Amaranth is conventionally consumed as a significant source of nutrients and bioactive compounds and is a potential alternate crop. The present study aimed to validate the folklore and ethnomedicinal claims regarding the utilization of foliar tissues of the pseudocereal Amaranthus hypochondriacus L. for their pharmacological propensities, primarily focusing on bioactive polyphenolic compounds and associated anti-degenerative properties, in view of the scarce evidence available on the same.

Reverse-phase high-performance liquid chromatography coupled with a photodiode array assay of nineteen significant bioactive polyphenolic compounds, along with their in vitro antioxidant-based pharmacological properties (superoxide and hydroxyl radical scavenging properties, metal-chelating and reducing properties, radical scavenging properties, anti-lipid peroxidation and protein coagulation properties, and α-glucosidase and α-amylase inhibitory activities), were assessed and compared for foliar extracts of ten promising experimental accessions of Amaranthus hypochondriacus, grown in two different seasons (summer and winter).

The results exhibited germplasm-specific variations in the pharmacological potential of foliar tissues of the experimental amaranths, which can be substantiated by data showing a close correlation between the abundance of bioactive polyphenolic compounds (naringin, myricetin, naringenin, apigenin, rutin, catechin, quercetin) and in vitro antioxidant (2,2-diphenyl-1-picrylhydrazyl, 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) assay, hydroxyl radical scavenging, reducing, and metal-chelating) properties, as well as anti-diabetic (inhibition of α-glucosidase and α-amylase activities) and anti-inflammatory (anti-lipid peroxidation) attributes. Accessions IC107144 and IC47434 stood out as the most promising medicinal crops based on overall in vitro anti-degenerative properties and the bioavailability of polyphenolic compounds.

Overall, the results validated the traditional ethnomedicinal claim regarding the utilization of foliar tissues of the underutilized pseudocereal Amaranthus hypochondriacus L., and identified lead germplasms (IC107144 and IC47434) as low-cost natural sources of bioactive compounds, potentially promoting their pharmacological utilization.

Stroke patients have a high incidence of venous thromboembolism (VTE). Improving the prevention and control rates of VTE in stroke patients can enhance their quality of life. The aim of this study was to analyze the effect of 4R crisis management combined with the health belief model in the prevention and control of VTE in stroke patients.

A randomized controlled trial was conducted on 86 stroke patients in the neurosurgery department of a tertiary hospital in Wuhan. The control group was treated with the routine VTE prevention and control strategy, while the experimental group was treated with 4R crisis management combined with the health belief model. The primary outcome measures were the incidence rates of deep vein thrombosis and pulmonary thromboembolism, while the secondary outcome measures were the Short Form Health Belief Model Scale score, medical quality evaluation, and stroke patients’ health behavior scale score. The statistical analysis methods included t-tests and non-parametric tests.

After the intervention, the incidence rate of deep vein thrombosis in the control group was 14.6% (6/41), while in the experimental group it was 2.4% (1/41). The difference was statistically significant (χ2 = 3.905, P = 0.048). The incidence rates of pulmonary thromboembolism in both groups were 0%. The scores of all dimensions of the Short Form Health Belief Model Scale in the experimental group were higher than those in the control group, and the difference was statistically significant (P < 0.05, P < 0.01). The medical quality for each item showed that the experimental group performed better than the control group, with the difference being statistically significant (P < 0.05, P < 0.01). The scores on the stroke patients’ health behavior scale in the experimental group were higher than in the control group, except for responsibility, tobacco, and alcohol (P < 0.01).

The application of 4R crisis management combined with the health belief model can effectively improve the health beliefs and health behaviors of stroke patients to prevent VTE, thereby reducing the incidence of VTE.

Artificial intelligence (AI) is transforming the diagnosis, treatment, monitoring, and research of soft tissue disorders, which include muscles, tendons, ligaments, fascia, nerves, and blood vessels. Traditional diagnostic methods often rely on imaging, histopathology, and clinical evaluation, which can be time-consuming and prone to human error. This review aims to explore the impact of AI on enhancing soft tissue care. The review examines the application of deep learning algorithms in medical imaging, pathology, predictive analytics, and treatment planning. It also evaluates AI’s role in monitoring and rehabilitation, as well as its contributions to research in soft tissue disorders. AI significantly improves the accuracy of medical imaging analysis, facilitating the detection of abnormalities such as tumors and tears. AI-powered pathology tools automate slide analysis, enhancing diagnostic consistency and efficiency. Predictive analytics enable early risk assessment and personalized patient management. In surgical contexts, AI supports preoperative simulations and robotic-assisted procedures, leading to improved outcomes. Additionally, AI enhances patient monitoring through wearable devices and telemedicine. The integration of AI into soft tissue diagnostics and therapeutics presents transformative potential for personalized and efficient healthcare. However, challenges related to data security, algorithm bias, interpretability, and ethical considerations must be addressed. Overall, AI holds promise for improving patient outcomes and advancing medical science in the field of soft tissue disorders.

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.

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.

For individuals with decompensated advanced chronic liver disease (dACLD), the onset of refractory ascites (RA) represents a dramatic event. In this setting, a relevant proportion of RA patients develop kidney dysfunction, as well as hepatorenal syndrome-acute kidney injury, with limited therapeutic and survival chances. An 81-year-old woman with dACLD-RA was admitted with severe ascites and stage IV chronic kidney dysfunction. On the second day, hepatorenal syndrome-acute kidney injury occurred, requiring standard medical therapy. Intravenous human albumin (HA) and terlipressin administration were compromised by poor venous access and severe respiratory dysfunction. After excluding transjugular intrahepatic portosystemic shunt and transplantation due to age and comorbidities, peritoneal dialysis (PD) was initiated, leading to renal recovery and ascites resolution. Two weeks later, she was readmitted due to the unfeasibility of accessing peripheral veins for the intravenous administration of HA, which was essential to support circulatory function, preserve oncotic balance, and properly manage both RA and chronic kidney dysfunction. A novel PD+HA protocol was therefore started, with intraperitoneal infusion of HA-enriched dialysate to allow a positive albumin gradient from dialysate to blood. Over 12 months, serum albumin levels increased, and clinical stability and improved nutritional status were observed, with no additional hospitalizations or complications. This is the first case describing the application of HA-enriched PD in managing a dACLD patient with RA and kidney dysfunction. HA-enriched PD may represent a promising strategy in complex dACLD care by guaranteeing frequent and small-volume paracentesis and preservation of oncotic pressure without dialytic albumin loss.