BioMedicine-Taiwan最新文献

Background: Breast cancer is the highest cancer incidence in the world. Chemotherapy is currently one of the main breast cancer treatments besides surgery. It is capable of evolving to become resistant to chemotherapy agents. Chemotherapy also has significant side effects. Rosmarinic acid could become an anti-cancer agent candidate for the treatment of breast cancer, but its molecular mechanism is still unclear.

Aim: This study aimed to clarify the molecular mechanism of rosmarinic acid anti-breast cancer properties via an in-silico study.

Methods: Web-based screening tools such as SwissTargetPrediction, Similarity Ensemble Approach (SEA), and TargetNet were used as initial screening. From web-based screening, potential proteins that interact with rosmarinic acid could be determined. Intersected proteins from 3 web-based screenings were assessed via literature review. We found 11 intersected proteins, and 6 of 11 proteins are involved in breast cancer development and progression. Those 6 proteins are MMP-1, MMP-2, MMP-9, MMP-12, aldose reductase, and M-phase Inducer Phosphatase 2 (CDC25B). Then molecular docking using Autodock 4.6.2 was used in ligand and protein interaction simulation. Those 6 proteins were selected as macromolecules in the docking study.

Results: Based on the docking result, we found that rosmarinic acid can bind MMP-1, MMP2, MMP-9, and MMP-12 active sites. The binding profile of rosmarinic acid with aldose reductase has similarities with other confirmed inhibitors. Docking with CDC25B showed that rosmarinic acid also binds in the same place as cyclin-dependent kinases (CDKs).

Conclusion: The ability of rosmarinic acid to inhibit MMP-1, MMP-2, MMP-9, aldose reductase, and CDC25B activity may underlie how rosmarinic acid is able to inhibit the development of breast cancer.

Phantosmia belongs to the group of olfactory dysfunctions. It is more commonly described in psychiatric conditions and some cases of viral infections, but it has been also rarely described in cancer patients who develop primary or metastatic central nervous system tumors; the early identification of this symptom in this population is crucial, as it could lead to timely diagnosis and treatment through a multidisciplinary approach. With the current report we present the case of a 60-year-old lady with metastatic breast cancer and without known preexisting brain metastases, who developed acute phantosmia without other neurological deficits; computed tomography of the brain revealed multiple brain metastases, which were attributed to the malignancy, and for which she was effectively treated with whole brain irradiation and antipsychotic as well as anticonvulsant medications. Furthermore, we underline the value of cooperation between the various specialties that could aid in diagnosis and management of this symptomatology. Phantosmia is an extremely rare symptom in cancer patients, and its appearance should alarm physicians to rapidly investigate a possible progression of disease in the central nervous system. Multidisciplinary approach is needed for the optimal management of these patients.

Background: Prediabetes precedes Type-2 diabetes development, is characterized by impaired glucose tolerance (IGT) as judged by the 75-g oral glucose tolerance test (75-OGTT) and is associated with higher cardiac risk (CR), dyslipidemia, hepatosteatosis (HS) and cancer.

Aims: The study aimed to determine the distinguishing ability of estimated levels of glycated albumin (GA) and hemoglobin A1c (HbA1c), and serum C-reactive protein (CRP) and serum total albumin (TA) levels for prediabetic out of apparently healthy subjects.

Methods: IGT was diagnosed if fasting blood glucose (FBG) was 100-125 mg/dl and if < 100 mg/dl subject is normal glucose tolerant (NGT). CR, HS and insulin resistance (IR) were suggested if the calculated atherogenic index of plasma (AIP) was >0.1, HS index (HSI) was >36 with high computerized hepatorenal index (HRI) and the homeostasis model assessment of IR (HOMA-IR) score was >2, respectively.

Results: 253 subjects (50.8%) had IGT and were older, more obese and mainly females. IGT had higher GA%, HOMA-IR score, serum CRP and lower TA with high CRP/TA ratio (CAR) than NGT. The frequency of subjects had AIP>0.1, HSI>36 and high HRI was significantly higher among IGT subjects. Statistical analyses defined high GA% and CAR as predictors for IGT, HOMA-IR>36 and AIP score >0.1, while high CAR is the only predictor for HSI score >36 and high HIR score.

Conclusion: Prediabetes is not uncommon and high GA% and CAR might differentiate them among the apparently healthy population, and could predict those at increased risk for IR, HS and at CR.

Heterogeneous nature of breast cancer has significantly affected the overall survival, disease free survival and progression free survival amongst the diseased individuals. Metastasis of cancerous cells to distant sites including bone, lungs, liver, lymph node and others have further exhilarated the adverse effects. However, ER, PR and HER-2 are responsible for normal physiological development of women but in altered conditions they may act as initiator or progressor and so far 5 subtypes of disease have been identified. Alteration of pro-survival, pro-proliferative and anti-apoptotic pathways including JAK/STAT, MAPK, PI3K/AkT/mTOR, NF-κB, BCL2 and several others have induced oncogenic events including epithelial-mesenchymal transition, intra-vasation, extra-vasation and many more. Although several US-FDA approved drugs are available in market to target above mentioned signalling pathways but issues of resistance, side effects have restricted their efficacy. The present review article aims to highlight diverse molecular subtypes and the signalling pathways involved in complicating the disease along with the US-FDA approved drugs to target them. Potential herbal medicine to target the disease have also been emphasized that can be used either as mono-therapeutic approach or in combination with conventional therapeutic regimens to target breast cancer.

Objectives: This study tried to examine the ability of the estimated plasma gene-expression levels (PGEL) of microRNA (miR)-146a and miR-21b to distinguish patients with early rheumatoid arthritis (RA) out of arthritis patients who did not fulfill the diagnostic spectrum of either RA or osteoarthritis (OA); the diagnostic Gray-Zone (GZ).

Patients & methods: Enrolled patients underwent full diagnostic workup and were categorized as highseropositive and fulfilled the diagnostic spectrum for RA (RA-group), seronegative and fulfilling the diagnostic spectrum of OA (OA-group) and low-seropositive or seronegative patients who did not fulfill diagnostic criteria of RA or OA (GZ-group). Blood samples were obtained for quantification of PGEL of miR-146a and miR-21-b using the quantitative Reverse-transcriptase polymerase chain reaction and results were related to patients' seropositivity and clinical data.

Results: The mean fold change of PGEL of miR-146a and miR-21b was significantly higher in patients than in control samples, in samples of high-seropositive patients than in other samples, and in samples of low-seropositive than in seronegative patients. Both markers showed a positive significant correlation with Disease Activity Score-28 for RA-activity and seropositivity. Using the ROC curve analysis, the PGEL of both microRNAs could identify high-seropositive among the studied arthritis patients, but Regression Analysis defined high PGEL of miR-146a as the most significant predictor to identify RA patients and predict their disease activity. Statistical analyses defined miR-146a as the significant parameter that could differentiate between early RA and OA patients among GZ patients.

Conclusion: Early arthritis that does not fulfill the diagnostic spectrum of a certain type of arthritis is not uncommon and challenges therapeutic decision-making. The estimated PGEL of MicroRNA-146a might enlighten this gray diagnostic zone and allow differentiation between patients with early RA and early OA, and help to stratify RA patients according to disease activity and severity.

Introduction: Distal femur fractures result in high morbidity and mortality - comparable to that of hip fractures. The commonly used surgical fixation techniques today, locked plating and intramedullary nailing, have shown high postoperative complication rates. Thus, many surgeons temporarily keep patients non-weight bearing in the early postoperative stage. Increased time to ambulation after surgery is known to increase systemic complications in patients. We aim to investigate if an augmented fixation technique involving the use of a fibular strut allograft with dual locking plates helps to allow early mobilization postoperatively without adverse outcomes.

Methods: Five geriatric patients (four female, one male) with distal femur fractures (native or periprosthetic) were treated in our institution with the aforementioned technique, and were allowed early postoperative weight-bearing. These patients were followed up for postoperative outcomes. The primary outcomes studied were non union, implant failure and wound complications. Secondary outcomes studied include time to union, and Sander's functional score.

Results: There were no cases of non-union, implant failure or wound related infection. All patients achieved radiological union (mean = 12.6 weeks). Using Sander's functional scoring, two patients achieved excellent, two achieved good and one had fair outcomes. All patients were followed up for at least 6 months after operation.

Conclusion: Our method of augmented fixation with fibular strut allografts potentially allows for early weight bearing without adverse outcomes. Further studies with larger sample sizes are required to validate our findings.

In the past two decades, healthcare organizations have transitioned from the early stages of digitization and digitalization to a more comprehensive process of digital transformation, a shift significantly accelerated by the advent of artificial intelligence (AI). Consequently, the development of high-quality clinical data warehouses, derived from electronic health records (EHRs) and enriched with multidomain data, such as genomics, proteomics, and Internet of Things (IoT) information, has become essential for the creation of the modern patient digital twin (PDT). This approach is critical for leveraging AI in the evolving landscape of clinical practice. Leading medical centers and healthcare institutions have adopted this model, as summarized in this review. Since 2020, China Medical University Hospital (CMUH) has been constructing its data ecosystem by integrating EHRs with extensive genomic databases. This initiative has led to the development of a data service platform, the ignite Hyper-intelligence (iHi®) platform. The iHi platform serves as a case study exemplifying the workflow of the smart data chip, which facilitates the deep cleaning and reliable de-identification of clinical data while incorporating analytical platforms related to genomics and the microbiome to enhance insight extraction processes. The ability to predict complex interactions and disease trajectories among PDTs, digital counterparts of healthcare professionals, and virtual socioeconomic environments will be pivotal in advancing personalized healthcare and optimizing patient outcomes. Future challenges will involve the unification of cross-institutional data platforms and ensuring the interoperability of AI inferences-key factors that will define the next era of AI-driven healthcare.

Introduction: Our previous research demonstrated that a large language model (LLM) based on the transformer architecture, specifically the MegaMolBART encoder with an XGBoost classifier, effectively predicts the blood-brain barrier (BBB) permeability of compounds. However, the permeability coefficients of compounds that can traverse this barrier remain unclear. Additionally, the absorption, distribution, metabolism, and excretion (ADME) characteristics of substances obtained from the Natural Product Research Laboratory (NPRL) at China Medical University Hospital (CMUH) have not yet been determined.

Objectives: The study aims to investigate the pharmacokinetic ADME properties and BBB permeability coefficients of NPRL compounds.

Materials and methods: A combined model using a transformer-based MegaMolBART encoder and XGBoost classifier was employed to predict BBB permeability. Machine learning (ML) tools from Discovery Studio were used to assess the ADME characteristics of the NPRL compounds. The CCK-8 assay was conducted to evaluate the cytotoxic effects of NPRL compounds on bEnd.3 brain endothelial cells after exposure to 10 μg/mL of the compounds. We assessed the permeability coefficient by subjecting bEnd.3 cell monolayers to the test compounds and measuring the permeability of FITC-dextran.

Results: There were 4956 compounds that could cross the blood-brain barrier (BBB+) and 2851 that could not (BBB-) in the B3DB dataset that was utilized for training. A total of 2461 BBB+ and 2184 BBB- compounds were used in the NPRL-CMUH dataset for testing. The permeability coefficient of temozolomide (TMZ) and 21 other BBB + compounds exceeded 10 × 10^-7 cm/s. Computational analysis revealed that NPRL compounds exhibited a variety of ADME characteristics.

Conclusion: Computer-based predictions for the NPRL of CMUH compounds regarding their capacity to traverse the BBB are verified by the findings. Artificial intelligence (AI) prediction models have effectively identified the potential ADME characteristics of various compounds.

Introduction: Spine surgery is a common source of narcotic prescriptions and carries potential for long-term opioid dependence. As prescription opioids play a role in nearly 25 % of all opioid overdose deaths in the United States, mitigating risk for prolonged postoperative opioid utilization is crucial for spine surgeons.

Purpose: The aim of this study was to employ six ML algorithms to identify clinical variables predictive of increased opioid utilization across spinal surgeries, including anterior cervical discectomy and fusion (ACDF), posterior thoracolumbar fusion (PTLF), and lumbar laminectomy.

Methods: A query of the author's institutional database identified adult patients undergoing ACDF, PTLF, or lumbar laminectomy between 2013 and 2022. Six supervised ML algorithms, including Random Forest, Extreme Gradient Boosting, and LightGBM, were tasked with predicting additional opioid prescriptions at a patient's first postoperative visit based on set variables. Predictive variables were evaluated for missing data and optimized. Model performance was assessed with common analytical metrics, and variable importance was quantified using permutation feature importance. Statistical analysis utilized Pearson's Chi-square tests for categorical and independent sample t-tests for numerical differences.

Results: The author's query identified 3202 patients matching selection criteria, with 841, 1,409, and 952 receiving ACDF, PTLF, and lumbar laminectomy, respectively. The ML algorithms produced an aggregate AUC of 0.743, performing most effectively for lumbar laminectomy. Random Forest and LightGBM classifiers were selected for generation of permutation feature importance (PFI) values. Hospital length of stay was the only highly featured variable carrying statistical significance across all procedures.

Conclusion: Notable risk factors for increased postoperative opioid use were identified, including shorter hospital stays, younger age, and prolonged operative time. These findings can help identify patients at increased risk and guide strategies to mitigate opioid dependence.

Background: Bone age (BA) and sexual maturity rating (SMR) are crucial measures in assessing adolescent growth and development. However, studies specifically focusing on the association between BA and SMR in the Taiwanese adolescent population are limited. This study aims to utilize AI-assessed BA results to establish a relationship between BA and SMR in the Taiwanese adolescent population, particularly regarding the initiation of puberty.

Materials and methods: The electronic medical records of bone age assessments conducted between January 1, 2019, and December 31, 2019, were reviewed retrospectively. For individuals with multiple records, only the latest entry within this period was retained. Records lacking a valid SMR or presenting significant medical conditions were excluded from the analysis. Males aged 7-17 years and females aged 6-16 years were included in the study.

Results: The onset of puberty was observed to occur at a median bone age of 11.50 years (95% CI: 11.42-11.83) for males and 9.33 years (95% CI: 9.25-9.50) for females.

Conclusion: The consistency between BA and SMR could serve as an alternative approach for assessing pubertal status in peripubertal children, providing a less intrusive evaluation regardless of chronological age (CA).