Synergistic Effects of MOFs and Noble Metals in Photocatalytic Reactions: Mechanisms and Applications.

Photocatalytic technology can efficiently convert solar energy to chemical energy and this process is considered as one of the green and sustainable technology for practical implementation. In recent years, metal-organic frameworks (MOFs) have attracted widespread attention due to their unique advantages and have been widely applied in the field of photocatalysis. Among them, noble metals have contributed significant advances to the field as effective catalysts in photocatalytic reactions. Importantly, noble metals can also form a synergistic catalytic effect with MOFs to further improve the efficiency of photocatalytic reactions. However, how to precisely control the synergistic effect between MOFs and noble metals to improve the photocatalytic performance of materials still needs to be further studied. In this review, the synergistic effects of MOFs and noble metal catalysts in photocatalytic reactions are firstly summarized in terms of noble metal nanoparticles, noble metal monoatoms, noble metal compounds, and noble metal complexes, and focus on the mechanisms and advantages of these synergistic effects, so as to provide useful guidance for the further research and application of MOFs and contribute to the development of the field of photocatalysis.

Using Artificial Intelligence to Interpret Clinical Flow Cytometry Datasets for Automated Disease Diagnosis and/or Monitoring.

Flow cytometry (FC) is routinely used for hematological disease diagnosis and monitoring. Advancement in this technology allows us to measure an increasing number of markers simultaneously, generating complex high-dimensional datasets. However, current analytic software and methods rely on experienced analysts to perform labor-intensive manual inspection and interpretation on a series of 2-dimensional plots via a complex, sequential gating process. With an aggravating shortage of professionals and growing demands, it is very challenging to provide the FC analysis results in a fast, accurate, and reproducible way. Artificial intelligence has been widely used in many sectors to develop automated detection or classification tools. Here we describe a type of machine learning method for developing automated disease classification and residual disease monitoring on clinical flow datasets.

Imaging Characteristics and Interpretation Strategy for Renal Hyperparathyroidism: A Retrospective 4-Dimensional Computed Tomography Study.

OBJECTIVE: Parathyroidectomy treats uncontrolled renal hyperparathyroidism (RHPT), requiring identification of all glands. Three types of enhancement are proposed. Type A lesions have higher arterial phase attenuation than the thyroid, type B lesions lack higher arterial phase attenuation but have lower venous phase attenuation, and type C lesions have neither higher arterial phase attenuation nor lower venous phase attenuation than the thyroid. We aimed to outline the image features of problematic parathyroid glands in RHPT and propose a 4-dimensional computed tomography (4DCT) interpretation algorithm. METHODS: This retrospective study involved data collection from patients with RHPT who underwent preoperative 4DCT for parathyroidectomy between January and November 2022. Pathologically confirmed parathyroid lesions were retrospectively identified on 4DCT according to the location and size described in the surgical notes. The attenuation of parathyroid lesions and the thyroid glands was assessed in 3 phases, and demographic data of the patients were collected. RESULTS: Ninety-seven pathology-proven parathyroid glands from 27 patients were obtained, with 86 retrospectively detected on 4DCT. In the arterial phase, the attenuation of parathyroid lesions in RHPT did not exceed that of the thyroid gland (P < .001). In the venous phase, parathyroid lesions demonstrated lower attenuation than the thyroid gland (P < .001). A total of 81 parathyroid lesions (94.2%) exhibited type B patterns. CONCLUSION: Unlike primary hyperparathyroidism, lesions in RHPT exhibited more type B enhancement, making them less readily identifiable in the arterial phase. Therefore, we propose a distinct imaging interpretation strategy to locate these problematic glands more efficiently.

Ochratoxin A detoxification potentials of basil, chan, and chia seeds.

The most toxic of the ochratoxins is ochratoxin A (OTA), which is primarily produced by species of Aspergillus and Penicillium that can be found in maize, wheat, coffee, red wine, and various grains. OTA induces immunotoxicity, nephrotoxicity, hepatotoxicity, teratogenicity, and carcinogenicity in both animals and humans. Thus, there is a need to identify mycotoxin detoxification agents that can effectively decontaminate OTA. Seeds of basil (Ocimum basilicum L.), chan (Hyptis suaveolens L.), and chia (Salvia hispanica L.) are functional foods capable of eliminating harmful substances. Despite this potential, the impact of these seeds on OTA detoxification remains unclear. This study reveals that milled basil, chan, and chia seeds adsorb significant levels of OTA, with chia demonstrating the highest adsorption capacity, followed by chan and basil seeds showing the least efficiency. Furthermore, milled basil, chan, and chia seeds effectively reduced OTA residues in artificial gastric and intestinal fluids, where they achieved up to 93% OTA adsorption in the former. In addition, these milled seeds were able to remove OTAs from canned, drip, and instant coffee. This study is the first to report the OTA elimination potential of basil, chan, and chia seeds.

A Coarse-to-Fine Pathology Patch Selection for Improving Gene Mutation Prediction in Acute Myeloid Leukemia.

Identifying gene mutation is essential to prognosis and therapeutic decisions for acute myeloid leukemia (AML) but the current gene analysis is inefficient and non-scalable. Pathological images are readily accessible and can be effectively modeled using deep learning. This work aims at predicting gene mutation directly by modeling bone marrow smear images. Traditionally, bone marrow smear slides are cropped into patches with manual segmentation for patch-level modeling. Slide-level modeling, such as multi-instance learning, could aggregate patches for holistic modeling, though suffer from excessive redundancy. In this study, we propose a discriminative multi-instance approach to select useful patches in a coarse-to-fine process. Specifically, we preprocess a slide into patches by using a trained pre-selector network. Then, we rule out low quality patches in the coarse selection with known prior knowledge, and refine the model using gene-discriminative patches in the fine selection. We evaluate the framework for CEBPA, FLT3, and NPM1 gene mutation prediction and obtain 71.67%, 56.26%, and 56.34% F1-score. Further analysis show the effect of different selection criteria on prediction gene mutations using pathological images.

Associations between perceived overqualification, transformational leadership and burnout in nurses from intensive care units: A multicentre survey.

AIMS: To explore whether perceived overqualification increases the risk of burnout and whether transformational leadership negatively moderates this relationship. BACKGROUND: Perceived overqualification might contribute to burnout and lead to poor experience of transformational leadership, and transformational leadership might be associated with burnout. However, these relationships have not yet been confirmed. METHODS: A multicentre cross-sectional study. A total of 321 nurses from intensive care units were recruited from six tertiary hospitals. Scale of Perceived OverQualification, Transformational Leadership Questionnaire and emotional exhaustion subscale of the Maslach Burnout Inventory-General Survey were employed to collect the data. Hierarchical multiple regression and bootstrap resampling were applied to analyse the data. RESULTS: Burnout was positively associated with perceived overqualification and negatively associated with transformational leadership (each p < 0.05). Transformational leadership significantly mediated the relationship between perceived overqualification and burnout (b = -0.6389, 95% confidence interval: -0.8706, -0.4072). CONCLUSION: Our findings indicated that perceived overqualification and transformational leadership directly or indirectly affect burnout among nurses from intensive care units. IMPLICATIONS FOR NURSING MANAGERS: Personal and organizational-oriented interventions utilizing nurses' overall qualifications and implementing transformational leadership should be employed by nurse managers to alleviate burnout and promote the work performance of nurses from intensive care units.

Worsening Quality of Life in Young Adult, Highly Educated, and Married Female Patients with Vitiligo: A Hospital-Based Case Control Study in Taiwan.

Vitiligo is an acquired chronic depigmentation disorder that can have a negative impact on the quality of life (QoL). This is especially true for patients with non-white skin. Only few studies have investigated the QoL of Asian patients with vitiligo. We aimed to investigate the QoL in Taiwanese vitiligo patients and identify the factors that influence their QoL. The cross-sectional study recruited 100 vitiligo patients and 100 controls with general skin diseases in the Department of Dermatology of Changhua Christian Hospital. Data were obtained using a structured questionnaire for demographic information and modified Skindex-21 instruments. The QoL was not significantly different between vitiligo patients and controls. Among the vitiligo patients, adults exhibited deteriorated emotional levels and total QoL as compared with non-adults. Married females reported greater levels of emotional disturbance than the unmarried ones. A higher educational level and shorter history of disease were associated with greater emotional impacts. The patients with a generalized type of vitiligo suffered more in total QoL. After multivariate adjustment, the young adult patients aged 20-39 were associated with poorer total QoL. It is suggested that vitiligo patients who are aged between 20 and 39, are married females, are highly educated, have a shorter disease history, and suffer from the generalized type of this disease demonstrate more deterioration in their life quality compared with other vitiligo patients. Care providers should tailor the psychological counseling and treatment accordingly.

A Chunking-for-Pooling Strategy for Cytometric Representation Learning for Automatic Hematologic Malignancy Classification.

Differentiating types of hematologic malignancies is vital to determine therapeutic strategies for the newly diagnosed patients. Flow cytometry (FC) can be used as diagnostic indicator by measuring the multi-parameter fluorescent markers on thousands of antibody-bound cells, but the manual interpretation of large scale flow cytometry data has long been a time-consuming and complicated task for hematologists and laboratory professionals. Past studies have led to the development of representation learning algorithms to perform sample-level automatic classification. In this work, we propose a chunking-for-pooling strategy to include large-scale FC data into a supervised deep representation learning procedure for automatic hematologic malignancy classification. The use of discriminatively-trained representation learning strategy and the fixed-size chunking and pooling design are key components of this framework. It improves the discriminative power of the FC sample-level embedding and simultaneously addresses the robustness issue due to an inevitable use of down-sampling in conventional distribution based approaches for deriving FC representation. We evaluated our framework on two datasets. Our framework outperformed other baseline methods and achieved 92.3% unweighted average recall (UAR) for four-class recognition on the UPMC dataset and 85.0% UAR for five-class recognition on the hema.to dataset. We further compared the robustness of our proposed framework with that of the traditional downsampling approach. Analysis of the effects of the chunk size and the error cases revealed further insights about different hematologic malignancy characteristics in the FC data.

Cytoplasmic phosphorylated ERK1/2 expression in patients with melanoma is associated with tumor stage and metastasis.

Melanoma is the cause of most deaths from skin cancer. The extracellular signal-regulated kinase 1/2 (ERK1/2) pathway has been reported to participate in progression of melanoma in fair skinned populations. ERK1/2 is found in both the cytoplasm and nucleus of cells, and phosphorylated ERK1/2 has been implicated in tumor progression. We investigated the relation between melanoma progression and expression of cytoplasmic and nuclear phosphorylated ERK1/2. We examined 34 surgically resected melanomas and investigated their clinicopathologic characteristics. We found immunostaining of phosphorylated ERK1/2 in all melanomas and faint staining in benign nevi. We found expression of cytoplasmic phosphorylated ERK1/2 in most melanomas; however, nuclear phosphorylated ERK1/2 expression was found in only five melanomas. Expression of cytoplasmic phosphorylated ERK1/2 was related to the tumor stage in melanoma. Nine of 10 cases of distant metastasis were positive for cytoplasmic phosphorylated ERK1/2. Our findings suggest that phosphorylated ERK1/2 expression is relevant to clinical pathology and that in melanoma patients, phosphorylated ERK1/2 expression is found in both the cytoplasm and nucleus. Our findings suggest that cytoplasmic phosphorylated ERK1/2 participates in progression of melanoma and that it could be a useful target for clinical treatment of melanoma.

A Machine Learning Approach to the Classification of Acute Leukemias and Distinction From Nonneoplastic Cytopenias Using Flow Cytometry Data.

OBJECTIVES: Flow cytometry (FC) is critical for the diagnosis and monitoring of hematologic malignancies. Machine learning (ML) methods rapidly classify multidimensional data and should dramatically improve the efficiency of FC data analysis. We aimed to build a model to classify acute leukemias, including acute promyelocytic leukemia (APL), and distinguish them from nonneoplastic cytopenias. We also sought to illustrate a method to identify key FC parameters that contribute to the model's performance. METHODS: Using data from 531 patients who underwent evaluation for cytopenias and/or acute leukemia, we developed an ML model to rapidly distinguish among APL, acute myeloid leukemia/not APL, acute lymphoblastic leukemia, and nonneoplastic cytopenias. Unsupervised learning using gaussian mixture model and Fisher kernel methods were applied to FC listmode data, followed by supervised support vector machine classification. RESULTS: High accuracy (ACC, 94.2%; area under the curve [AUC], 99.5%) was achieved based on the 37-parameter FC panel. Using only 3 parameters, however, yielded similar performance (ACC, 91.7%; AUC, 98.3%) and highlighted the significant contribution of light scatter properties. CONCLUSIONS: Our findings underscore the potential for ML to automatically identify and prioritize FC specimens that have critical results, including APL and other acute leukemias.

Early spot sign is associated with functional outcomes in primary intracerebral hemorrhage survivors.

BACKGROUND: The computed tomography angiography (CTA) spot sign is a validated predictor of hematoma expansion and 30-day mortality in intracerebral hemorrhage (ICH). However, whether the spot sign predicts worse functional outcomes among ICH survivors remains unclear. This study investigated the frequency of the spot sign and its association with functional outcomes and length of hospital stay among ICH survivors. METHODS: This was a retrospective analysis of consecutive patients with primary ICH who received CTA within 24 h from presentation to admission to the emergency department of a single medical center between January 2007 and December 2017. Patients who died before discharge and those referred from other hospitals were excluded. CTAs with motion artifacts were excluded from the analysis. The presence of a spot sign was examined by an experienced neuroradiologist. Functional outcomes were determined based on the modified Rankin Scale (mRS) score and Barthel Index (BI). Severe dependency in activities of daily living (ADL) was defined as BI of ≤60 and severe disability as an mRS score of ≥4. Odds ratio (OR) and multiple linear regression were used as measures of association. RESULTS: In total, 66 patients met the inclusion criteria, of whom 9 (13.64%) were positive for a spot sign. No significant differences were observed in baseline characteristics between patients with and without a spot sign. Patients with a spot sign tended to be severely dependent in ADL at discharge (66.67% vs 41.07%; OR = 2.87; p = 0.15) and were more likely to require ICH-related surgery (66.67% vs 24.56%; OR = 6.14; p = 0.01). In multiple linear regression, patients with a higher spot sign score had a significantly longer hospital stay (coefficient = 9.57; 95% CI = 2.11-17.03; p = 0.013). CONCLUSIONS: The presence of a spot sign is a common finding and is associated with longer hospital stay and possibly worse functional outcomes in ICH survivors.

A Knowledge-Reserved Distillation with Complementary Transfer for Automated FC-based Classification Across Hematological Malignancies.

Acute leukemia often comes with life-threatening prognosis outcome and remains a critical clinical issue today. The implementation of measurable residual disease (MRD) using flow cytometry (FC) is highly effective but the interpretation is time-consuming and suffers from physician idiosyncrasy. Recent machine learning algorithms have been proposed to automatically classify acute leukemia samples with and without MRD to address this clinical need. However, most prior works either validate only on a small data cohort or focus on one specific type of leukemia which lacks generalization. In this work, we propose a transfer learning approach in performing automatic MRD classification that takes advantage of a large scale acute myeloid leukemia (AML) database to facilitate better learning on a small cohort of acute lymphoblastic leukemia (ALL). Specifically, we develop a knowledge-reserved distilled AML pre-trained network with ALL complementary learning to enhance the ALL MRD classification. Our framework achieves 84.5% averaged AUC which shows its transferability across acute leukemia, and our further analysis reveals that younger and elder ALL patient samples benefit more from using the pre-trained AML model.

Imbalances in the disposition of estrogen and naphthalene in breast cancer patients: a potential biomarker of breast cancer risk.

Elevation of naphthoquinones and estrogen quinones, which are reactive metabolites of naphthalene and estrogen, is thought to be an important indicator of naphthalene- and estrogen-induced carcinogenesis. We compared background levels of naphthalene and estrogen quinone-derived adducts in serum albumin (Alb) from 143 women with breast cancer and 119 healthy controls. Cysteinyl adducts of naphthoquinones, including 1,2-naphthoquinone (1,2-NPQ) and 1,4-naphthoquinone (1,4-NPQ), and estrogen quinones, including estrogen-2,3-quinones (E2-2,3-Q) and estrogen-3,4-quinones (E2-3,4-Q), were characterized after adduct cleavage. Levels of estrogen quinones and naphthoquinones were positively correlated in healthy controls, but not in breast cancer patients (p < 0.05). Compared with controls, levels of 1,2-NPQ and E2-3,4-Q were elevated by two- to ten-fold in cancer patients (p < 0.001). To explore the correlation between estrogen- and naphthalene-derived quinone adducts and disease status, we performed linear discriminant analysis of the ratio of 1,2-NPQ-Alb to (1,2-NPQ-Alb plus 1,4-NPQ-Alb) versus the ratio of E2-3,4-Q-2-S-Alb to (E2-2,3-Q-4-S-Alb plus E2-3,4-Q-2-S-Alb) in patients and controls. These two groups were separable using albumin adducts of estrogen quinones and naphthoquinones, with 99.6% overall correct classification rate (overall accuracy). The findings of this study suggest that differences in the disposition of estrogen and naphthalene, and the subsequent elevation of cumulative E2-3,4-Q and 1,2-NPQ may serve as biomarkers of breast cancer risk.

MLL3 Induced by Luteolin Causes Apoptosis in Tamoxifen-Resistant Breast Cancer Cells through H3K4 Monomethylation and Suppression of the PI3K/AKT/mTOR Pathway.

Tamoxifen is one of the most common hormone therapy drug for estrogen receptor (ER)-positive breast cancer. Tumor cells with drug resistance often cause recurrence and metastasis in cancer patients. Luteolin is a natural compound found from various types of vegetables and exhibit anticancer activity in different cancers. This study demonstrated that luteolin inhibits the proliferation and induces apoptosis of tamoxifen-resistant ER-positive breast cancer cells. Luteolin also causes cell cycle arrest at the G2/M phase and decreases mitochondrial membrane potential. Besides, luteolin reduces the levels of activated PI3K/AKT/mTOR signaling pathway. The combination treatment of luteolin and PI3K, AKT, or mTOR inhibitors synergistically increases apoptosis in tamoxifen-resistant ER-positive breast cancer cells. Ras gene family (K-Ras, H-Ras, and N-Ras), an activator of PI3K, was transcriptionally repressed by luteolin via induction of tumor suppressor mixed-lineage leukemia 3 (MLL3) expression. MLL3 increases the level of monomethylation of Histone 3 Lysine 4 on the enhancer and promoter region of Ras genes, thus causes repression of Ras expressions. Our finding implies that luteolin was a promising natural agent against tamoxifen resistance of breast cancer.

Association of surgical margins with local recurrence in patients undergoing breast-conserving surgery after neoadjuvant chemotherapy.

BACKGROUND: The aim of the current study was to report a single-institution experience using breast-conserving surgery after neoadjuvant chemotherapy (NACT), focusing on the association between microscopic resection margin status and locoregional recurrence (LRR). METHODS: Our institutional prospectively maintained database was reviewed to identify patients who were treated with NACT between January 2008 and April 2018. RESULTS: Among the main partial mastectomy specimens available for analysis (n = 161), 28 had margins < 1 mm, 21 had margin width of 1-2 mm and the remaining 112 had margins > 2 mm. LRR occurred in 16 patients (9.9%) and distant metastases were detected in 27 (16.8%) patients. There was no significant difference in the LRR between the > 2 mm margin group with a 60-month cumulative survival of 85.2% compared with 76.2% for the ≤2 mm group (P = 0.335) in the Kaplan-Meier analysis. When we stratified patients by margin widths of ≥1 mm or <  1 mm, there was no LRR-free survival benefit observed for the ≥1 mm pathologic excision margin group in the univariate analysis (hazard ratio = 0.443; 95% confidence interval = 0.142-1.383; P = 0.161) with a 60-month cumulative LRR-free survival of 84.9% compared with 69.5% for the < 1 mm margin cohort (P = 0.150). CONCLUSIONS: In the absence of multiple scattered microscopic tumour foci, a negative margin of no ink on tumour maybe sufficient for stage I-III invasive breast cancer treated with NACT and breast-conserving surgery.

Indocyanine green fluorescence method for sentinel lymph node biopsy in breast cancer.

BACKGROUND/OBJECTIVE: Breast biopsy and analysis of sentinel lymph nodes (SLNs) accurately predict tumor status in the affected basin and help in avoiding unnecessary axillary lymph node dissection, which is associated with remarkable morbidity risk. Blue dye and radioisotope are the most widely used mapping agents, but non-radioactive tracers of comparable accuracy warrant further investigation. This study aimed to investigate utilization of indocyanine green (ICG) fluorescence in sentinel node localization compared with blue dye and to assess the incremental value of ICG. METHODS: A total of 39 consecutive patients underwent sentinel lymph node biopsy (SLNB) (40 cases: 38 unilateral and 1 bilateral) with combined blue dye and ICG for localization. The obtained fluorescence images of the lymphatic system were investigated. RESULTS: All 84 lymph nodes removed in 40 procedures were identified by ICG, but only 37 were identified by blue dye. The ICG method identified an average of 2.1 SLNs in 39 of 40 cases with a detection rate of 97.5%, but only 0.93 SLN per case with blue dye. Subcutaneous lymphatic channel patterns were also detected by fluorescent imaging in 37 procedures, which all revealed lymphatic drainage toward the axilla except in one case with internal mammary pathway. CONCLUSION: This study demonstrated the accuracy and safety of ICG for SLNB and its superiority to blue dye method in SLN localization. Therefore, ICG fluorescence method is safe and effective addition in breast clinical settings, wherein blue dye alone is used.

In vitro proliferation of human epidermal melanocytes biopsied from multiple anatomical sites.

BACKGROUND: Vitiligo is an acquired disease that involves low pigment variation in the skin. The use of cultured melanocytes for treatment of recalcitrant vitiligo has become a well-established treatment modality. In vitro cultured melanocytes present an effective autologous transplantation treatment modality for vitiligo. The present study investigated the in vitro culture of epidermal melanocytes sampled from multiple body parts, as well as the differences in total propagation time. METHODS: Skin specimens were collected from 30 participants (14 males and 16 females) who supplied normal colored skin sections from various regions of their body, including the face, chest, abdomen, buttock, and extremities. Subsequently, all of these specimens were treated with an identical melanocyte purification and culturing process. RESULTS: Melanocytes from the face demonstrated the most rapid growth and longest total propagation time. Melanocytes from the buttock, abdomen, and extremities displayed similar results to one another, and melanocytes from the chest and back had the slowest growth and shortest total propagation time. CONCLUSION: Selecting the most favorable site to obtain epidermal melanocytes will reduce the required quantity of skin and culturing time, and maximize the growth and total propagation time of melanocytes. Therefore, care should be exercised when selecting the region of skin when culturing epidermal melanocytes.

A BLSTM with Attention Network for Predicting Acute Myeloid Leukemia Patient's Prognosis using Comprehensive Clinical Parameters.

The prognosis management is crucial for highrisk disease like Acute Myeloid Leukemia (AML) in order to support decisions of clinical treatment. However, the challenges of accurate and consistent forecasting lie in the high variability of the disease outcomes and the complexity of the multiple clinical measurements available over the course of the treatment. In order to capture the multi-dimensional and longitudinal aspect of these comprehensive clinical parameters, we utilize an attention-based bi-directional long shortterm memory (Att-BLSTM) network to predict AML patient's survival and relapse. Specifically, we gather a 10-year worth of real patient's clinical data including blood test, medication, HSCT status, and gene mutation information. Our proposed Att-BLSTM framework achieves 77.1% and 67.3% AUC in tasks of predicting the next 2-year mortality and disease relapse with these comprehensive clinical parameters, and our further analysis demonstrates that a next 0 to 3 months prediction performs equally well, i.e., 74.8% and 67% AUC for mortality and relapse respectively.

Learning a Cytometric Deep Phenotype Embedding for Automatic Hematological Malignancies Classification.

Identification of minimal residual disease (MRD) is important in assessing the prognosis of acute myeloid leukemia (AML) and myelodysplastic syndrome (MDS). The current best clinical practice relies heavily on Flow Cytometry (FC) examination. However, the current FC diagnostic examination requires trained physicians to perform lengthy manual interpretation on high-dimensional FC data measurements of each specimen. The difficulty in handling idiosyncrasy between interpreters along with the time-consuming diagnostic process has become one of the major bottlenecks in advancing the treatment of hematological diseases. In this work, we develop an automatic MRD classifications (AML, MDS, normal) algorithm based on learning a deep phenotype representation from a large cohort of retrospective clinical data with over 2000 real patients' FC samples. We propose to learn a cytometric deep embedding through cell-level autoencoder combined with specimen-level latent Fisher-scoring vectorization. Our method achieves an average AUC of 0.943 across four different hematological malignancies classification tasks, and our analysis further reveals that with only half of the FC markers would be sufficient in obtaining these high recognition accuracies.

Mangiferin Potentiates Neuroprotection by Isoflurane in Neonatal Hypoxic Brain Injury by Reducing Oxidative Stress and Activation of Phosphatidylinositol-3-Kinase/Akt/Mammalian Target of Rapamycin (PI3K/Akt/mTOR) Signaling.

BACKGROUND Hypoxic-ischemic brain injury in the perinatal period is a main cause of perinatal mortality and neurologic complications in neonates and children. Recent studies have focused on the neuroprotective effect of anesthetic drugs. The volatile anesthetic isoflurane has been shown to exert neuroprotective effects in cerebral ischemia. Mangiferin is a natural polyphenol with various pharmacological properties, including antioxidant and ant-tumor effects. This study aimed to determine whether mangiferin potentiates the neuroprotective effects of isoflurane and also if mangiferin when administered alone exerts neuroprotective effects following hypoxic-ischemic brain injury. MATERIAL AND METHODS Sprague-Dawley rats were subjected to cerebral hypoxic ischemia on postnatal day 10 (P10). Mangiferin (50, 100, or 200 mg/kg b.w.) was intragastrically administered from P3 to P12 and 1 h prior to insult on the day of ischemic induction. At 3 h after hypoxia-ischemia (HI) insult, separate groups of rat pups were exposed to isoflurane (1.5%) for 6 h. Following 48 h of HI, the rats were sacrificed and brain tissues were used for analysis. RESULTS Mangiferin treatment attenuated neuronal apoptosis and reduced cerebral infarct volume. The expression of cleaved caspase-3 and apoptotic cascade proteins were regulated. The levels of reactive oxygen species (ROS) and malondialdehyde were reduced by mangiferin and/or isoflurane exposure. The levels of antioxidant glutathione were considerably raised under HI injury, which was modulated by mangiferin and isoflurane exposure. The PI3K/Akt signaling pathway, which was downregulated following HI insult, was activated by mangiferin and/or isoflurane. CONCLUSIONS This study reveals the potent neuroprotective efficacy of mangiferin against HI-induced brain injury via effectively modulating apoptotic pathways, ROS levels, and PI3K/Akt cascades while potentiating protective effects of isoflurane.