Iron deficiency anaemia (IDA) frequently occurs in haemodialysis
(HD) patients undergoing recombinant human erythropoietin (rHuEPO)
therapy and is commonly associated with rHuEPO hypo-responsiveness.
However, the conventional iron indices are inadequate to exhibit the status or
utilisation of iron during erythropoiesis. The aim of this study was to elucidate
the accuracy and usefulness of the reticulocyte haemoglobin (RET-He) test
for diagnosing IDA in HD patients undergoing rHuEPO therapy. Methods: In
this cross-sectional study, fifty-five blood samples of HD patients on rHuEPO
therapy were collected and analysed for haematological and biochemical
parameters. A receiver operating characteristics curve was also plotted for
sensitivity and specificity analysis. IDA detection rates by RET-He, soluble
transferrin receptor (sTfR) and serum ferritin were 63.64%, 3.64% and 0%,
respectively. RET-He level was significantly correlated with sTfR level, mean
cell volume, mean cell haemoglobin level and the transferrin receptor-ferritin
index. The sensitivity and specificity of RET-He in detecting IDA were 78.3%
and 92.0%, respectively, with an area under the curve of 0.864. IDA was more
frequently detected by RET-He than by ferritin or sTfR in HD patients
undergoing rHuEPO therapy. The RET-He level also showed higher sensitivity
and specificity for the iron status in these patients. Therefore, RET-He is a
useful biomarker for the detection of IDA in HD patients undergoing rHuEPO
therapy.
Supracondylar humeral fracture is the most common elbow injury in children. It may be associated with a vascular injury in nearly 20% of the cases with a pink pulseless limb. We present a unique case of a paediatric pink pulseless supracondylar humeral fracture, seen late, on the 16th-day post-trauma. Open reduction, cross Kirschner wiring, and brachial artery exploration and repair were performed, and the patient recovered well. Early open reduction and exploration of the brachial artery with or without prior CT angiography was a safe approach in treating patients who presented at 16 days.
Recent advancements in deep learning (DL) have played a crucial role in aiding experts to develop personalized healthcare services, particularly in drug response prediction (DRP) for cancer patients. The DL's techniques contribution to this field is significant, and they have proven indispensable in the medical field. This review aims to analyze the diverse effectiveness of various DL models in making these predictions, drawing on research published from 2017 to 2023. We utilized the VOS-Viewer 1.6.18 software to create a word cloud from the titles and abstracts of the selected studies. This study offers insights into the focus areas within DL models used for drug response. The word cloud revealed a strong link between certain keywords and grouped themes, highlighting terms such as deep learning, machine learning, precision medicine, precision oncology, drug response prediction, and personalized medicine. In order to achieve an advance in DRP using DL, the researchers need to work on enhancing the models' generalizability and interoperability. It is also crucial to develop models that not only accurately represent various architectures but also simplify these architectures, balancing the complexity with the predictive capabilities. In the future, researchers should try to combine methods that make DL models easier to understand; this will make DRP reviews more open and help doctors trust the decisions made by DL models in cancer DRP.
BACKGROUND: The ethiopathogenesis of increased apoptosis of lymphocytes in systemic lupus erythematosus (SLE) is still incompletely understood but anti-C1q autoantibodies have been shown to induce apoptosis in lymphocytes from healthy donors and certain cell lines.
AIM: This study was undertaken to investigate the relationship between peripheral lymphocyte apoptosis and serum levels of anti-C1q autoantibodies in SLE patients.
METHODS: The sera of 124 patients with SLE involving 62 active SLE and 62 inactive SLE, fulfilling America College of Rheumatology (ACR) classification criteria for SLE (1997) were incubated with peripheral blood lymphocytes of healthy donors. The results were compared with 124 sex- and age-matched normal controls. Apoptotic lymphocytes (AL) were detected by flow cytometry using annexin V and propidium iodide binding. Anti-C1q autoantibodies were detected by an enzyme-linked immunoassay kit for all SLE patients.
RESULTS: Results demonstrated that the percentage of AL in the peripheral blood of active SLE patients was significantly higher (n = 62, 34.95 ± 12.78%) than that of the inactive SLE patients (n = 62, 30.69 ± 10.13%, P = 0.042, 95%CI = 0.16-8.36) and normal controls (n = 124, 27.92 ± 10.22%, P = 0.001, 95%CI = 3.33-10.73). The percentage of AL significantly correlated with serum levels of anti-C1q autoantibodies in the active SLE patients (r = 0.263, P = 0.039) but not in the inactive SLE patients (r = 0.170, P = 0.185).
CONCLUSION: The results of this study suggest that increased serum levels of anti-C1q autoantibodies are responsible for apoptosis and may play a pathogenic role in SLE patients, especially in active disease.
KEYWORDS: anti-C1q; apoptosis; flowcytometry; systemic lupus erythematosus
Study site: Medical outpatient clinic and medical wards, Hospital Universiti Sains Malaysia (HUSM), Kelantan, Malaysia
Gene regulatory network (GRN) comprises complicated yet intertwined gene-regulator relationships. Understanding the GRN dynamics will unravel the complexity behind the observed gene expressions. Insect gene regulation is often complicated due to their complex life cycles and diverse ecological adaptations. The main interest of this review is to have an update on the current mathematical modelling methods of GRNs to explain insect science. Several popular GRN architecture models are discussed, together with examples of applications in insect science. In the last part of this review, each model is compared from different aspects, including network scalability, computation complexity, robustness to noise and biological relevancy.
The metabolic network is the reconstruction of the metabolic pathway of an organism that is used to represent the interaction between enzymes and metabolites in genome level. Meanwhile, metabolic engineering is a process that modifies the metabolic network of a cell to increase the production of metabolites. However, the metabolic networks are too complex that cause problem in identifying near-optimal knockout genes/reactions for maximizing the metabolite's production. Therefore, through constraint-based modelling, various metaheuristic algorithms have been improvised to optimize the desired phenotypes. In this paper, PSOMOMA was compared with CSMOMA and ABCMOMA for maximizing the production of succinic acid in E. coli. Furthermore, the results obtained from PSOMOMA were validated with results from the wet lab experiment.
Metabolic engineering involves the modification and alteration of metabolic pathways to improve the production of desired substance. The modification can be made using in silico gene knockout simulation that is able to predict and analyse the disrupted genes which may enhance the metabolites production. Global optimization algorithms have been widely used for identifying gene knockout strategies. However, their productions were less than theoretical maximum and the algorithms are easily trapped into local optima. These algorithms also require a very large computation time to obtain acceptable results. This is due to the complexity of the metabolic models which are high dimensional and contain thousands of reactions. In this paper, a hybrid algorithm of Cuckoo Search and Minimization of Metabolic Adjustment is proposed to overcome the aforementioned problems. The hybrid algorithm searches for the near-optimal set of gene knockouts that leads to the overproduction of metabolites. Computational experiments on two sets of genome-scale metabolic models demonstrate that the proposed algorithm is better than the previous works in terms of growth rate, Biomass Product Couple Yield, and computation time.
In recent years, metabolic engineering has gained central attention in numerous fields of science because of its capability to manipulate metabolic pathways in enhancing the expression of target phenotypes. Due to this, many computational approaches that perform genetic manipulation have been developed in the computational biology field. In metabolic engineering, conventional methods have been utilized to upgrade the generation of lactate and succinate in E. coli, although the yields produced are usually way below their theoretical maxima. To overcome the drawbacks of such conventional methods, development of hybrid algorithm is introduced to obtain an optimal solution by proposing a gene knockout strategy in E. coli which is able to improve the production of lactate and succinate. The objective function of the hybrid algorithm is optimized using a swarm intelligence optimization algorithm and a Simple Constrained Artificial Bee Colony (SCABC) algorithm. The results maximize the production of lactate and succinate by resembling the gene knockout in E. coli. The Flux Balance Analysis (FBA) is integrated in a hybrid algorithm to evaluate the growth rate of E. coli as well as the productions of lactate and succinate. This results in the identification of a gene knockout list that contributes to maximizing the production of lactate and succinate in E. coli.