Abstract Traditional Chinese medicine (TCM) auscultation has a long history, and with advancements in equipment and analytical methods, the quantitative analysis of auscultation parameters has determined. However, the complexity and diversity of auscultation, along with variations in devices, analytical methods, and applications, bring challenges to its standardization and deeper application. This review presents the advancements in auscultation equipment and systems, auscultation characteristic parameters, and their application in the diagnosis of pulmonary diseases and syndromes over the past 10 years, while also exploring the progress and challenges of current digital research of auscultation. This review also proposes the establishment of standardized protocols for the collection and analysis of auscultation data, the incorporation of advanced artificial intelligence (AI) auscultation analysis methods, and an exploration of the diagnostic utility of auscultatory features in pulmonary diseases and syndromes, so as to provide more precise decision support for intelligent diagnosis of pulmonary diseases and syndromes

The continuous emergence of SARS-CoV-2 variants as well as other potential future coronavirus has challenged the effectiveness of current COVID-19 vaccines. Therefore, there remains a need for alternative antivirals that target processes less susceptible to mutations, such as the formation of six-helix bundle (6-HB) during the viral fusion step of host cell entry. In this study, a novel high-throughput screening (HTS) assay employing a yeast-two-hybrid (Y2H) system was established to identify inhibitors of HR1/HR2 interaction. The compound IMB-9C, which achieved single-digit micromolar inhibition of SARS-CoV-2 and its Omicron variants with low cytotoxicity, was selected. IMB-9C effectively blocks the HR1/HR2 interaction in vitro and inhibits SARS-CoV-2-S-mediated cell-cell fusion. It binds to both HR1 and HR2 through non-covalent interaction and influences the secondary structure of HR1/HR2 complex. In addition, virtual docking and site-mutagenesis results suggest that amino acid residues A930, I931, K933, T941, and L945 are critical for IMB-9C binding to HR1. Collectively, in this study, we have developed a novel screening method for HR1/HR2 interaction inhibitors and identified IMB-9C as a potential antiviral small molecule against COVID-19 and its variants.

Objective: To explore the feasibility of constructing a lung cancer early-warning risk model based on facial image features, providing novel insights into the early screening of lung cancer. Methods: This study included patients with pulmonary nodules diagnosed at the Physical Examination Center of Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine from November 1, 2019 to December 31, 2024, as well as patients with lung cancer diagnosed in the Oncology Departments of Yueyang Hospital of Integrated Traditional Chinese and Western Medicine and Longhua Hospital during the same period. The facial image information of patients with pulmonary nodules and lung cancer was collected using the TFDA-1 tongue and facial diagnosis instrument, and the facial diagnosis features were extracted from it by deep learning technology. Statistical analysis was conducted on the objective facial diagnosis characteristics of the two groups of participants to explore the differences in their facial image characteristics, and the least absolute shrinkage and selection operator (LASSO) regression was used to screen the characteristic variables. Based on the screened feature variables, four machine learning methods: random forest, logistic regression, support vector machine (SVM), and gradient boosting decision tree (GBDT) were used to establish lung cancer classification models independently. Meanwhile, the model performance was evaluated by indicators such as sensitivity, specificity, F1 score, precision, accuracy, the area under the receiver operating characteristic (ROC) curve (AUC), and the area under the precision-recall curve (AP). Results: A total of 1 275 patients with pulmonary nodules and 1 623 patients with lung cancer were included in this study. After propensity score matching (PSM) to adjust for gender and age, 535 patients were finally included in the pulmonary nodule group and the lung cancer group, respectively. There were significant differences in multiple color space metrics (such as R, G, B, V, L, a, b, Cr, H, Y, and Cb) and texture metrics [such as gray-levcl co-occurrence matrix (GLCM)-contrast (CON) and GLCM-inverse different moment (IDM)] between the two groups of individuals with pulmonary nodules and lung cancer (P < 0.05). To construct a classification model, LASSO regression was used to select 63 key features from the initial 136 facial features. Based on this feature set, the SVM model demonstrated the best performance after 10-fold stratified cross-validation. The model achieved an average AUC of 0.8729 and average accuracy of 0.799 0 on the internal test set. Further validation on an independent test set confirmed the model’s robust performance (AUC = 0.823 3, accuracy = 0.729 0), indicating its good generalization ability. Feature importance analysis demonstrated that color space indicators and the whole/lip Cr components (including color-B-0, wholecolor-Cr, and lipcolor-Cr) were the core factors in the model’s classification decisions, while texture indicators [GLCM-angular second moment (ASM)_2, GLCM-IDM_1, GLCM-CON_1, GLCM-entropy (ENT)_2] played an important auxiliary role. Conclusion The facial image features of patients with lung cancer and pulmonary nodules show significant differences in color and texture characteristics in multiple areas. The various models constructed based on facial image features all demonstrate good performance, indicating that facial image features can serve as potential biomarkers for lung cancer risk prediction, providing a non-invasive and feasible new approach for early lung cancer screening.

As a common ophthalmic disease, pterygium exhibits a considerably high incidence in global area, particularly in regions with harsh natural environment. While it is unlikely to cause blindness, pterygium frequently disrupts the normal homeostasis of the tear film, leading to discomfort such as dry eyes and foreign body sensation, thereby significantly impairing patients' quality of life. Furthermore, the high surgical intervention rate for pterygium consumes substantial medical resources. Therefore, investigating the underlying mechanisms of pterygium development is essential for its prevention and control. Extensive research has demonstrated the correlation between solar radiation and pterygium occurrence, confirming that ultraviolet rays within solar radiation contribute to the onset and progression of pterygium through multiple pathways, including direct DNA damage to ocular surface cells, oxidative stress-induced injury, involvement in inflammatory regulation, and extracellular matrix remodeling. This paper reviews both domestic and international epidemiological trends of pterygium, as well as the multifaceted mechanisms by which solar radiation influences pterygium development. The aim is to enhance understanding of the relationship between solar exposure and pterygium occurrence, while emphasizing and guiding public awareness of solar radiation protection in clinical practice.

OBJECTIVE: To observe the effects of electroacupuncture at the pterygopalatine region on nucleotide-binding oligomerization domain-like receptor protein 3 (NLRP3)-mediated pyroptosis and inflammatory factors in rats with allergic rhinitis (AR). METHODS: Twenty-four SD rats were randomly divided into a blank group, a model group, an acupuncture group and an electroacupuncture group, 6 rats in each group. Except for the blank group, OVA-induced AR model was established in the remaining groups. In the electroacupuncture group, the rats were treated with electroacupuncture at the bilateral pterygopalatine region, with disperse-dense wave, in frequency of 2 Hz/100 Hz and current of 0.5-1 mA, 15 min each time, once every other day, for 3 times. In the acupuncture group, the rats were treated with acupuncture at bilateral pterygopalatine region simply, without electrical stimulation. The rhinitis symptom score was observed, the pathomorphology of the nasal mucosa was observed by HE staining; the serum levels of OVA-specific immunoglobulin E (OVA-sIgE), interleukin (IL)-4, IL-6 and IL-1β were detected by ELISA; the mRNA expression of NLRP3, apoptosis-associated speck-like protein containing CARD (ASC), cysteinyl aspartate specific proteinase-1 (caspase-1) and IL-18 in the nasal mucosa was detected by real-time PCR; the protein expression of NLRP3, ASC, caspase-1 and IL-18 in the nasal mucosa was detected by Western blot. RESULTS: Compared with the blank group, in the model group, the rhinitis symptom score was increased (P<0.01), the serum levels of OVA-sIgE, IL-4, IL-6 and IL-1β were increased (P<0.05), the nasal mucosa showed pathomorphology of inflammatory infiltration; the mRNA and protein expression of NLRP3, ASC, caspase-1 and IL-18 in the nasal mucosa was increased (P<0.05). Compared with the model group, in the electroacupuncture group, the rhinitis symptom score was reduced (P<0.01), the pathology of the nasal mucosa was improved; the serum levels of OVA-sIgE, IL-4, IL-6 and IL-1β were decreased (P<0.05); the mRNA and protein expression of NLRP3, ASC, caspase-1 and IL-18 in the nasal mucosa was decreased (P<0.05). CONCLUSION Electroacupuncture at the pterygopalatine region can exerting the anti-inflammatory effect by inhibiting NLRP3-mediated pyroptosis and inflammatory factor imbalance, thus alleviate rhinitis symptoms in AR rats.
Animals ; Electroacupuncture ; NLR Family, Pyrin Domain-Containing 3 Protein/immunology* ; Rats ; Rats, Sprague-Dawley ; Rhinitis, Allergic/physiopathology* ; Pyroptosis ; Male ; Acupuncture Points ; Humans ; Female ; Interleukin-1beta/genetics* ; Interleukin-18/immunology* ; Interleukin-6/genetics* ; Caspase 1/immunology*

BACKGROUND: Physical activity, sedentary behavior (SB), and sleep duration are associated with brain health. Effects of those on developing Parkinson's disease (PD) are poorly investigated. This study aimed to examine the independent and joint associations of physical activity, SB, sleep with PD risk. METHODS: We analyzed data on 401,697 participants from the UK Biobank cohort, which was enrolled in 2006-2010. Physical activities were measured based on a questionnaire. Sleep and SB time were defined through self-reported total number of hours. Models fitted with restricted cubic spline were conducted to test for linear and non-linear shapes of each association. Cox proportional hazards regression models were used to estimate the association of three modifiable behaviors. RESULTS: Our analytic sample included 401,697 participants with 3030 identified cases of PD (mean age, 63 years; 62.9% male). PD risk was 18% lower in the high total physical activity group (95% CI, 0.75-0.90), 22% lower in the high leisure-time physical activity (LTPA) group (95% CI, 0.71-0.86) compared with the low level and 14% higher in the high sleep duration group (95% CI, 1.05-1.24) compared to moderate group. Total SB time was irrelevant with PD risk, while high TV viewing showed a 12% increase of PD risk compared to the low group (95% CI, 1.02-1.22). Low computer use (0 h/day) was associated with a 14% higher risk compared to 1 h/day use (95% CI, 1.04-1.26). Those associations were independent. A combination of 7 h/day sleep, moderate-to-high computer use, and moderate-to-vigorous intensity of LTPA showed lowest PD risk (HR, 0.70; 95% CI, 0.57-0.85). CONCLUSIONS Physical activity, SB, and sleep were associated with PD risks separately. Our findings emphasize the possibility for changing these three daily activities concurrently to lower the risk of PD. These findings may promote an active lifestyle for PD prevention.
Humans ; Parkinson Disease/physiopathology* ; Male ; Sedentary Behavior ; Female ; Middle Aged ; Exercise/physiology* ; Prospective Studies ; Sleep/physiology* ; Aged ; Surveys and Questionnaires ; Proportional Hazards Models ; Risk Factors

BACKGROUND: Neuroticism has been associated with numerous health outcomes. However, most research has focused on a single specific disorder and has produced controversial results, particularly regarding mortality risk. Here, we aimed to examine the association of neuroticism with morbidity and mortality and to elucidate how neuroticism affects trajectories from a healthy state, to one or more neuroticism-related disorders, and subsequent mortality risk. METHODS: We included 483,916 participants from the UK Biobank at baseline (2006-2010). Neuroticism was measured using the Eysenck Personality Questionnaire. Three clusters were constructed, including worry, depressed affect, and sensitivity to environmental stress and adversity (SESA). Cox proportional hazards regression and multistate models were used. Linear regression was used to examine the association between neuroticism and immune parameters and neuroimaging measures. RESULTS: High neuroticism was associated with 37 non-overlapping diseases, including increased risk of infectious, cardiometabolic, neuropsychiatric, digestive, and respiratory diseases, and decreased risk of cancer. After adjustment for sociodemographic variables, physical measures, healthy behaviors, and baseline diagnoses, moderate-to-high neuroticism was associated with a decreased risk of all-cause mortality. In multistate models, high neuroticism was associated with an increased risk of transitions from a healthy state to a first neuroticism-related disease (hazard ratio [HR] [95% confidence interval (CI)] = 1.09 [1.05-1.13], P  <0.001) and subsequent transitions to multimorbidity (1.08 [1.02-1.14], P  = 0.005), but was associated with a decreased risk of transitions from multimorbidity to death (0.90 [0.84-0.97], P for trend = 0.006). The leading neuroticism cluster showing a detrimental role in the health-illness transition was depressed affect, which correlated with higher amygdala volume and lower insula volume. The protective effect of neuroticism against mortality was mainly contributed by the SESA cluster, which, unlike the other two clusters, did not affect the balance between innate and adaptive immunity. CONCLUSION This study provides new insights into the differential role of neuroticism in health outcomes and into new perspectives for establishing mortality prevention programs for patients with multimorbidity.
Humans ; Neuroticism/physiology* ; Male ; Female ; Middle Aged ; Aged ; Proportional Hazards Models ; Surveys and Questionnaires ; Adult ; Risk Factors

Protein structure determines function, and structural information is critical for predicting protein thermostability. This study proposes a novel method for protein thermostability prediction by integrating graph embedding features and network topological features. By constructing residue interaction networks (RINs) to characterize protein structures, we calculated network topological features and utilize deep neural networks (DNN) to mine inherent characteristics. Using DeepWalk and Node2vec algorithms, we obtained node embeddings and extracted graph embedding features through a TopN strategy combined with bidirectional long short-term memory (BiLSTM) networks. Additionally, we introduced the Doc2vec algorithm to replace the Word2vec module in graph embedding algorithms, generating graph embedding feature vector encodings. By employing an attention mechanism to fuse graph embedding features with network topological features, we constructed a high-precision prediction model, achieving 87.85% prediction accuracy on a bacterial protein dataset. Furthermore, we analyzed the differences in the contributions of network topological features in the model and the differences among various graph embedding methods, and found that the combination of DeepWalk features with Doc2vec and all topological features was crucial for the identification of thermostable proteins. This study provides a practical and effective new method for protein thermostability prediction, and at the same time offers theoretical guidance for exploring protein diversity, discovering new thermostable proteins, and the intelligent modification of mesophilic proteins.
Neural Networks, Computer ; Algorithms ; Protein Stability ; Proteins/chemistry* ; Protein Conformation ; Temperature

[This corrects the article DOI: 10.1016/j.apsb.2021.07.004.].

The oxytocin receptor (OXTR) has garnered increasing attention for its role in regulating both mature behaviors and brain development. It has been established that OXTR mediates a range of effects that are region-specific or period-specific. However, the current studies of OXTR expression patterns in mice only provide limited help due to limitations in resolution. Therefore, our objective was to generate a comprehensive, high-resolution spatiotemporal expression map of Oxtr mRNA across the entire developing mouse brain. We applied RNAscope in situ hybridization to investigate the spatiotemporal expression pattern of Oxtr in the brains of male mice at six distinct postnatal developmental stages (P7, P14, P21, P28, P42, P56). We provide detailed descriptions of Oxtr expression patterns in key brain regions, including the cortex, basal forebrain, hippocampus, and amygdaloid complex, with a focus on the precise localization of Oxtr⁺ cells and the variance of expression between different neurons. Furthermore, we identified some neuronal populations with high Oxtr expression levels that have been little studied, including glutamatergic neurons in the ventral dentate gyrus, Vgat⁺Oxtr⁺ cells in the basal forebrain, and GABAergic neurons in layers 4/5 of the cortex. Our study provides a novel perspective for understanding the distribution of Oxtr and encourages further investigations into its functions.
Animals ; Receptors, Oxytocin/metabolism* ; Male ; Brain/growth & development* ; Mice ; Mice, Inbred C57BL ; Neurons/metabolism* ; Single-Cell Analysis ; Gene Expression Regulation, Developmental ; RNA, Messenger/metabolism* ; Animals, Newborn