaluation method. Additionally， it proposed recommendations and future prospects for improving these models. MethodsA comprehensive literature review was conducted on existing AIT animal models， using databases and the diagnostic criteria of both Western and TCM. Core and accompanying symptoms of these models were scored based on the diagnostic criteria of both Western and TCM， and clinical consistency was assessed. ResultsMice are the primary experimental animals used in AIT modeling. Modeling methods include vaccine immunization， iodine induction， heterologous thyroid antigen immunization， and a combination of high iodine water and antigen immunization. The average consistency of clinical syndromes based on TCM and Western medicine is 40%， 60%， 54%， and 63%， with the highest consistency observed in the combined high iodine water and antigen immunization model. Pathological models based on TCM are less common， with the liver-stagnation-spleen-deficiency rat model showing high clinical consistency. While most models are designed according to Western medical theory， meeting the surface and structural effectiveness criteria of Western medicine. However， there is a lack of fine-tuning and clear differentiation of TCM syndromes. ConclusionCurrent AIT syndrome-disease combination animal models primarily reflect the pathological features of Western medicine， with limited integration of TCM syndromes. Future research should aim to combine the syndrome characteristics of TCM with the pathological features of Western medicine， creating multi-factor and dynamic syndrome-disease models. Such models would better facilitate an experimental platform that conforms to the theories of TCM， providing more comprehensive support and guidance for the pathogenesis and treatment strategies of AIT.

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 To understand the disease spectrum of a natural village in an area with high incidence of esophageal cancer to provide a reference for precise prevention and control. Methods From 2008 to 2024, 711 asymptomatic people over the age of 35 years in a natural village with high incidence of esophageal cancer in China were surveyed, and 171 of them were subjected to gastroscopy, biopsy, and pathological examination. All participants were followed up for a long time, and their disease history was recorded. Results A total of 16 years of follow-up were performed, and 703 people were effectively followed up. In 2008, 171 people underwent gastroscopy, and 160 people had biopsy and pathological results in endoscopic screening. By 2024, 76 people had been diagnosed with malignant tumors of 12 different types, and among these people, 45 had esophageal cancer. Conclusion Esophageal cancer remains a significant cause of morbidity and mortality from malignant tumors in this region. Biopsy and pathological examination should be strengthened during gastroscopy, and follow-ups and regular check-ups should be given high importance to reduce the incidence and mortality rates of esophageal cancer.

AIM: To investigate the abnormal conditions and change patterns of accommodative facility in patients with different refractive states before and after small incision lenticule extraction(SMILE)surgery.METHODS:A prospective clinical cohort study was conducted. A total of 59 patients(118 eyes)who underwent SMILE surgery and had visual function files established in our hospital from June to December 2023 were randomly selected, including 37 males and 22 females, aged 18-35 years(with an average age of 25.19±5.65 years). According to the preoperative spherical equivalent(SE), they were divided into two groups: the low-to-moderate myopia group(SE≥-6.00 DS)with 40 patients(80 eyes), and the high myopia group(SE<-6.00 DS)with 19 patients(38 eyes). The monocular and binocular accommodative facility before surgery and at 1 wk and 1 mo after surgery were compared, and the changes in accommodative facility before and after SMILE surgery in the two groups of patients were analyzed.RESULTS:All surgeries were completed successfully. In the low-to-moderate myopia group, 33 cases(66 eyes)completed the 1-month follow-up after surgery, with a loss to follow-up rate of 17.5%(7/40). In the high myopia group, 15 patients(30 eyes)completed the 1-month follow-up after surgery, with a loss to follow-up rate of 21.1%(4/19). After SMILE surgery, the uncorrected visual acuity and SE of both low-to-moderate myopia and high myopia were significantly improved(all P<0.05). The accommodative facility of the right eyes in all the patients at 1 mo after surgery was better than that before surgery and at 1 wk after surgery(P=0.002, 0.006), the accommodative facility of the left eyes was significantly increased at 1 mo after surgery than that at 1 wk after surgery(P=0.005), and the binocular accommodative facility at 1 mo after surgery was significantly increased compared with that before surgery(P<0.017). Furthermore, there were statistical significance in accommodative facility of the right eyes in the low-to-moderate group at 1 mo compared with that before surgery and at 1 wk after surgery(P=0.011, 0.004); it was significantly increased in the left eyes at 1 mo after surgery compared with that at 1 wk after surgery(P=0.001), and binocular accommodative facility at 1 mo after surgery was significantly better than that before surgery(P<0.001). Furthermore, there was no statistical significance in the right, left and binocular accommodative facility of patients in the high myopia group(all P>0.017).CONCLUSION: After SMILE surgery, the monocular accommodative facility shows a transient decrease and then exceeds the preoperative level at 1 mo after surgery, and the binocular accommodative facility gradually improves after surgery. SMILE surgery has a positive impact on the monocular and binocular accommodative facility in patients with low-to-moderate myopia, but has no significant impact on the accommodative facility in patients with high myopia. It is of clinical significance to strengthen the detection of monocular and binocular accommodative facility before and after SMILE surgery.

Obstructive sleep apnea (OSA) is an increasingly widespread sleep-breathing disordered disease, and is an independent risk factor for many high-risk chronic diseases such as hypertension, coronary heart disease, stroke, arrhythmias and diabetes, which is potentially fatal. The key to the prevention and treatment of OSA is early diagnosis and treatment, so the assessment and diagnostic technologies of OSA have become a research hotspot. This paper reviews the research progresses of severity assessment parameters and diagnostic technologies of OSA, and discusses their future development trends. In terms of severity assessment parameters of OSA, apnea hypopnea index (AHI), as the gold standard, together with the percentage of duration of apnea hypopnea (AH%), lowest oxygen saturation (LSpO₂), heart rate variability (HRV), oxygen desaturation index (ODI) and the emerging biomarkers, constitute a multi-dimensional evaluation system. Specifically, the AHI, which measures the frequency of sleep respiratory events per hour, does not fully reflect the patients’ overall sleep quality or the extent of their daytime functional impairments. To address this limitation, the AH%, which measures the proportion of the entire sleep cycle affected by apneas and hypopneas, deepens our understanding of the impact on sleep quality. The LSpO₂ plays a critical role in highlighting the potential severe hypoxic episodes during sleep, while the HRV offers a different perspective by analyzing the fluctuations in heart rate thereby revealing the activity of the autonomic nervous system. The ODI provides a direct and objective measure of patients’ nocturnal oxygenation stability by calculating the number of desaturation events per hour, and the biomarkers offers novel insights into the diagnosis and management of OSA, and fosters the development of more precise and tailored OSA therapeutic strategies. In terms of diagnostic techniques of OSA, the standardized questionnaire and Epworth sleepiness scale (ESS) is a simple and effective method for preliminary screening of OSA, and the polysomnography (PSG) which is based on recording multiple physiological signals stands for gold standard, but it has limitations of complex operations, high costs and inconvenience. As a convenient alternative, the home sleep apnea testing (HSAT) allows patients to monitor their sleep with simplified equipment in the comfort of their own homes, and the cardiopulmonary coupling (CPC) offers a minimal version that simply analyzes the electrocardiogram (ECG) signals. As an emerging diagnostic technology of OSA, machine learning (ML) and artificial intelligence (AI) adeptly pinpoint respiratory incidents and expose delicate physiological changes, thus casting new light on the diagnostic approach to OSA. In addition, imaging examination utilizes detailed visual representations of the airway’s structure and assists in recognizing structural abnormalities that may result in obstructed airways, while sound monitoring technology records and analyzes snoring and breathing sounds to detect the condition subtly, and thus further expands our medical diagnostic toolkit. As for the future development directions, it can be predicted that interdisciplinary integrated researches, the construction of personalized diagnosis and treatment models, and the popularization of high-tech in clinical applications will become the development trends in the field of OSA evaluation and diagnosis.

Virus-related lymphoma exhibits a dual nature as both a hematologic malignancy and a viral infectious disease， making it more resistant to treatment and associated with poorer prognosis. This paper analyzes the understanding and therapeutic advantages of traditional Chinese medicine （TCM） in virus-related lymphoma. It proposes a TCM-based approach centered around syndrome differentiation， using standardized measurements of the overall TCM condition， multi-omics research of hematologic tumors， and artificial intelligence technologies to identify the "pre-condition" of virus-related lymphoma. A risk warning model will be established to early identify high-risk populations with viral infections that may develop into malignant lymphoma， thereby establishing a risk warning system for virus-related lymphoma. At the same time， a TCM health management approach will be applied to manage and regulate virus-related lymphoma， interrupting its progression and forming a human-centered， comprehensive， continuous health service model. Based on this， a standardized， integrated clinical prevention and treatment decision-making model for virus-related lymphoma， recognized by both Chinese and western medicine， will be established to provide TCM solutions for primary prevention of major malignant tumors.

Objective: To investigate the distribution of traditional Chinese medicine (TCM) syndromes and syndrome elements in lymphoma, as well as the correlation between TCM syndromes and Western clinical indicators, in order to analyze associations between TCM syndromes and these indicators. Methods: From January 2023 to May 2024, 216 patients with lymphoma who met the inclusion criteria in the Department of Hematology, Third People′s Hospital Affiliated to Fujian University of Traditional Chinese Medicine were enrolled. Four diagnostic methods were applied to perform TCM syndrome differentiation and extract syndrome elements. The correlations between various syndromes and blood test indicators of lactate dehydrogenase (LDH), β2-microglobulin (β2-MG), immunoglobulin G (IgG), immunoglobulin M (IgM), immunoglobulin A (IgA), white blood cell (WBC), hemoglobin (Hb), platelet count (PLT), neutrophil (NEUT), immunohistochemical markers of B-cell lymphoma-6 (BCL6), B-cell lymphoma-2 (BCL2), proto-oncogene MYC, and Ki67 protein expression, Ann Arbor staging, international prognostic index (IPI) score, bone marrow infiltration, concurrent infections during chemotherapy, and post-chemotherapy bone marrow suppression rate were analyzed. Results: Five TCM syndromes, ranked by frequency, were syndromes of yin deficiency with phlegm accumulation(41.67%), qi depression with phlegm obstruction(30.56%), cold-phlegm congelation and stagnation(12.96%), phlegm-blood stasis toxin(12.04%), and lingering pathogen due to deficient vital qi(2.77%). Yin deficiency(50.93%) and phlegm(45.37%) were the more prevalent syndrome elements. The TCM syndromes were correlated with β2-MG, PLT, MYC, BCL2/MYC, Ki67 protein expression, and bone marrow infiltration (P<0.05). No statistically significant differences were observed in Ann Arbor staging or IPI score across the syndromes. Compared to the syndrome of cold-phlegm congelation and stagnation, the syndrome of qi depression with phlegm obstruction exhibited higher levels of NEUT, MYC, BCL2/MYC, and Ki67 protein expression, as well as a higher rate of post-chemotherapy bone marrow suppression (P<0.05); the syndrome of phlegm-blood stasis toxin showed higher MYC and BCL2/MYC protein expression and a higher rate of post-chemotherapy bone marrow suppression rate (P<0.05); the syndrome of yin deficiency with phlegm accumulation demonstrated higher MYC and BCL2/MYC protein expression and bone marrow infiltration rates, whereas PLT level was lower (P<0.05); the syndrome of lingering pathogen due to deficient vital qi had higher MYC, BCL2/MYC, and Ki67 protein expression levels, as well as a higher rate of post-chemotherapy bone marrow suppression rate (P<0.05). Compared to the syndrome of qi depression with phlegm obstruction, the syndrome of phlegm-blood stasis toxin exhibited lower Ki67 protein expression (P<0.05); the syndrome of yin deficiency with phlegm accumulation had higher β2-MG level, bone marrow infiltration rate, and rate of concurrent infections during chemotherapy, whereas PLT and NEUT levels and the rate of post-chemotherapy bone marrow suppression rate were lower (P<0.05). Compared to the syndrome of phlegm-blood stasis toxin, the syndrome of yin deficiency with phlegm accumulation had higher β2-MG level, whereas NEUT and the rate of post-chemotherapy bone marrow suppression were lower(P<0.05); the syndrome of lingering pathogen due to deficient vital qi exhibited a higher Ki67 protein expression (P<0.05). Compared to the syndrome of yin deficiency with phlegm accumulation, the syndrome of lingering pathogen due to deficient vital qi also showed a higher Ki67 protein expression(P<0.05). Conclusion The syndrome of yin deficiency with phlegm accumulation is relatively common in lymphoma. There is a correlation between TCM syndromes and Western medicine clinical indicators. The presence of heat signs in the syndromes may indicate active disease and poor prognosis, while the presence of strong pathogenic factors and weak vital qi in the syndromes may indicate a severer chemotherapy-related bone marrow suppression.