A Novel Expert System for Diagnosis of Iron Deficiency Anemia.

Diagnosis of a disease is one of the most important processes in the field of medicine. Thus, computer-aided detection systems are becoming increasingly important to assist physicians. The iron deficiency anemia (IDA) is a serious health problem that requires careful diagnosis. Diagnosis of IDA is a classification problem, and there are various studies conducted. Researchers also use feature selection approaches to detect significant variables. Studies so far investigate different classification problems such as outliers, class imbalance, presence of noise, and multicollinearity. However, datasets are usually affected by more than one of these problems. In this study, we aimed to create multiple systems that can separate diseased and healthy individuals and detect the variables that have a significant effect on these diseases considering influential classification problems. For this, we prepared different datasets based on the original dataset whose outliers were removed using different outlier detection methods. Then, a multistep classification algorithm was proposed for each dataset to see the results under irregular and regulated conditions. In each step, a different classification problem is handled. The results showed that it is important to consider each question together as it can and should change the outcome. Dataset and R codes used in the study are available as supplementary files online.

Developing an AI-assisted clinical decision support system to enhance in-patient holistic health care.

Holistic health care (HHC) is a synonym for complete patient care, and as such an efficient clinical decision support system (CDSS) for HHC is critical to support the judgement of physician's decision in response of patient's physical, emotional, social, economic, and spiritual needs. The field of artificial intelligence (AI) has evolved considerably in the past decades and many AI applications have been deployed in various contexts. Therefore, this study aims to propose an AI-assisted CDSS model that predicts patients in need of HHC and applies an improved recurrent neural network (RNN) model, long short-term memory (LSTM) for the prediction. The data sources include in-patient's comorbidity status and daily vital sign attributes such as blood pressure, heart rate, oxygen prescription, etc. A two-year dataset consisting of 121 thousand anonymized patient cases with 890 thousand physiological medical records was obtained from a medical center in Taiwan for system evaluation. Comparing with the rule-based expert system, the proposed AI-assisted CDSS improves sensitivity from 26.44% to 80.84% and specificity from 99.23% to 99.95%. The experimental results demonstrate that an AI-assisted CDSS could efficiently predict HHC patients.

Nutrient expert system optimizes fertilizer management to improve potato productivity and tuber quality.

BACKGROUND: Improving potato productivity and quality plays an important role in enhancing global food security and human health. However, inappropriate fertilizer management negatively affects potato growth and tuber development, especially in developing countries where there are large numbers of smallholders without modern soil testing equipment. Nutrient Expert (NE), a new and convenient fertilization decision system, was evaluated in the present study by conducting four site-years field experiments in Northeast China, aiming to determine its effectiveness and applicability for potato production relative to local farmers' practice (FP) and fertilizer recommendation based on soil testing (ST). RESULTS: The excessive fertilization at planting promoted seedling growth for potato plants in FP. Nevertheless, superior plant growth and tuber development were observed in NE at the middle and later growing stages, by optimizing fertilizer input and implementing split fertilization. Overall, compared to FP, the NE system increased total and marketable tuber yields by 12-15% and 16-26%, respectively, at the same time as obtaining 19-31% higher net returns and enhanced fertilizer use efficiencies. Moreover, NE improved tuber quality by increasing the contents of starch, soluble protein and vitamin C and decreasing reducing sugar content relative to FP, as well as increasing starch yields by 23-52%. The ST method also showed comprehensive improvements in potato performances compared to FP, although it did not show any advantages compared to NE system. CONCLUSION: The NE system improved potato productivity and tuber quality by optimizing fertilization management, which is an effective and promising alternative to the ST method for potato production in China and other developing countries. © 2021 Society of Chemical Industry.

Formulation of Medicated Chewing Gum Containing Sceletium tortuosum and Process Optimization Utilizing the SeDeM Diagram Expert System.

Sceletium tortuosum is one of the most promising medicinal plant species for treating anxiety and depression. Traditionally, aerial parts are chewed (masticatory herbal medicine) providing fast relief and rendering the masticatory route for delivery, ideal. This study intended formulating novel medicated chewing gum containing S. tortuosum to alleviate depression and anxiety. S. tortuosum extract was formulated into directly compressed medicated chewing gum (MCG) containing different Health-in-Gum® (HIG) bases through process optimization with the SeDeM Diagram Expert System. Physical properties of MCGs were characterized, and specialized drug release studies performed. According to the manufacturer, only HIG-03 was specifically developed for direct compression; however, the SeDeM System was successfully applied to all HIG-bases investigated. HIG-01 and HIG-04 are also considered useful in direct compression as no considerable differences in these MCG formulations' physical properties were recognized. Inclusion of a lubricant, however, is deemed essential, and MCG comprising HIG-01, most suited for direct compression. Dissolution experiments found only two alkaloids used as markers, mesembrine and mesembrenone, were released in quantifiable concentrations regardless formulation constituents. Novel directly compressed MCG-containing S. tortuosum extract was successfully formulated by which the biologically active phytochemicals of S. tortuosum can be scientifically delivered through the traditionally applied mastication method.

Web-based expert system with quick response code for beta-thalassemia management.

ß-thalassemia is an inherited blood disorder in which the body cannot produce hemoglobin normally. Since patients with this condition receive blood transfusions regularly, iron builds up primarily in organs such as the heart, liver and endocrine glands. Accumulation of iron in the organs necessitates chelation therapy. These patients must visit the hospital frequently to assess and follow up on their health condition. Physician intervention is required after each regular assessment to adjust the treatment. Lifelong healthcare support using a web-based expert system with a quick response code is designed for ß-thalassemia management in order to deliver benefits to patients, physicians, and other healthcare providers. The aim of this study is to implement a web-based expert system for ß-thalassemia management in order to provide treatment recommendations and support the lifelong healthcare of patients. The system provides patient-related details, such as medical history, medicines, and appointments, in real-time. It has been also tested in real-life cases and shown to enhance ß-thalassemia management.

Computer Kinesiology: New Diagnostic and Therapeutic Tool for Lower Back Pain Treatment (Pilot Study).

The aim of this study was to demonstrate the effectiveness of the diagnostic and therapeutic medical information system Computer Kinesiology in physiotherapy in patients with low back pain who were not responding to conventional therapy. Computer Kinesiology is primarily intended for the diagnostics and therapy of functional disorders of the locomotor system. This pilot study population included 55 patients (Group 1) with acute and chronic back pain and 51 persons (Group 2) without back pain. The third group was a control group of 67 healthy volunteers with no evidence of musculoskeletal pathologies and no back pain. All 173 subjects were examined three times by the diagnostic part of the Computer Kinesiology method. Groups 1 and 2 were treated after every diagnostics. Group 3 was not treated. The effect was evaluated by H score. Improvements after therapy were defined by reducing the H score by at least 1 point. In Group 1, the H score decreased by at least 1 point in 87.3% (95% CI: 75.5-94.7) and in Group 2 in 78.4% (95% CI: 64.7-88.7). There was no change of distribution of H Score grade in Group 3. The improvement neither depended on gender, age, and BMI nor was it influenced by the length of the therapy. This study demonstrated a high therapeutic efficacy of the Computer Kinesiology system in patients with back pain (Group 1) and in persons without back pain (Group 2) who used the Computer Kinesiology system for primary and secondary prevention of back pain.

Using the Recommendations Inferred by a Decision Support System to Compare Breast Cancer Clinical Practice Guidelines.

Numerous studies have reported that inconsistencies exist between clinical practice guidelines (CPGs) elaborated on the same topic and at the same date. These results are usually established from the analyses handled on narrative CPGs or on their semi-structured version. In the context of the European-funded DESIREE project, we have developed a guideline-based decision support system embedding various contemporary CPGs on breast cancer management. We have run the GL-DSS on a sample of 571 retrospective clinical cases and specifically assessed the level of inconsistencies between the recommendations issued by the US National Comprehensive Cancer Network (NCCN) Guidelines for Breast Cancer and those issued by Assistance Publique - Hôpitaux de Paris (AP-HP, France) CPGs. We proposed a typology with six different situations from total incompatibility to complete identity as a result of the comparison of NCCN and AP-HP CPGs for each clinical case. It was interesting to observe that we got 38% of inconsistencies with 3% of total incompatibility, and 62% of similarity with 0% of complete identity. The silence of one CPG was resolved by the other CPG providing recommendations in 21% of the cases.

Evaluation of Xpert MTB-RIF guided diagnosis and treatment of rifampicin-resistant tuberculosis in Indonesia: A retrospective cohort study.

BACKGROUND: Rifampicin-resistant tuberculosis (RR-TB) is largely underdetected in Indonesia. Xpert MTB/RIF (Xpert) has recently been introduced, prioritizing patients at risk of RR-TB, followed by phenotypic drug-susceptibility (DST) if rifampicin resistance is detected. OBJECTIVE: This study investigated Xpert-based management of presumptive RR-TB cases under routine practice in West Java, Indonesia. METHODS: We examined all records of patients tested with Xpert in the referral hospital for West Java in 2015-2016. We measured loss across a limited cascade of care, time to Xpert diagnosis and the commencement of initial second-line treatment, and identified factors associated with diagnostic and treatment delay. Additionally, we analyzed the appropriateness of treatment according to DST results. RESULTS: Of 3415 patients with presumptive RR-TB, 3215 (94%) were tested by Xpert, of whom 339 (10.5%) were diagnosed as RR-TB. 288 (85%) of 339 RR-TB patients started initial second-line TB treatment, with 48 (14%) patients being lost between diagnosis and pre-treatment assessment. Second-line treatment was commenced at a median of 41 days (IQR 29-70) after RR-TB diagnosis. Delays in both diagnosis and treatment initiation were observed in 104 (52%) of 201 RR-TB patients with identifiable referral date. Rural residence was associated with delay to diagnosis (adjusted OR 2.7; 95%CI 1.5-5.2) and treatment initiation (adjusted OR 2.0; 1.2-3.4). Of 162 patients with available DST result, 107 (66%) had multidrug-resistant tuberculosis (MDR-TB) and 32 (20%) had either pre-extensively drug resistant (pre-XDR) or extensively drug resistant tuberculosis (XDR-TB). We estimated that with the current algorithm 41% of pre-XDR or XDR-TB patients are diagnosed, and 33% of them started on an appropriate treatment regimen. CONCLUSIONS: Many patients with Xpert-diagnosed RR-TB either do not start MDR-TB treatment or encountered diagnostic and treatment delays under programmatic conditions in Indonesia, and most pre-XDR and XDR-TB cases remain undiagnosed. Further expansion and ongoing quality improvement of RR-TB services are urgently needed.

Data-driven knowledge acquisition, validation, and transformation into HL7 Arden Syntax.

OBJECTIVE: The objective of this study is to help a team of physicians and knowledge engineers acquire clinical knowledge from existing practices datasets for treatment of head and neck cancer, to validate the knowledge against published guidelines, to create refined rules, and to incorporate these rules into clinical workflow for clinical decision support. METHODS AND MATERIALS: A team of physicians (clinical domain experts) and knowledge engineers adapt an approach for modeling existing treatment practices into final executable clinical models. For initial work, the oral cavity is selected as the candidate target area for the creation of rules covering a treatment plan for cancer. The final executable model is presented in HL7 Arden Syntax, which helps the clinical knowledge be shared among organizations. We use a data-driven knowledge acquisition approach based on analysis of real patient datasets to generate a predictive model (PM). The PM is converted into a refined-clinical knowledge model (R-CKM), which follows a rigorous validation process. The validation process uses a clinical knowledge model (CKM), which provides the basis for defining underlying validation criteria. The R-CKM is converted into a set of medical logic modules (MLMs) and is evaluated using real patient data from a hospital information system. RESULTS: We selected the oral cavity as the intended site for derivation of all related clinical rules for possible associated treatment plans. A team of physicians analyzed the National Comprehensive Cancer Network (NCCN) guidelines for the oral cavity and created a common CKM. Among the decision tree algorithms, chi-squared automatic interaction detection (CHAID) was applied to a refined dataset of 1229 patients to generate the PM. The PM was tested on a disjoint dataset of 739 patients, which gives 59.0% accuracy. Using a rigorous validation process, the R-CKM was created from the PM as the final model, after conforming to the CKM. The R-CKM was converted into four candidate MLMs, and was used to evaluate real data from 739 patients, yielding efficient performance with 53.0% accuracy. CONCLUSION: Data-driven knowledge acquisition and validation against published guidelines were used to help a team of physicians and knowledge engineers create executable clinical knowledge. The advantages of the R-CKM are twofold: it reflects real practices and conforms to standard guidelines, while providing optimal accuracy comparable to that of a PM. The proposed approach yields better insight into the steps of knowledge acquisition and enhances collaboration efforts of the team of physicians and knowledge engineers.

Prediction of Drug-Drug Interaction between Tacrolimus and Principal Ingredients of Wuzhi Capsule in Chinese Healthy Volunteers Using Physiologically-Based Pharmacokinetic Modelling.

Schisantherin A and schisandrin A, the most abundant active ingredients of Wuzhi capsule, are known to inhibit tacrolimus metabolism by inhibiting CYP3A4/5. We aimed to predict the contribution of schisantherin A and schisandrin A to drug-drug interaction (DDI) between Wuzhi capsule and tacrolimus using physiologically-based pharmacokinetic (PBPK) modelling. Firstly, the inhibition mechanism of schisantherin A and schisandrin A on CYP3A4/5 was investigated. Thereafter, PBPK models of schisantherin A, schisandrin A and tacrolimus were established. Finally, tacrolimus pharmacokinetics were evaluated after the combined use with schisantherin A or schisandrin A. The blood area under the curve (AUC) of tacrolimus increased 1.77- and 2.61-fold after a single dose and multiple doses of schisantherin A, respectively. Meanwhile, schisandrin A inhibited tacrolimus metabolism to a smaller extent. Also, it showed that mechanism-based inhibition (MBI) played a more important role in DDI than reversible inhibition after long-term administration, while reversible inhibition was comparable to MBI after single-dose administration. In conclusion, we utilized PBPK modelling to quantify the contribution of schisantherin A and schisandrin A to DDI between tacrolimus and Wuzhi capsule. This may provide more insights for the rational use of this drug combination.

Inhibition of metastasis and angiogenesis in Hep-2 cells by wheatgrass extract - an in vitro and in silico approach.

Metastasis is the major hindrance in the treatment of all cancers, including laryngeal squamous cell carcinoma. Intensive researches are under way to identify the effective natural polyphenols with anti-metastatic ability for cancer treatment. Wheatgrass, an herbal plant has been reported to show anticancer effects. Hence, in this study, we aimed to analyze the anti-metastatic effect of methanol extract of wheatgrass (MEWG). The levels of metastatic marker proteins were determined by western blot. PI3K and AKT levels were determined by real time (RT)-PCR analysis. In silico molecular docking was done to check the interaction of the 14 components (identified by HPLC/GCMS) of MEWG with PI3K and AKT. MEWG effectively decreased the metastatic protein expressions, namely VEGF, MMP-9 and COX-2 and increased TIMP-2. RT-PCR results showed reduced m-RNA levels of both PI3K and AKT when compared to control. Molecular docking studies revealed interaction of most of the identified compounds of the extract with the important residues of PI3K and AKT. These findings indicate that MEWG inhibits metastasis and angiogenesis in Hep-2 cells possibly via PI3K/AKT due to the cumulative effect of polyphenols and other constituent present in extract. The compounds of the extract were also found to be directly involved in inhibition of AKT/PI3K, thus could help to restrain metastasis.

In silico identification of milk antihypertensive di- and tripeptides involved in angiotensin I-converting enzyme inhibitory activity.

Identification of bioactive milk peptides could improve food technology through improved selection of food supplements with a focus on antihypertensive properties. We hypothesized that angiotensin I-converting enzyme (ACE) inhibitory activities of milk di- and tripeptides could be predicted using 3-dimensional quantitative structure activity relationship methods and that these activities could be explained through evaluation of structural features (hydrogen bond donor/acceptor, hydrophobic, steric, and electrostatic) that are responsible for this bioactivity. We aimed to build comparative molecular field analysis (CoMFA) models combined with in silico digestion to predict the peptide sequences released from enzymatic digestion and to evaluate peptides without experimental data. Furthermore, molecular docking simulation was performed with the aim to evaluate structural features. Molecular docking simulations revealed that the most potent inhibitory peptides contain hydrophobic amino acids that enter deep into the hydrophobic pocket of the ACE active site and make interactions with its residues. CoMFA results point out favorable steric interactions and electronegativity at the C-terminus of the milk dipeptides. The CoMFA model appears to favor electropositive amino acids at the second place in tripeptides and electronegative interaction with Tyr520. Furthermore, predicted values of ACE inhibitory activity of dipeptides obtained by peptide cutter are relatively high, which recommend them for application as functional food supplements and natural alternatives to ACE inhibitory drugs. This research suggests that obtained 3-dimensional quantitative structure activity relationship models are able to successfully identify milk-derived di- and tripeptides that have significant antihypertensive activity and provide information for screening and design of novel ACE inhibitors that could be used as supplements in human nutrition.

Expert2OWL: A Methodology for Pattern-Based Ontology Development.

The formalization of expert knowledge enables a broad spectrum of applications employing ontologies as underlying technology. These include eLearning, Semantic Web and expert systems. However, the manual construction of such ontologies is time-consuming and thus expensive. Moreover, experts are often unfamiliar with the syntax and semantics of formal ontology languages such as OWL and usually have no experience in developing formal ontologies. To overcome these barriers, we developed a new method and tool, called Expert2OWL that provides efficient features to support the construction of OWL ontologies using GFO (General Formal Ontology) as a top-level ontology. This method allows a close and effective collaboration between ontologists and domain experts. Essentially, this tool integrates Excel spreadsheets as part of a pattern-based ontology development and refinement process. Expert2OWL enables us to expedite the development process and modularize the resulting ontologies. We applied this method in the field of Chinese Herbal Medicine (CHM) and used Expert2OWL to automatically generate an accurate Chinese Herbology ontology (CHO). The expressivity of CHO was tested and evaluated using ontology query languages SPARQL and DL. CHO shows promising results and can generate answers to important scientific questions such as which Chinese herbal formulas contain which substances, which substances treat which diseases, and which ones are the most frequently used in CHM.

Gamma-linolenic acid ameliorated glycation-induced memory impairment in rats.

CONTEXT: γ-Linolenic acid (GLA) is an important constituent of anti-ageing supplements. OBJECTIVE: The current study investigates the anti-ageing effect of GLA in Sprague-Dawley rats. MATERIALS AND METHODS: GLA (0.1, 0.2, 0.4, 2, 10, 20 and 24 µM) was initially evaluated for its effect on the formation of advanced glycation end products (AGEs) in vitro. For in vivo assessment (1, 5 or 15 mg/kg), the rat model of accelerated ageing was developed using d-fructose (1000 mg/kg (i.p.) plus 10% in drinking water for 40 days). Morris water maze was used to evaluate impairment in learning and memory. The blood of treated animals was used to measure glycosylated haemoglobin (HbA1c) levels. The interaction of GLA with active residues of receptor of AGE (RAGE) was analyzed using AutoDock Vina. RESULTS: Our data showed that GLA inhibited the production of AGEs (IC50 = 1.12 ± 0.05 µM). However, this effect was more significant at lower tested doses. A similar pattern was also observed in in vivo experiments, where the effect of fructose was reversed by GLA only at lowest tested dose of 1 mg/kg. The HbA1c levels also revealed significant reduction at lower doses (1 and 5 mg/kg). The in silico data exhibited promising interaction of GLA with active residues (Try72, Arg77 and Gln67) of RAGE. CONCLUSION: The GLA, at lower doses, possesses therapeutic potential against glycation-induced memory decline.

Current limitations and future opportunities for prediction of DILI from in vitro.

Drug-induced liver injury (DILI) is a major concern for drug developers, regulators and clinicians. It is triggered by drug and xenobiotic insults leading to liver impairment or damage, in the worst-case liver failure. In contrast to acute "intrinsic" hepatotoxicity, DILI typically manifests in a very small subset of the population under treatment with no clear dose relationship and inconsistent temporal patterns and is therefore termed an idiosyncratic event. Involved are multifactorial, compound-dependent mechanisms and host-specific factors, making the prediction in preclinical test systems very challenging. While preclinical safety studies in animals usually are able to capture direct, acute liver toxicities, they are less predictive for human DILI, where specific, human-derived in vitro models can potentially close the gap. On one hand, mechanistic approaches addressing key mechanisms involved in DILI in well-characterized and standardized in vitro test systems have been developed. On the other hand, co-cultures of different cell types, including patient- and/or stem cell-derived cells, in a three-dimensional setup allow for prolonged incubations and multiplexed readouts. Such complex setups might better reflect multifactorial human DILI. One major challenge is that for many compounds with human DILI the underlying mechanisms are not yet fully understood, complicating establishment and validation of predictive cellular tools. A tiered approach including rapid mechanism-based in vitro screens followed by confirmatory tests in more physiologically relevant models might allow minimizing DILI risk early on in vitro. Such complex, integrated approaches will gain from larger collaborations in multidisciplinary groups bringing existing knowledge and state-of-the-art technology together.

Predicting Renal Failure Progression in Chronic Kidney Disease Using Integrated Intelligent Fuzzy Expert System.

BACKGROUND: Chronic kidney disease (CKD) is a covert disease. Accurate prediction of CKD progression over time is necessary for reducing its costs and mortality rates. The present study proposes an adaptive neurofuzzy inference system (ANFIS) for predicting the renal failure timeframe of CKD based on real clinical data. METHODS: This study used 10-year clinical records of newly diagnosed CKD patients. The threshold value of 15 cc/kg/min/1.73 m(2) of glomerular filtration rate (GFR) was used as the marker of renal failure. A Takagi-Sugeno type ANFIS model was used to predict GFR values. Variables of age, sex, weight, underlying diseases, diastolic blood pressure, creatinine, calcium, phosphorus, uric acid, and GFR were initially selected for the predicting model. RESULTS: Weight, diastolic blood pressure, diabetes mellitus as underlying disease, and current GFR(t) showed significant correlation with GFRs and were selected as the inputs of model. The comparisons of the predicted values with the real data showed that the ANFIS model could accurately estimate GFR variations in all sequential periods (Normalized Mean Absolute Error lower than 5%). CONCLUSIONS: Despite the high uncertainties of human body and dynamic nature of CKD progression, our model can accurately predict the GFR variations at long future periods.

Expert system for skin problem consultation in Thai traditional medicine.

BACKGROUND: This paper aimed to demonstrate the research and development of a rule-based expert system for skin problem consulting in the areas of acne, melasma, freckle, wrinkle, and uneven skin tone, with recommended treatments from Thai traditional medicine knowledge. MATERIALS AND METHODS: The tool selected for developing the expert system is a software program written in the PHP language. MySQL database is used to work together with PHP for building database of the expert system. The system is web-based and can be reached from anywhere with Internet access. RESULTS: The developed expert system gave recommendations on the skin problem treatment with Thai herbal recipes and Thai herbal cosmetics based on 416 rules derived from primary and secondary sources. The system had been tested by 50 users consisting of dermatologists, Thai traditional medicine doctors, and general users. The developed system was considered good for learning and consultation. CONCLUSION: The present work showed how such a scattered body of traditional knowledge as Thai traditional medicine and herbal recipes could be collected, organised and made accessible to users and interested parties. The expert system developed herein should contribute in a meaningful way towards preserving the knowledge and helping promote the use of Thai traditional medicine as a practical alternative medicine for the treatment of illnesses.

A decision support system for the treatment of patients with ventricular assist device support.

BACKGROUND: Heart failure (HF) is affecting millions of people every year and it is characterized by impaired ventricular performance, exercise intolerance and shortened life expectancy. Despite significant advancements in drug therapy, mortality of the disease remains excessively high, as heart transplant remains the gold standard treatment for end-stage HF when no contraindications subsist. Traditionally, implanted Ventricular Assist Devices (VADs) have been employed in order to provide circulatory support to patients who cannot survive the waiting time to transplantation, reducing the workload imposed on the heart. In many cases that process could recover its contractility performance. OBJECTIVES: The SensorART platform focuses on the management and remote treatment of patients suffering from HF. It provides an interoperable, extendable and VAD-independent solution, which incorporates various hardware and software components in a holistic approach, in order to improve the quality of the patients' treatment and the workflow of the specialists. This paper focuses on the description and analysis of Specialist's Decision Support System (SDSS), an innovative component of the SensorART platform. METHODS: The SDSS is a Web-based tool that assists specialists on designing the therapy plan for their patients before and after VAD implantation, analyzing patients' data, extracting new knowledge, and making informative decisions. RESULTS: SDSS offers support to medical and VAD experts through the different phases of VAD therapy, incorporating several tools covering all related fields; Statistics, Association Rules, Monitoring, Treatment, Weaning, Speed and Suction Detection. CONCLUSIONS: SDSS and its modules have been tested in a number of patients and the results are encouraging.

Study on discrimination of white tea and albino tea based on near-infrared spectroscopy and chemometrics.

BACKGROUND: White tea and albino tea have their own nutritional characteristics, but from the appearance they are quite similar to each other. It is not easy to distinguish them with existing analytical tools or by visual inspection. The current study proposed a rapid method to discriminate them based on near-infrared (NIR) spectroscopy associated with supervised pattern recognition methods. RESULTS: For this purpose, discriminant partial least-squares (DPLS) and discriminant analysis (DA) were employed to build classification models on the basis of a reduced subset of wavenumbers and different pretreatment methods. A completely independent validation set was also used to test the model performance. The results of the DA model showed that with the SNV Karl Norris derivative spectral pre-treatment samples from the two different origins could be 100% correctly discriminated. Similarly, for the DPLS model, the best classification results were obtained with the multiplicative scattering correction (MSC) + first derivative spectral pre-treatments; the accuracy of identification was 98.48% for the calibration set and 100% for the validation set. CONCLUSION: The overall results demonstrated that NIR spectroscopy with pattern recognition could be successfully applied to discriminate white tea and albino tea quickly and non-destructively without the need for various analytical determinations.

Brine shrimp lethality assay 'an effective prescreen': microwave-assisted synthesis, BSL toxicity and 3DQSAR studies-based designing, docking and antitumor evaluation of potent chalcones.

CONTEXT: In the course of searching potential antitumor agents from a library of chalcones synthesized under microwave irradiations, the brine shrimp lethality (BSL) assay and a 3D structure-activity relationship (3DQSAR) studies were followed by the antitumor evaluation of most potent analogues. OBJECTIVE: The objective of the current study was to effectively use the BSL assay for the identification of potential cytotoxic analogues from a set of compounds. METHODS: We applied the comparative molecular field analysis (CoMFA) and devised 3DQSAR on 33 synthesized chalcones leading to prediction of five related compounds with improved activity. The scope of BSL assay for the prediction of antitumor potency was tested through the in vitro antitumor studies against six human tumor cell-lines, docking studies and the tubulin-polymerization assay. RESULTS: The newly designed compounds 34-38 displayed very promising cytotoxic potency. From our results, the BSL toxicity, antitumor efficacy and docking outcomes could be easily co-related. CONCLUSION: The study draws a very good relationship between a simple, inexpensive, and bench-top BSL assay and the antitumor potential of the cytotoxic compounds. Devising the CoMFA analysis helped in designing chalcones with improved cytotoxic potential as displayed through their BSL and cytotoxic activity against human tumor cell lines. The studies are noteworthy as such comprehensive studies were never performed before on the BSL assay. The present studies widen the scope of the BSL model that may prove quite helpful as a preliminary screen in the antitumor drug designing and synthesis expeditions.