Technology and Health Care最新文献

BackgroundIn clinical diagnosis, determining the level of malignancy in tumors and differentiating between benign and malignant tumors are common classification challenges. Accurate and early diagnosis is essential for targeted treatment, and machine learning methods can assist in making these judgments.MethodsThis paper focuses on the classification of the lung tissue as benign or malignant and assessing the degree of aggressiveness in lung cancer. The study employed artificial neural network (ANN), logistic regression, and ridge penalized logistic regression, which are methods without built-in feature selection. Additionally, lasso penalized logistic regression, elastic-net penalized logistic regression, and sparse logistic regression with the hybrid L1/2 + 2 regularization (HLR), which are methods with built-in feature selection, were also utilized.ResultsIn the study on classifying benign and malignant lung tissue, ANN demonstrated the best predictive performance among the methods without built-in feature selection, achieving an average test accuracy of 91.82%. Among the methods with built-in feature selection, HLR outperformed the others with an average test accuracy of 96.67%. When determining the level of malignancy in lung tumors, ANN surpassed other methods without built-in feature selection, attaining an average test accuracy of 84.74%. In comparison, HLR exceeded the performance of other methods with built-in feature selection, reaching an average test accuracy of 93.33%.ConclusionsThe experimental results indicated that HLR with built-in feature selection and ANN without built-in feature selection exhibited strong competitiveness among the methods investigated in both classifying benign and malignant lung tissue and assessing the degree of aggressiveness in lung cancer.

BackgroundChemotherapy-induced nausea and vomiting (CINV) remains a prevalent and debilitating side effect of cancer treatment with limited therapeutic options. Dingxiang Shidi Decoction (DXSD), a classical Chinese herbal formula, has demonstrated clinical efficacy in CINV management. However, its active components and mechanism of action require systematic investigation.ObjectiveThis study aims to elucidate the pharmacologically active constituents of DXSD and their anti-CINV mechanisms.MethodsWe employed advanced analytical techniques including UPLC-MS/MS and GC-MS to analyze the chemical components of DXSD. Network pharmacology techniques were applied to explore its pharmacological mechanisms. And molecular docking simulation was conducted to further refine the drug-target interaction.ResultsA total of 292 chemical compounds were identified in DXSD, comprising 165 water-soluble components, 56 volatile components, and 84 network database entries. By integrating 564 drug targets with 888 CINV disease targets, we identified 143 potential therapeutic targets. Further protein-protein interaction (PPI) network analysis revealed 12 key active ingredients and 13 key therapeutic targets. Enrichment analysis suggested that DXSD may reduce inflammation, modulate neurotransmitter stimulation in the gastrointestinal tract, and regulate cellular proliferation and differentiation. Notably, key active ingredients, predominantly aromatic compounds such as sabinene, (-)-α-cubebene, α-copaene, β-caryophyllene, aromandendrene, γ-muurolene, (-)-α-muurolene, α-phellandrene, α-terpinene, γ-terpinene, (+)-δ-cadinene, and Gomisin B, demonstrated significant binding affinity with multiple targets, particularly AKT1, BCL2, EGFR, MTOR, and STAT3.ConclusionsThis study reveals the active components and therapeutic mechanisms of DXSD against CINV, supporting its clinical application and demonstrating the potential of aromatherapy as an effective treatment strategy.

BackgroundInsomnia is a prevalent global health issue that significantly impacts quality of life and poses a substantial economic burden. While conventional pharmacological and non-pharmacological treatments exist, they are often limited by side effects, dependency, or low clinical applicability. Guasha therapy, a traditional Chinese medicine nursing technique, shows promise for treating insomnia, but lacks standardized, evidence-based guidelines for its clinical application.ObjectiveTo analyze the regularity of meridian and acupoint selection in the treatment of insomnia with scraping therapy by using data mining technology.MethodsA computer-based search of PubMed, Web of Science, China National Knowledge Infrastructure (CNKI), Wanfang Data Knowledge Service Platform, Weipu Database(VIP), and Chinese Biomedical Literature Database (CBM) was conducted to retrieve the literature on scraping therapy for insomnia from the establishment of the database to March 15,2024.Excel was used to establish a database of acupoints for scraping therapy for insomnia, and the frequency of acupoints and meridians and the meridian tropism of acupoints were analyzed. SPSS 26.0 was used to cluster the high-frequency acupoints. The Apriori algorithm model provided by SPSS Modeler 18.0 was used to analyze the association rules of acupoint compatibility. Cytoscape 3.10.1 software was used to construct a complex network of meridian compatibility, and to analyze the core meridian of scraping in the treatment of insomnia.ResultsA total of 119 literatures were finally included, including 144 acupoints, with a total of 1121 frequencies. The most commonly used acupoints were Xinshu (BL15), Baihui (GV20), and Shenmen (HT7), and the commonly used compatibility acupoints were Xinshu-Pishu (BL20) and Xinshu-Pishu-Shenmen. The main meridians are Bladder meridian of foot-taiyang, Governor vessel, Gallbladder meridian of foot-shaoyang and Heart meridian of hand-shaoyin, which are the core meridian routes.ConclusionsData mining can effectively analyze the commonly used acupoints, acupoint compatibility rules and meridian rules of Gua Sha in the treatment of insomnia. The analysis results align with the traditional Chinese medicine principle of primarily following meridians, combining syndrome and root, enhancing the treatment effectiveness of insomnia, and providing a reference for clinical practice and research.

BackgroundUnderstanding patients' psychological, anatomical, and functional needs helps clinicians identify issues affecting smile aesthetics, improving orthodontic outcomes and quality of life.ObjectivesThis review examined laypeople's esthetic perceptions of malocclusion and orthodontic appliances via eye-tracking technology, considering gender and professional background influences.MethodsA systematic search was conducted in PubMed, Web of Science, Ovid MEDLINE, and ProQuest. Risk of bias (RoB) was assessed using the Joanna Briggs Institute tools. Data on eye fixation metrics and subjective ratings were descriptively synthesized.ResultsAmong 7009 studies, 27 met inclusion criteria (11 high, 10 moderate, and 6 low RoB) with variable methodologies. Ethnic and cultural factors were rarely considered. The eyes received the most attention in facial aesthetics. However, severe malocclusion and orthodontic appliances redirected gaze to the mouth, while attractive facial features mitigated this effect. Laypeople were more tolerant of malocclusion than dental professionals, who were more sensitive to malocclusion deviations. Facial esthetic perception revealed gender-related differences, where females focusing more on the eyes and males on the mouth and nose.ConclusionsMalocclusion and orthodontic appliances affect esthetic perception by altering gaze patterns. Integrating eye-tracking technology into orthodontic practice can improve patient satisfaction by aligning treatments with esthetic expectations.

BackgroundAtrial fibrillation (AF) is a common complication of acute myocardial infarction (AMI), significantly affecting patient prognosis by increasing the risk of heart failure, thromboembolic events, and mortality.ObjectiveTo explore the risk factors of AM combined with first-diagnosed AF.MethodsClinical data related to a total of 142 consecutive AMI patients admitted to Beijing Tongren Hospital from January 1, 2020 to May 1, 2020 were retrospectively analyzed, of whom 8 cases (5.6%) were divided into first-diagnosed AF group and the rest 134 cases were in non-first-diagnosed AF group according to the presence or absence of first-diagnosed AF. Clinical data, laboratory indexes, cardiac ultrasonography and coronary angiography results of the two groups were compared. Logistic regression method was used to analyze the possible risk factors offirst-diagnosed AF.ResultsComparing the various data between the two groups, the peak creatinine kinase myocardial band (CKMB), white blood cell count, left ventricular ejection fraction, and the proportion of patients with admission Killip grades 3-4 in the first-diagnosed AF group were significantly different from those in the non- first-diagnosed AF group (p < 0.05); the results of logistic multifactorial regression analysis showed that admission Killip grades 3-4 was an independent risk factor for first-diagnosed AF in patients with AMI (OR = 9.549, 95% CI: 1.697-53.737, p = 0.01).ConclusionPatients with AMI and Killip grade 3-4 face a heightened risk of developing atrial fibrillation. Close clinical observation, including ECG monitoring, is essential for early atrial fibrillation detection. Given the limitations of the current study, such as small sample size in single center, small number of AF events, the retrospective design, the validity and reliability of the research findings are constrained.

Background: Blood transfusion is a critical medical intervention, and accurate cross-matching is essential to prevent adverse reactions. However, difficult cross-matching (DCM), often caused by irregular antibodies, poses significant challenges, particularly in patients with prior transfusions, pregnancies, or certain medical conditions.

Objective: To analyze the incidence and causes of DCM from 2017 to 2021 and propose strategies to improve transfusion safety and compatibility.

Methods: A total of 363 patients who underwent difficult cross-matching due to incompatible blood matching between January 2017 and December 2021 were selected as the study population. Pre-transfusion testing was conducted for all patients to observe patterns in difficult cross-matching, assess the causes of blood mismatch, and analyze irregular antibody detection results. Based on these findings, specific treatment measures were proposed to address the causes of incompatibility.

Results: Most DCM cases occurred in females (81.27%), especially under 45 years of age, with obstetrics and gynecology being the most affected departments. Positive irregular antibodies were identified in 86.50% of cases, mainly from the Rh blood group system (42.99%), followed by autoantibodies (19.43%) and other blood group antibodies. Effective strategies included selecting antigen-negative blood, warming transfusions for cold autoantibodies, and using saline for pseudoagglutination.

Conclusion: Irregular antibodies are the main cause of DCM. Accurate identification and tailored transfusion strategies are essential to ensure safety. Greater awareness and improved screening can reduce risks and enhance clinical outcomes.

BackgroundInflammatory Bowel Disease (IBD) significantly affects patients' long-term health and survival quality. Accurate and technology-driven assessment tools are essential for monitoring disease impact and optimizing individualized treatment strategies.ObjectiveTo develop a standardized and technology-enhanced evaluation system for IBD based on patient-reported outcomes (IBDPRO), enabling a comprehensive and dynamic assessment of patients' survival quality.MethodsA multidimensional evaluation system was constructed using the three-dimensional quality structure model. The framework incorporated expert consensus through the Delphi method, semi-structured interviews, and analytic hierarchy process (AHP) for weight assignment. A digital platform based on the REDCap system was used for data collection and patient follow-up. Statistical correlations were analyzed between survival quality scores and disease activity indices (PMS and HBI), as well as subjective self-assessments.ResultsHigh expert authority coefficients (0.926, 0.931) and significant agreement levels (Kendall's W = 0.226, 0.239, P < 0.01) were achieved. survival quality was strongly negatively correlated with disease activity (Spearman's r = -0.76, P < 0.001) and positively correlated with patients' self-perceived condition changes (r = 0.79, P < 0.001). The system demonstrated reliability, validity, and responsiveness across multiple dimensions.ConclusionsThe IBDPRO system provides a scientifically rigorous and technologically integrated method for evaluating the survival quality in IBD patients. This approach facilitates data-driven clinical decision-making and supports a transition toward personalized, patient-centered healthcare.

BackgroundThe increasing demand for dental implants necessitates the exploration of advanced materials and manufacturing techniques. Three-dimensional (3D) printing has emerged as a viable method for producing custom dental implants, allowing for intricate designs and improved patient-specific fits. This study focuses on the design and structural deformation assessment of 3D-printed dental implants using Finite Element Analysis (FEA). By simulating the mechanical behavior of these implants under realistic loading conditions, we aim to evaluate their performance and predict potential failure points, ultimately enhancing their reliability and longevity in clinical applications.ObjectiveThe primary objective of this study is to conduct a comprehensive design and structural deformation assessment of three-dimensional (3D) printed dental implants using Finite Element Analysis (FEA). Specifically, the study aims to: Evaluate stress distribution and deformation patterns in three 3D-printed dental implant designs under simulated physiological loading.Compare the stiffness, strength, and elastic behavior of PEEK and CFR-PEEK under occlusal forces.Identify failure points in implants and bone-implant interfaces by analyzing high stress concentrations.Predict the biomechanical behavior of a novel dental implant by determining its elastic modulus through finite element analysis (FEA).MethodsThree models 3D were designed to understand stress distribution with different structures using PEEK as biomaterial, with 4 test conditions modeled and compared. An occlusal load was applied (230 N at 90˚ and 30˚) on the implants. Isotropic, linear elastic, and homogeneous were considerate as properties of the components.ResultsUnder axial loads, all models stayed within physiological stress limits, while under 30° oblique loading, Model 3 showed the lowest stress, strain, and pressure.ConclusionsFEA results indicate that 3D-printed dental implants, particularly the optimized Model 3, maintain safe stress levels under axial and oblique loads, supporting their potential for immediate loading. However, due to numerical limitations, experimental validation remains necessary to advance implant designs that optimize bone regeneration and material efficiency.

ObjectiveThis study aims to compare the efficacy and safety of Proximal Femoral Nail Anti-Rotation (PFNA) versus Dynamic Hip Screw (DHS) in the treatment of intertrochanteric fractures (IF) in elderly patients. We specifically evaluate perioperative indicators, postoperative hip function, and the rate of internal fixation failure.Methods300 elderly IF patients treated in our hospital from July 2018 to May 2022 were divided into the PFNA group (n = 150), control group (n = 150), PFNA group treated with PFNA, and control group treated with mobility hip screw (DHS). Perioperative bleeding, operation time, postoperative time and hospital time were observed in the two groups: fracture reduction, hip function at 1 and 6 months after surgery, and failure of internal fixation. Observe the postoperative internal fixation failure in the PFNA group.ResultsThe intraoperative blood loss, operation time, first postoperative time and hospital time in the PFNA group were less than in the control group (P < 0.05). One month after surgery, the Harris score of the PFNA group was less than that of the control group (P < 0.05); the Harris score was not different at 6 months (P > 0.05). The excellent rate of fracture reduction in the PFNA group was greater than that in the control group (P < 0.05). The failure rate of internal fixation in the PFNA group was less than that in the control group (P < 0.05). By univariate analysis, Sing Index classification, Evans classification, 25 min and underlying disease were risk factors for postoperative internal fixation failure in PFNA patients (P < 0.05). After multivariate Logistic regression analysis, Sing Index grade, Evans classification, and tip distance 25min were independent risk factors for postoperative internal fixation failure in PFNA patients (P < 0.05).ConclusionThe treatment of elderly IF patients with PFNA has the advantages of small trauma, good fracture reduction, firm internal fixation, low failure rate of internal fixation, and quick postoperative recovery. Sing index classification, Evans classification, and 25min tip distance mainly caused internal fixation failure in patients with PFNA.

ObjectiveTo analyse the characteristic features of infants with pneumonia who test positive for serum milk-specific immunoglobulin E (sIgE) and to provide a reference for the diagnosis, management and prevention of the condition.MethodsWe retrospectively analysed data from 284 infants admitted to our hospital with pneumonia between January 2017 and December 2020 who underwent serum allergen testing. Based on the results, patients were categorised into three groups: pure milk sIgE-positive; mixed milk sIgE-positive; and allergen sIgE-negative. We then compared the general conditions, clinical characteristics, laboratory tests, imaging results and pathogenic data across these groups.ResultsAmong the patient population, 16.20% (46/284) tested positive for pure milk sIgE, 32.75% (93/284) tested positive for mixed milk sIgE and 51.06% (145/284) were negative for any allergen sIgE. Statistically significant differences were observed among the three groups in terms of general conditions, breastfeeding status, pre-existing respiratory infections and history of respiratory infections (>3 times) (p < 0.05 for each). The median length of hospital stay was longer in the pure milk sIgE-positive group and the mixed milk sIgE-positive group (8 [range 7-10] days) compared with the allergen sIgE-negative group (8 [range 6-9] days) (p < 0.05). The eosinophil counts of the mixed milk sIgE-positive group were significantly higher than in the other two groups (p < 0.05). Haemophilus influenzae of the pure milk sIgE-positive group was significantly higher than in the other two groups (p < 0.05).ConclusionThe presence of mixed milk sIgE allergens does not exacerbate clinical symptoms. However, infants who test positive solely for milk sIgE and have pneumonia require longer hospitalisation. This suggests that extra attention is necessary for infants with milk allergies when they develop pneumonia.