In silico screening of Fyn kinase inhibitors using classification-based QSAR model, molecular docking, molecular dynamics and ADME study.

This study aimed to use a computational approach that combined the classification-based QSAR model, molecular docking, ADME studies, and molecular dynamics (MD) to identify potential inhibitors of Fyn kinase. First, a robust classification model was developed from a dataset of 1,078 compounds with known Fyn kinase inhibitory activity, using the XGBoost algorithm. After that, molecular docking was performed between potential compounds identified from the QSAR model and Fyn kinase to assess their binding strengths and key interactions, followed by MD simulations. ADME studies were additionally conducted to preliminarily evaluate the pharmacokinetics and drug-like characteristics of these compounds. The results showed that our obtained model exhibited good predictive performance with an accuracy of 0.95 on the test set, affirming its reliability in identifying potent Fyn kinase inhibitors. Through the application of this model in conjunction with molecular docking and ADME studies, nine compounds were identified as potential Fyn kinase inhibitors, including 208 (ZINC70708110), 728 (ZINC8792432), 734 (ZINC8792187), 736 (ZINC8792350), 738 (ZINC8792286), 739 (ZINC8792309), 817 (ZINC33901069), 852 (ZINC20759145), and 1227 (ZINC100006936). MD simulations further demonstrated that the four most promising compounds, 728, 734, 736, and 852 exhibited stable binding with Fyn kinase during the simulation process. Additionally, a web-based platform ( https://fynkinase.streamlit.app/ ) has been developed to streamline the screening process. This platform enables users to predict the activity of their substances of interest on Fyn kinase from their SMILES, using our classification-based QSAR model and molecular docking.

Identification of potential FAK inhibitors using mol2vec molecular descriptor-based QSAR, molecular docking, ADMET study, and molecular dynamics simulation.

This study aims to identify potential focal adhesion kinase (FAK) inhibitors through an integrated computational approach, combining mol2vec descriptor-based QSAR, molecular docking, ADMET study, and molecular dynamics simulation. A dataset of 437 compounds with known FAK inhibitory activities was used to develop QSAR models using machine learning algorithms combined with mol2vec descriptors. Subsequently, the most promising compounds were subjected to molecular docking against FAK to evaluate their binding affinities and key interactions. ADMET study and molecular dynamics simulation were also employed to investigate the pharmacokinetic, drug-like properties, and the stability of the protein-ligand complexes. The results showed that the mol2vec descriptor-based QSAR model established by support vector regression demonstrated good predictive performance (R2 = 0.813, RMSE = 0.453, MAE = 0.263 in case of training set, and R2 = 0.729, RMSE = 0.635, MAE = 0.477 in case of test set), indicating their reliability in identifying potent FAK inhibitors. Using this QSAR model and molecular docking, compound 21 (ZINC000004523722) was identified as the most potential compound, with predicted logIC50 value and binding energy of 2.59 and - 9.3 kcal/mol, respectively. The results of molecular dynamics simulation and ADMET study also further suggested its potential as a promising drug candidate. However, because our research was merely theoretical, additional in vitro and in vivo studies are required for the verification of these results.

Towards safer and efficient formulations: Machine learning approaches to predict drug-excipient compatibility.

Predicting drug-excipient compatibility is a critical aspect of pharmaceutical formulation design. In this study, we introduced an innovative approach that leverages machine learning techniques to improve the accuracy of drug-excipient compatibility predictions. Mol2vec and 2D molecular descriptors combined with the stacking technique were used to improve the performance of the model. This approach achieved a significant advancement in the predictive capacity as demonstrated by the accuracy, precision, recall, AUC, and MCC of 0.98, 0.87, 0.88, 0.93 and 0.86, respectively. Using the DE-INTERACT model as the benchmark, our stacking model could remarkably detect drug-excipient incompatibility in 10/12 tested cases, while DE-INTERACT managed to recognize only 3 out of 12 incompatibility cases in the validation experiments. To ensure user accessibility, the trained model was deployed to a user-friendly web platform (URL: https://decompatibility.streamlit.app/). This interactive interface accommodated inputs through various types, including names, PubChem CID, or SMILES strings. It promptly generated compatibility predictions alongside corresponding probability scores. However, the continual refinement of model performance is crucial before applying this model in practice.

Deep eutectic solvent combined with soybean as an efficient approach to enhance the content of apigenin in the Chrysanthemum indicum L. extract.

Our research aimed to cost-effectively enhance apigenin content in Chrysanthemum indicum L. extract using soybeans combined with a deep eutectic solvent. First, various deep eutectic solvents were investigated for the extraction of apigenin, followed by soybean treatment to increase aglycon levels. Combining single factor experiments with response surface methodology and optimization algorithms (genetic algorithm and particle swarm optimization), the optimal conditions were also determined. The results revealed that choline chloride-propylene glycol emerged as the optimal solvent. The optimized treatment conditions involved a temperature of 54 °C, a time of 2 h, and the addition of 3 mL of soybean extract, yielding an apigenin content of 3.380 ± 0.031 mg/g - a remarkable eightfold increase compared to the initial extract. The computational study suggested that the deep eutectic solvent may play an important role in stabilizing ß-glucosidase in soybeans. However, further research is needed to scale up and fully elucidate soybean's mechanism.

Oral tolerance to systemic vaccination remains intact without RORγt expression in regulatory T cells.

Many promising vaccine candidates and licensed vaccines lead to variable immune responses within humans. Studies suggest that environmental exposures in the gastrointestinal tract could contribute to a reduction in vaccine efficacy via immune tolerance at this site; this is partly achieved by a high abundance of regulatory T cells (Tregs). It is unclear if Treg subsets regulate systemic vaccine responses following oral antigen pre-exposure. Here, we implemented a conditional knock-out mouse model of RORγt+ Tregs to examine the role of these cells in mediating this process. Following oral exposure to the model antigen ovalbumin (OVA) prior to immunization, we found similar induction of vaccine-induced antibody responses in mice lacking RORγt expression in Tregs compared to sufficient controls. Use of various adjuvants led to distinct findings. Our data suggest that expression of RORγt+ within Tregs is not required to regulate tolerance to systemic vaccination following oral antigen exposure.

The presence of Rickettsia felis in communities in the central highlands of Vietnam.

Rickettsia felis is an emerging flea-borne spotted fever pathogen that causes febrile illness in humans. In Vietnam, R. felis was detected in hospitalized patients, but there is no information on its presence in the Vietnamese community. This cross-sectional study aimed to determine the presence of R. felis in humans of the Central Highlands of Vietnam. A total of 158 blood and 213 serum samples were subjected to PCR and IFAT, respectively, to detect the presence of R. felis DNA and antibodies against R. felis. PCR assays detected R. felis DNA in four out of 158 blood samples, accounting for a prevalence of 2.53 % (95 % CI: 0.81 %-6.76 %). Phylogenetic analysis indicated the presence of R. felis and R. felis genotype RF2125 in the communities in the Central Highlands of Vietnam. The result of IFAT identified seven out of 213 serum samples (3.29 %, 95 % CI: 1.45 %-6.93 %) positive for antibodies against R. felis. This study was the first to demonstrate the presence of active R. felis infections in the communities in the Central Highlands of Vietnam utilizing both molecular and serological methods.

The Regulation of Nucleic Acid Vaccine Responses by the Microbiome.

Nucleic acid vaccines, including both RNA and DNA platforms, are key technologies that have considerable promise in combating both infectious disease and cancer. However, little is known about the extrinsic factors that regulate nucleic acid vaccine responses and which may determine their effectiveness. The microbiome is recognized as a significant regulator of immune development and response, whose role in regulating some traditional vaccine platforms has recently been discovered. Using germ-free and specific pathogen-free mouse models in combination with different protein, DNA, and mRNA vaccine regimens, we demonstrate that the microbiome is a significant regulator of nucleic acid vaccine immunogenicity. Although the presence of the microbiome enhances CD8+ T cell responses to mRNA lipid nanoparticle immunization, the microbiome suppresses Ig and CD4+ T cell responses to DNA-prime, DNA-protein-boost immunization, indicating contrasting roles for the microbiome in the regulation of these different nucleic acid vaccine platforms. In the case of mRNA lipid nanoparticle vaccination, germ-free mice display reduced dendritic cell/macrophage activation that may underlie the deficient vaccine response. Our study identifies the microbiome as a relevant determinant of nucleic acid vaccine response with implications for continued therapeutic development and deployment of these vaccines.

Antimicrobial, Cytotoxic, and α-Glucosidase Inhibitory Activities of Ethanol Extract and Chemical Constituents Isolated from Homotrigona apicalis Propolis-In Vitro and Molecular Docking Studies.

The chemical investigation of Homotrigona apicalis propolis collected in Binh Dinh province, Vietnam, led to the isolation of nine compounds, including four sesquiterpenes: spathulenol (1), 1αH,5ßH-aromandendrane-4ß,10α-diol (2), 1ß,6α-dihydroxy-4(15)-eudesmene (3), and 1ßH,5ßH-aromandendrane-4α,10ß-diol (4); three triterpenes: acetyl oleanolic acid (5), 3α-hydroxytirucalla-8,24-dien-21-oic acid (6), and ursolic acid (7); and two xanthones: cochinchinone A (8) and α-mangostin (9). Sesquiterpens 1-4 and triterpene 6 were isolated for the first time from stingless bee propolis. Plants in the Cratoxylum and Aglaia genus were suggested as resin sources of the propolis sample. In the antibacterial activity evaluation, the EtOH extract only showed moderate activity on S. aureus, while the isolated compounds 7-9 showed good antibacterial activity, with IC50 values of 0.56 to 17.33 µg/mL. The EtOH extract displayed selective cytotoxicity against the A-549 cancer cell line, with IC50 values of 22.82 ± 0.86 µg/mL, and the xanthones 8 and 9 exhibited good activity against the KB, HepG-2, and A-549 cancer cell lines, with IC50 values ranging from 7.55 ± 0.25 µg/mL to 29.27 ± 2.07 µg/mL. The cytotoxic effects of xanthones 8 and 9 were determined by the inhibition of the EGFR and HER2 pathways using a molecular docking study. Compounds 8 and 9 displayed strong binding affinity with EFGR and HER2, with values of -9.3 to -9.9 kcal/mol. Compounds 5, 8, and 9 showed potential α-glucosidase inhibitory activities, which were further confirmed by computational studies. The binding energies of compounds 5, 8, and 9 were lower than that of arcabose.

Optimization of ß-Cyclodextrin-Assisted Extraction of Apigenin and Luteolin from Chrysanthemum indicum L. Using Response Surface Methodology Combined with Different Optimization Algorithms and Evaluation of Its Antioxidant Capacity.

Cyclodextrins and their derivatives have shown successful applications in extracting active compounds from medicinal plants. However, the use of ß-cyclodextrin derivatives for extracting apigenin and luteolin from Chrysanthemum indicum L. remains unexplored. Additionally, the application of nature-inspired optimization algorithms in optimizing extraction conditions has been limited. Therefore, this study was performed with the aims of optimizing the extraction of apigenin and luteolin from C. indicum with the assistance of 2-hydroxypropyl-ß-cyclodextrin (HP-ß-CD) using response surface methodology combined with various optimization algorithms, including desirability function approach, genetic algorithm, particle swarm optimization, and firefly algorithm. The results showed that the optimal conditions obtained by the four algorithms were consistent, with an extraction time of 60 min, HP-ß-CD concentration of 30 mg/mL, and a solvent-to-solid ratio of 24 mg/mL. At these conditions, the apigenin and luteolin contents were 1.362±0.008 and 8.724±0.117 mg/g, respectively. The results also showed that HP-ß-CD-assisted extraction exhibited significantly higher apigenin and luteolin contents compared to conventional solvent. Comparable results were also yielded from the antioxidant assay. Our study suggested that the nature-inspired optimization algorithms might be potential options in enhancing the effectiveness of the traditional response surface methodology for the optimization of extraction of natural products.

The Regulation of Nucleic Acid Vaccine Responses by the Microbiome.

Nucleic acid vaccines, including both RNA and DNA platforms, are key technologies that have considerable promise in combating both infectious disease and cancer. However, little is known about the extrinsic factors that regulate nucleic acid vaccine responses and which may determine their effectiveness. The microbiome is recognized as a significant regulator of immune development and response, whose role in regulating some traditional vaccine platforms has recently been discovered. Using germ-free and specific-pathogen-free mouse models in combination with different protein, DNA, and mRNA vaccine regimens, we demonstrate that the microbiome is a significant regulator of nucleic acid vaccine immunogenicity. While the presence of the microbiome enhances CD8+ T cell responses to mRNA lipid nanoparticle (LNP) immunization, the microbiome suppresses immunoglobulin and CD4+ T cell responses to DNA-prime, DNA-protein-boost immunization, indicating contrasting roles for the microbiome in the regulation of these different nucleic acid vaccine platforms. In the case of mRNA-LNP vaccination, germ-free mice display reduced dendritic cell/macrophage activation that may underlie the deficient vaccine response. Our study identifies the microbiome as a relevant determinant of nucleic acid vaccine response with implications for their continued therapeutic development and deployment.

Self-Determination Theory and Accountant Employees' Psychological Wellbeing: The Roles of Positive Affectivity and Psychological Safety.

This study investigates the influence of self-determination motivations on accountant employees' psychological wellbeing with the mediating role of positive affectivity and the moderating role of psychological safety. Multivariate analysis and structural equation modeling are used to analyze a three-way time-lagged sample data of 391 accountant employees. Results indicate that positive affectivity positively mediates the relationship between extrinsic motivation and psychological wellbeing and between intrinsic motivation and psychological wellbeing. Furthermore, psychological safety positively moderates the relationship between extrinsic motivation and positive affectivity and between intrinsic motivation and positive affectivity. In addition, psychological safety also positively moderates the relationship between positive affectivity and psychological wellbeing. The findings of this study provide implications for researchers and business managers in managing and enhancing accountant employees' psychological wellbeing.

Green extraction of apigenin and luteolin from celery seed using deep eutectic solvent.

In recent years, deep eutectic solvent (DES) has attracted a great deal of attention as an environmentally friendly solvent and could be used as an alternative to conventional solvents. In this study, 34 choline and betaine-based deep eutectic solvents were prepared and investigated the ability to extract apigenin and luteolin from the celery seed. The results showed that DES composed of betaine hydrochloride and propylene glycol had the best extraction efficiency. Using one factor at a time combined with response surface methodology, the optimal conditions of extraction were determined as follows: time of extraction: 19 min, extraction temperature: 75 °C, the water content in solvent: 40% (w/w). Antisolvent (water) combined with distillation was proposed as an efficient method to recover apigenin and luteolin from the DES extract. The relationship between components of DES and solubility of apigenin, luteolin was also established which was the starting point for the prediction of extractability of DES. Molecular dynamics revealed that betaine hydrochloride and propylene glycol could interact with each other by the formation of two types of hydrogen bond while water molecules might play an important role in the ability to dissolve apigenin and luteolin of DES.

Application of multivariate linear regression models for selection of deep eutectic solvent for extraction of apigenin and luteolin from Chrysanthemum indicum L.

INTRODUCTION: Among a variety of compounds presented in chrysanthemum, apigenin and luteolin are the two main components that play a major role in numerous biological activities of this herb. OBJECTIVES: We aimed to obtain linear models showing the dependence of the yield of extraction of apigenin and luteolin on the composition of deep eutectic solvent and investigate the extraction of these two ingredients from Chrysanthemum indicum L. METHODS: Two models showing the dependence of luteolin and apigenin concentrations on the composition of the solvent were established using a multilinear regression algorithm and were applied to screen 119 different solvents. After that, the extraction process was optimized using response surface methodology and an artificial neural network. Apigenin and luteolin were recovered from the extract by the combination of distillation and addition of water. RESULTS: The screening results on 119 solvents revealed that choline chloride-acetic acid (1:4) was the most suitable deep eutectic solvent. It was showed that both response surface methodology and the artificial neural network could accurately determine the optimal conditions of extraction of apigenin and luteolin from C. indicum L., including time of extraction (65 minutes), temperature of extraction (90°C) and water content (20%). By the combination of distillation and addition of water, apigenin and luteolin could be effectively recovered from the deep eutectic solvent extract with a recovery rate of over 80%. CONCLUSIONS: Deep eutectic solvent could be used as an effective green alternative to the conventional solvents for the extraction of bioactive compounds from plants.

In vitro screening of siderophore-producing rice root endophytic bacteria from up-land paddies in north-western Vietnam for plant growth-promoting activities

Aims@#Endophytic bacteria (EB) living inside plant tissues possess different beneficial traits including siderophore production and other plant growth-promoting (PGP) activities. Siderophore-producing EB promote host plant growth by secreting ferrum in iron-deficient conditions. This study screened 19 siderophore producers in vitro, isolated from upland rice roots grown in mountain farms of Tung Village, Nậm Có Commune, Mù Cang Chải District, Yên Bái Province, Vietnam, for PGP traits, including phosphate solubilisation, indole-3-acetic acid (IAA), ammonia, gelatinase, amylase and catalase production.@*Methodology and results@#The bacteria were identified by Matrix assisted Laser Desorption Ionization Time of Flight mass spectrometry (MALDI-TOF MS). All 19 isolates were identified as genera Pseudomonas, Enterobacter, Pantoe, Bacillus, Burkholderia, Staphylococcus, Ralstonia and Cronotacter. The isolates produced catalase and ammonia. The amount of ammonia ranged from 60.74 ± 0.14 to 466.72 ± 0.18 mg/L. Out of the 19 siderophore producers, 17 (89.47%) were able to solubilise phosphate with solubilisation index (PSI) ranging from 1.12 ± 0.07 to 2.14 ± 0.15. The qualitative assays identified 12 isolates (63.15%) positive for IAA production with a tryptophan concentration of 5 mM, whereas 15 (78.94%) and 17 (89.47%) isolates were positive for gelatin and starch hydrolysis, respectively. Especially, 7 isolates were found to be positive for all tested assays in vitro including Pseudomonas rhodesiae (NC2), Enterobacter asburiae (NC50), Pantoea ananatis (NC63), Bacillus cereus (NC64), Burkholderia cenocepacia (NC110), Staphylococcus sciuri (NC112) and Ralstonia pickettii (NC122).@*Conclusion, significance and impact of study@#This study serves as crucial findings of multi-trait plant growth-promoting endophytic bacteria isolated from upland rice root in north-western Vietnam. The seven potential isolates positive for all tested assays could be effective PGP bacteria for bio-inoculants.

A Variable-Length Chromosome Genetic Algorithm for Time-Based Sensor Network Schedule Optimization.

Scheduling sensor nodes has an important role in real monitoring applications using sensor networks, lowering the power consumption and maximizing the network lifetime, while maintaining the satisfaction to application requirements. Nevertheless, this problem is usually very complex and not easily resolved by analytical methods. In a different manner, genetic algorithms (GAs) are heuristic search strategies that help to find the exact or approximate global optimal solution efficiently with a stochastic approach. Genetic algorithms are advantageous for their robustness to discrete and noisy objective functions, as they are only evaluated at independent points without requirements of continuity or differentiability. However, as explained in this paper, a time-based sensor network schedule cannot be represented by a chromosome with fixed length that is required in traditional genetic algorithms. Therefore, an extended genetic algorithm is introduced with variable-length chromosome (VLC) along with mutation and crossover operations in order to address this problem. Simulation results show that, with help of carefully defined fitness functions, the proposed scheme is able to evolve the individuals in the population effectively and consistently from generation to generation towards optimal ones, and the obtained network schedules are better optimized in comparison with the result of algorithms employing a fixed-length chromosome.

Spectrum-effect relationship between high-performance thin-layer chromatography data and xanthine oxidase inhibitory activity of celery seed extract.

Celery seeds are medicinal herbs used for the prevention and treatment of gout as these have the ability to inhibit the activity of xanthine oxidase and reduce the concentration of serum uric acid. In this study, the relationship between xanthine oxidase inhibitory effects and high-performance thin-layer chromatography data of celery seed extracts was established using multilayer neural network (MNN) in combination with principal component analysis (PCA). The constructed MNN-PCA model was stable and had accurate prediction ability with coefficient of determination = 0.9998, leave-one-out coefficient = 0.7371, root mean square error = 0.0025, and mean absolute deviation = 0.0019 for the training set and coefficient of determination = 0.8124, root mean square error = 0.0784, and mean absolute deviation = 0.0645 for the test set. This model can be used to identify the main compounds related to the xanthine oxidase inhibitory effect of celery seed extract. These results can be applied not only to celery extract but also to other herbal medicines.

An exploration of the gut and environmental resistome in a community in northern Vietnam in relation to antibiotic use.

Background: Antibiotic resistance is a major global public health threat. Antibiotic use can directly impact the antibiotic resistant genes (ARGs) profile of the human intestinal microbiome and consequently the environment through shedding. Methods: We determined the resistome of human feces, animal stools, human food and environmental (rain, well, and irrigative water) samples (n = 304) in 40 households within a community cohort and related the data to antibiotic consumption. Metagenomic DNA was isolated and qPCR was used to determine presence of mobile colistin resistance (mcr) genes, genes encoding extended-spectrum ß-lactamases (ESBL), carbapenemases and quinolone resistance genes. Results: Nearly 40 % (39.5%, 120/304) of samples contained ESBL genes (most frequent were CTX-M-9 (23.7% [72/304]), CTX-M-1 (18.8% [57/304]). Quinolone resistance genes (qnrS) were detected in all human and 91% (41/45) of animal stool samples. Mcr-1 and mcr-3 were predominantly detected in human feces at 88% (82/93) and 55% (51/93) and animal feces at 93% (42/45) and 51% (23/45), respectively. Mcr-2, mrc-4 and mcr-5 were not detected in human feces, and only sporadically (< 6%) in other samples. Carbapenemase-encoding genes were most common in water (15% [14/91]) and cooked food (13% [10/75]) samples, while their prevalence in human and animal stools was lower at 4% in both human (4/93) and animal (2/45) samples. We did not find an association between recent antibiotic consumption and ARGs in human stools. Principal component analysis showed that the resistome differs between ecosystems with a strong separation of ARGs profiles of human and animal stools on the one hand versus cooked food and water samples on the other. Conclusions: Our study indicated that ARGs were abundant in human and animal stools in a rural Vietnamese community, including ARGs targeting last resort antibiotics. The resistomes of animal and human stools were similar as opposed to the resistomes from water and food sources. No association between antibiotic use and ARG profiles was found in a setting of high background rates of AMR.

Emergence of New Delhi metallo-beta-lactamase 1 and other carbapenemase-producing Acinetobacter calcoaceticus-baumannii complex among patients in hospitals in Ha Noi, Viet Nam.

Acinetobacter baumannii is an important cause of multidrug-resistant hospital acquired infections in the world. Here, we investigate the presence of NDM-1 and other carbapenemases among carbapenem-resistant A. baumannii isolated between August 2010 and December 2014 from three large hospitals in Hanoi, Vietnam. We identified 23/582 isolates (4 %) (11 from hospital A, five from hospital B, and seven from hospital C) that were NDM-1 positive, and among them 18 carried additional carbapenemase genes, including seven isolates carrying NDM-1, IMP-1, and OXA-58 with high MICs for carbapenems. Genotyping indicated that NDM-1 carrying A. baumannii have expanded clonally in these hospitals. Five new STs (ST1135, ST1136, ST1137, ST1138, and ST1139) were identified. One isolate carried NDM-1 on a plasmid belonging to the N-repA replicon type; no NDM-1-positive plasmids were identified in the other isolates. We have shown the extent of the carbapenem resistance and the local clonal spread of A. baumannii carrying NDM-1 in these hospitals; coexistence of NDM-1 and IMP-1 is reported for the first time from Vietnam here, and this will further seriously limit future therapeutic options.

A systematic review of modelling approaches in economic evaluations of health interventions for drug and alcohol problems.

BACKGROUND: The overarching goal of health policies is to maximize health and societal benefits. Economic evaluations can play a vital role in assessing whether or not such benefits occur. This paper reviews the application of modelling techniques in economic evaluations of drug and alcohol interventions with regard to (i) modelling paradigms themselves; (ii) perspectives of costs and benefits and (iii) time frame. METHODS: Papers that use modelling approaches for economic evaluations of drug and alcohol interventions were identified by carrying out searches of major databases. RESULTS: Thirty eight papers met the inclusion criteria. Overall, the cohort Markov models remain the most popular approach, followed by decision trees, Individual based model and System dynamics model (SD). Most of the papers adopted a long term time frame to reflect the long term costs and benefits of health interventions. However, it was fairly common among the reviewed papers to adopt a narrow perspective that only takes into account costs and benefits borne by the health care sector. CONCLUSIONS: This review paper informs policy makers about the availability of modelling techniques that can be used to enhance the quality of economic evaluations for drug and alcohol treatment interventions.

The National Perinatal Depression Initiative: An evaluation of access to general practitioners, psychologists and psychiatrists through the Medicare Benefits Schedule.

OBJECTIVE: To evaluate the impact of the National Perinatal Depression Initiative on access to Medicare services for women at risk of perinatal mental illness. METHOD: Retrospective cohort study using difference-in-difference analytical methods to quantify the impact of the National Perinatal Depression Initiative policies on Medicare Benefits Schedule mental health usage by Australian women giving birth between 2006 and 2010. A random sample of women of reproductive age enrolled in Medicare who had not given birth where used as controls. The main outcome measures were the proportions of women giving birth each month who accessed a Medicare Benefits Schedule mental health items during the perinatal period (pregnancy through to the end of the first postnatal year) before and after the introduction of the National Perinatal Depression Initiative. RESULTS: The proportion of women giving birth who accessed at least one mental health item during the perinatal period increased from 88 to 141 per 1000 between 2007 and 2010. The difference-in-difference analysis showed that while there was an overall increase in Medicare Benefits Schedule mental health item access as a result of the National Perinatal Depression Initiative, this did not reach statistical significance. However, the National Perinatal Depression Initiative was found to significantly increase access in subpopulations of women, particularly those aged under 25 and over 34 years living in major cities. CONCLUSION: In the 2 years following its introduction, the National Perinatal Depression Initiative was found to have increased access to Medicare funded mental health services in particular groups of women. However, an overall increase across all groups did not reach statistical significance. Further studies are needed to assess the impact of the National Perinatal Depression Initiative on women during childbearing years, including access to tertiary care, the cost-effectiveness of the initiative, and mental health outcomes. It is recommended that new mental health policy initiatives incorporate a planned strategic approach to evaluation, which includes sufficient follow-up to assess the impact of public health strategies.