The existing wound assessment tools, which are based on modern medical theory, limit the clinical application of traditional Chinese medicine (TCM) nursing. This research aimed to develop a scientific, standardized, and characteristic TCM nursing evaluation form for chronic wounds.

Based on a literature review and research group discussions, an initial draft of an expert consultation questionnaire, based on literature from the past five years (2017–2021) from databases such as CNKI, Wanfang, VIP, and SinoMed, was formulated. The authority of the experts was expressed using the authority coefficient, derived from self-evaluations, which is critical for ensuring the scientific validity and rationality of the indicator system. After three rounds of Delphi expert consultation, the TCM nursing assessment form for wound surfaces was finalized.

The effective response rate for the three rounds of expert consultation questionnaires was 100%. The judgment coefficient was 0.85, the familiarity coefficient was 0.89, and the authority coefficient was 0.87. The coefficients of variation for the three rounds were 0.172, 0.044, and 0.013, respectively, while the Kendall’s coefficients of concordance were 0.406, 0.269, and 0.502, respectively, with statistically significant differences (p < 0.001). The final TCM nursing assessment form for wound surfaces included four basic information items, two primary indicators, 17 secondary indicators, and 13 tertiary indicators.

The TCM nursing assessment form integrates TCM syndrome differentiation principles and provides a standardized tool for the assessment of chronic wounds.

Bone marrow metastasis (BMM) from non-hematolymphoid malignancies with resultant cytopenia(s) can mimic primary hematolymphoid disorders. This study aimed to investigate the clinical and pathological characteristics of BMM from non-hematopoietic tumors.

We conducted a retrospective cohort study of patients diagnosed with BMM by non-hematolymphoid malignancies at our institution over the past 10 years. Demographic and clinical characteristics, histopathological findings of bone marrow, types of metastatic tumors, and prognosis were analyzed.

A total of 54 cases were included. The four most common malignancies with BMM, regardless of gender, were prostatic adenocarcinoma (29.6%), breast carcinoma (25.9%), colorectal adenocarcinoma (5.5%), and lung carcinoma (5.5%). The main clinical and laboratory manifestations were anemia (90.7%), reticulocytosis (80.5%), thrombocytopenia (73.9%), bone pain (55.5%), disseminated intravascular coagulation (39.6%), leukoerythroblastosis (35.3%), and leukopenia (24%). The vast majority (96.3%) of metastatic tumors were identified by morphology alone; however, in approximately 2.7% of cases, immunohistochemistry was required due to subtle morphologic features. In 29.6% (16/54) of patients, BMM was identified prior to or concurrently with other metastatic sites. The median time interval between the initial diagnosis of non-hematolymphoid malignancies and BMM was 29 months. Although patients who received anti-tumor treatment after BMM diagnosis showed significantly improved prognosis (P < 0.01), no significant differences were observed between those treated with immunotherapy versus chemotherapy and/or radiotherapy (P = 0.145).

Prostate and breast carcinomas are the most common malignancies associated with BMM, with anemia, reticulocytosis, and thrombocytopenia being the most frequent clinical manifestations. While our data demonstrate that anti-neoplastic treatments, regardless of regimen, significantly improve overall survival after BMM, no significant survival differences were observed when prostate and breast carcinomas were compared with other types of BMM.

Full or partial trisomy of human chromosome 21 results in dysregulation of gene expression, leading to the manifestation of specific phenotypes described in individuals with Down syndrome (DS). Defects in brain development, coupled with impairment in neurogenesis, are ultimately expressed as cognitive deficiency, Alzheimer disease (AD), and dementia. Amid the triplication of all human chromosome 21 (HSA21) genes, dual-specificity tyrosine phosphorylation-regulated kinase 1A (DYRK1A)-mediated neurogenesis and dendritic development have been attributed to the learning and memory deficits and cognitive impairment in the DS population. Upregulated DYRK1A perturbs the development and function of the brain, collectively affecting neurogenesis, synaptogenesis, synaptic transmission, and cell signaling pathways, which might disproportionately produce inhibitory neurotransmission and contribute to the cognitive phenotype. However, the lack of distinct gene-phenotype associations acts as a potential barrier to therapeutic improvement of cognitive performance and amelioration of AD-related neurodegeneration. The present review aims to summarize the neurogenetic consequences of triplicated DYRK1A in the DS population in relation to sexual dimorphism and expression of the Apolipoprotein Eε4 (APOE ε4) genotype. Notably, normalization of trisomic DYRK1A demonstrated improved synaptic plasticity, glutamatergic/GABAergic (excitatory/inhibitory) balance, and learning and memory in DS mouse models. Therapeutic approaches using inhibitors of DYRK1A, including catechins present in green tea extract and several other natural and synthetic agents, produced variable outcomes in cognitive improvement, depending on age and dose of administration. Mitigation of impairment in neurogenetic differentiation and cognitive performance might help control AD-related dementia and enhance quality of life. This review highlights the consequences of upregulated DYRK1A kinase on impairment of neurogenesis and cognitive deficits, and the therapeutic challenges associated with DYRK1A inhibitors for ameliorating dysregulated gene expression in DS models and human DS.

Empirical and theoretical studies can be distinguished among the areas of investigation of the renin-angiotensin-aldosterone system (RAAS) and its relationship with the development of cardiovascular diseases. Theoretical work is based mainly on the bioinformatic analysis of key elements of RAAS (genes, proteins, metabolites), on calculations and predictions of protein interactions, and on mechanisms of RAAS gene expression regulation. An associative gene network based on big data analysis allows us to reveal relationships among the proteins, regulatory pathways, and biological processes acting in RAAS, as well as to identify new diagnostic markers, therapeutic targets, putative molecular mechanisms of the development of RAAS-associated diseases, drug interactions, and drug toxicity.

The reconstruction and analysis of associative gene networks were performed using ANDSystem. The regulation of RAAS-associated gene expression was analyzed by transcription factor (TF) binding sites (TFBSs) prediction in the proximal promoters of these genes and by studying interactions between TFs themselves using the Ensembl Biomart web service and AnimalTFDB 4.0. The recognition of potential TFBSs in RAAS gene promoters was performed using MoLoTool.

According to the centrality criteria of the RAAS associative gene network, the following proteins were identified as exerting a significant influence on information interplay between network components: IL6, EDN1, TNFA, MK01, LEP, and JUN. Analysis of the ten identified TFs and their TFBSs among the genes in the RAAS network under study revealed clusters of three to 26 genes regulated by them.

Components with the highest values of centrality and vertex degrees were identified in the reconstructed associative gene network of the RAAS, and ten TFs supposed to regulate 26 RAAS genes were determined.

Alpinia calcarata (A. calcarata) Roscoe (Family: Zingiberaceae) is a rhizomatous perennial herb used in traditional medicine to treat inflammatory conditions. This study aimed to develop a topical emulgel dosage form by incorporating the essential oil of A. calcarata rhizome and to investigate it’s in vitro anti-inflammatory activity. A thin-layer chromatographic fingerprint of the essential oil of A. calcarata rhizome was developed. Then, an emulsion base containing plant oil was formulated and incorporated within a Carbopol gel base. The physical characteristics of this formulation were evaluated subsequently. The anti-inflammatory mechanism of the emulgel was determined by in vitro blood cell membrane stabilization assay and thrombolytic activity assay. The results were statistically analyzed by one-way analysis of variance. The thin-layer chromatographic fingerprint of the test oil demonstrated several bands with unique retention factor values. The formulated herbal emulgel was white, viscous, and homogeneous in appearance. The spreadability was 118 g·cm/M, and the pH of the emulgel was 6.30 at 25°C. The A. calcarata emulgel significantly (p < 0.05) inhibited heat-induced in vitro hemolysis, with the highest activity at a 50 µg/mL dose (87.68 ± 0.35%) compared to the placebo. Furthermore, this activity was found to be dependent on the essential oil concentration (r2 = 0.99) of the emulgel. Therefore, it was concluded that the essential oil of A. calcarata rhizome is an effective active ingredient to be used in a topical emulgel formulation, whereas the diverse phytochemicals present in the essential oil would be the underlying source of its anti-inflammatory activity.

To support clinicians in making informed decisions regarding the diagnosis and management of inherited hyperbilirubinemia, including Gilbert syndrome, Crigler-Najjar syndrome, Dubin-Johnson syndrome, and Rotor syndrome, the Inherited and Metabolic Liver Disease Collaboration Group of the Hepatology Branch of the Chinese Medical Association convened a panel of Chinese experts in this field. This multidisciplinary consortium developed the present expert consensus by integrating the latest advances in both clinical practice and basic research.

Lung cancer remains the leading cause of cancer-related mortality worldwide. Early detection of pulmonary nodules is crucial for timely diagnosis and effective treatment. Conventional computer-aided detection systems have shown limitations, including high false-positive rates and low sensitivity. Recent advances in deep learning, particularly convolutional neural networks (CNNs), have shown great potential in improving the accuracy and reliability of nodule detection and classification. This study aimed to develop and evaluate an automatic method for lung nodule detection and classification using a CNN-based architecture applied to computed tomography images from the publicly available LIDC-IDRI database.

This retrospective study was conducted on 82 patients (10,496 computed tomography slices) selected from the LIDC-IDRI database. The proposed method consists of five main steps: image preprocessing, lung parenchyma segmentation using Otsu’s thresholding and morphological operations, detection of nodule candidates, feature extraction, and classification using a CNN model. The CNN architecture includes two convolutional layers (20 and 30 filters, 3×3 kernel), ReLU activation, max-pooling layers, and a Softmax output layer. The network was trained with a mini-batch size of 32 for 50 epochs using the Stochastic Gradient Descent with Momentum optimizer (learning rate = 0.001, momentum = 0.9). Model performance was evaluated in terms of sensitivity, specificity, precision, and accuracy.

The proposed CNN model successfully detected pulmonary nodules and achieved accurate classification between benign and malignant nodules. On the LIDC-IDRI dataset, the model achieved a sensitivity of 98.7%, specificity of 97.5%, precision of 97.9%, and accuracy of 98.4%. Comparative analysis with recent studies, including hybrid CNN-long short-term memory and ResNet-based models, demonstrated that the proposed method provides competitive performance while maintaining lower computational complexity. The classification of nodule subtypes (solid, partially frosted, totally frosted) showed satisfactory discrimination results.

The proposed CNN-based system demonstrates the feasibility and robustness of deep learning for automatic lung nodule detection and classification. Despite strong results, the study acknowledges limitations such as single-database validation and a relatively small training size. Future work will focus on validating the model across other datasets (e.g., ELCAP, NELSON) and optimizing multi-class classification performance to enhance generalizability and clinical applicability.

The prognostic nutritional index (PNI), calculated from serum albumin and lymphocyte count, reflects a patient’s immune-nutritional status and has been proposed as a prognostic marker in hepatocellular carcinoma (HCC). However, its role in advanced HCC patients treated with atezolizumab plus bevacizumab (Ate/Bev) remains unclear. In this study, we aimed to evaluate the prognostic value of PNI in patients receiving first-line Ate/Bev therapy.

We retrospectively analyzed 362 patients with unresectable HCC who received Ate/Bev between November 2020 and June 2023 across two centers. Based on prior literature, a cutoff of 45 was used to classify patients into low-PNI (<45) and high-PNI (≥45) groups. Propensity score matching was performed to balance baseline characteristics.

After propensity score matching, 130 patients (65 per group) were included in the analysis. The high-PNI group showed a significantly lower incidence of grade ≥ 3 treatment-related adverse events (10.8% vs. 24.6%, p = 0.039), a higher objective response rate (38.4% vs. 20.0%, p = 0.037), and significantly longer overall survival (16.7 vs. 7.9 months, p = 0.009). Although progression-free survival was longer in the high-PNI group (4.8 vs. 3.0 months), the difference was not statistically significant (p = 0.597). Multivariate analysis confirmed that PNI was an independent predictor of overall survival (hazard ratio: 0.574, 95% confidence interval: 0.353–0.933, p = 0.025), after adjusting for vascular invasion, alpha-fetoprotein levels, concurrent therapy, and post-treatment interventions.

PNI is an independent prognostic factor for overall survival in advanced HCC patients treated with Ate/Bev in real-world clinical practice. Incorporating PNI into routine assessments may enhance risk stratification and guide therapeutic decision-making.