Significant differences were observed in fasted blood glucose , diastolic pressure , and glycated hemoglobin  between Phase 1 and Phase 3. Conclusions: Education has a positive effect on adherence to recommended diet and glycemia regulation in diabetes patients after four-week follow-ups. However, after two years, participants showed a decrease in adherence to recommended diet and increased glycemia.Background: The aim of this study was to examine the effect of dietary education on glycemic control in patients with any type of diabetes at four-week and two-year follow-ups. Methods: A two-year prospective study was conducted in three phases: before, four weeks after, and two years after an educational program. The participants were patients diagnosed with diabetes who were receiving insulin or oral hypoglycemics and who attended the Diabetes Clinic of the General County Hospital Našice, Croatia to receive their treatment. The questionnaire and educational program were created for the purpose of this study. Measurements were made  at baseline, four weeks and two years after education. Results: A total of 109 participants took part in the study, of which 56 (51.4%) were on insulin therapy and 53 (48.6%) were on oral therapy, after two years, 78 (72%) subjects were followed up with. There was no control group. At the two-year follow-up, participants with insulin therapy had significantly higher glycated hemoglobin (Mann–Whitney U test,  Today, chronic non-communicable diseases are the leading cause of mortality and hospitalization and represent a major public health problem . The numBest management practices encourage patients to measure their blood glucose, take medications, and adjust their dietary habits to maintain health with the disease . BecauseThe overall goal of diabetes education is to help individuals and their families gain the necessary knowledge, life skills, resources, and support needed to achieve optimal health . When plProspective research regarding “knowledge questionnaire” was conducted in order to validate it. Research was conducted in three phases: Phase 1 from 10 June 2014 to 14 August 2014, Phase 2 which was conducted four weeks after the phase 1 depending on the beginning of the Phase 1, and Phase 3 which was conducted two years after the Phase 2, from 5 August 2016 to 13 October 2016. The educational program was administered by a team of diabetes specialist nurses, internal specialists, and diabetes experts. The education was conducted individually and in small groups. The group received the required education within five two-hour sessions in groups of 3–4 individuals in the period of one week and one session individually. Before the education program, participants completed a questionnaire; and fasting blood glucose, glycated hemoglobin, body mass index, blood pressure measurements were performed in a fasted state. The results obtained from these measurements were the basis for the development of the educational program. The education was individualized based on the questionnaire results , but it was similar to all of the participants. Four weeks after the education (Phase 2), subjects completed the knowledge test again using the same questionnaire, and measurements were repeated. Two years after the education, subjects were contacted by telephone to attend a follow-up meeting where they completed the questionnaire a third time, and the measurements were repeated (Phase 3).The study was conducted at the Diabetes Clinic of the General County Hospital Našice, which is a general hospital in Osijek-Baranja County, Croatia. The population gravitating to our hospital is 16,000, primarily rural.The inclusion criteria were male or female patients with any type of diabetes who were receiving insulin, oral hypoglycemics, or combination therapy of insulin and oral hypoglycemics, having at least one treatment prior to the study, with an active record in the diabetes clinic and informed consent to participate in the research. The exclusion criteria were patients under 18 and/or over 75 years of age, cognitive and/or mental diseases, illiteracy, patients with life expectancy of less than 2 years, and the patients who did not participate in educational sessions (less than 60% of sessions).As a study tool, a questionnaire was created for the purpose of this study according to the literature on educating and training diabetic patients ,14. The Diabetes education was based on the pre-test of diabetic patients and literature on educating and training of diabetic patients and teaching about the illness, possible complications, diabetes diet, taking medications, physical activity, risk factors and their effect on improving treatment outcomes, quality of life and self-management ,14. TeacThe content and plan used in five two-hour educational sessions included: (1) Patho-physiological view: diabetes definition, range of blood glucose levels, physical activity, and stress. (2) Diet and food: personalized nutritional advice was given based on preferences, but the overall aim was to reduce energy, portions, carbohydrates, fats, salts, and also education aimed at identifying healthy food and amount of energy per gram of carbohydrates, fats, proteins, and quantitative features of recommended diet. (3) Treatment: insulin and oral hypoglycemics administration. (4) Measurements: Level of blood glucose monitoring. (5) Information of acute complications: prevention and treatment of hypoglycemia and hyperglycemia. (6) Information about chronic complications: prevention of late stage chronic complications, prevention of cardiovascular disease and events , burden of overweight, the need of smoking cessation, hypertension, and dyslipidemia. (7) Practical tips: source and storage of insulin, monitoring of glucose supplies, diabetes nurse specialist telephone number. Within each session, the participants were offered lunch and participated in practical work by calculating food energy value and identifying recommended food. Recommended diet is suitable for the whole family, the main defining points are low energy, portion, carbohydrate, fat, and salt. Men do not usually buy food or prepare meals, but their wives have not been involved to the education sessions. Participants were provided with written educational content. .N = 56), and oral hypoglycemic (N = 53). In the third phase 3/4 patients participated, a total of 78 (72%) (two years after the education program), insulin therapy, and combination insulin and oral hypoglycemic (N = 38), and oral hypoglycemic (N = 40). Of original subjects, 11 (10%) died, 7 (6.4%) changed their place of residence, 7 (6.4%) declined to participate in the follow-up, and 6 (5.6%) could not be contacted. Some subjects 4 (3.6%) who previously used oral hypoglycemics were transferred to insulin therapy, their medication had changed since their initial involvement in the study. The study was conducted in accordance with the ethical principles and human rights standards of medical study. For the purpose of conducting the investigation, the consent of the Commission for Ethical Affairs of the County General Hospital Našice was obtained (Order No. 01-421/1-2014).In total, 109 patients participated in the first two phases (before and four weeks after the education program), insulin therapy, and combination insulin and oral hypoglycemic . Considering that the study had a two-year follow-up, the high mortality rate in diabetic patients [p = 0.046). The mean duration of the disease was 8 years  and was significantly shorter in the oral therapy group . A majority of participants had completed secondary education qualifications   subjects were followed up with. The mean age of participants was significantly higher in subjects on oral therapy  .p = 0.001), diastolic pressure , and glycated hemoglobin (HbA1c)  between Phase 1 and Phase 3. Regarding systolic pressure and the body mass index, there were no significant differences.Significant differences were observed in fasted blood glucose   .p = 0.048)  patients using insulin always followed dietary guidelines and 11 (27.5%) patients taking medications always followed dietary guidelines  .There was a significant difference in the knowledge on foods that increase blood glucose before and after education for patients on insulin and patients on oral hypoglycemics therapy. At two-year follow-up, a significant difference was present only in patients using insulin .This study reports on a hospital outpatient-based education program designed to address gaps in knowledge in people with diabetes. Patients were grouped and compared for baseline biographical characteristics depending on whether they were prescribed oral hyperglycemic medications or insulin. Patient completed questionnaires on knowledge and self-reported behaviors before the education sessions. Pre- and post-knowledge, and adherence to a recommended diet comparison were made at 4 weeks and 2 years. Significant improvements were noted for HbA1c, fasting serum glucose, for all patients. However, a significant increase in diastolic blood pressure was found for patients who had prescribed insulin therapy which may be related to their non-significant increase in body mass index (BMI). In addition, significant improvements were found in the proportion of patients who adhered to the recommended diet and for knowledge of food that increases serum glucose levels. However, after two years, participants showed a decrease in adherence to recommended diet and increased glycemia.Several studies have demonstrated how diet education improved patients’ knowledge and helped them adopt healthier behaviors ,21,22,23To test effectiveness of the systematic health education model for type 2 diabetes mellitus (T2DM), a study was conducted in China. The objectives were accomplished through systematic health education in a group provided by health educators every month for over 2 years. The researchers concluded that the systematic health education model is a useful method in the treatment of DM because it contributes to a decrease in HbA1c , and BMI did not change . In thisThe study carried out in Spain showed a reduction in HbA1c of −0.03 after a 2-year follow-up period in interventional group and nonsignificant increase in control group 0.04 . The twoRegarding systolic pressure, there were no significant differences in this study. The study in China showed a decrease of systolic pressure (10.83 mmHg) , while tNurses play an important role in pharmacological and non-pharmacological treatment of diabetic patients . Apart fPrimary limitation of this study is that we did not include the control group. One of the limitations of this study was that its findings could not be generalized to the entire Croatian diabetic population, because it was conducted in one rural geographical area. We recommend that a randomized controlled trial should be carried out to test this intervention in Croatia for patients attending outpatient care. Our findings must also be considered in the context of several limitations, including certain losses, 31 (28%) participants, high mortality, as well as the small sample of participants. Of original participants, 11 (10%) died, , 7 (6.4%) changed their place of residence (the older population moved in with their children in town), 7 (6.4%) declined to participate in the follow-up, and 6 (5.6%) could not be contacted mostly because of the rural area. Some subjects, 4 (3.6%) who previously used oral hypoglycemics, were transferred to insulin therapy; they had increased glycemia and their medication had changed since their initial involvement in the study and were transferred into another group on insulin therapy and their results of increased glycemia could have influenced the final group results.In conclusion, significant improvements were noted for HbA1c, fasting serum glucose, for all patients, and education had a positive effect on glycemic control and adherence to the recommended diet. The results of this study show that, as time passed, two years after the education most participants forgot what they had learned and returned to their usual habits. However, at two-year follow-up, participants showed an increase in glycemia. We think it is necessary to conduct education continuously every six months to once a year, which is consistent with results of the study carried out in Brazil , and str


In this work, the self-assembled morphologies obtained for poly(styrene-b-butadiene-b-styrene) (SBS) and poly(styrene-b-isoprene-b-styrene) (SIS) ABA-type copolymers were investigated before and after hydrogenation of the polydiene block, which led to poly(styrene-b-ethylene)/poly(ethylene-b-styrene) (SEES) and poly(styrene-b-ethylene)/poly(propylene-b-styrene) (SEPS) copolymers, respectively. The evaluation of different morphologies was carried out using atomic force microscopy (AFM), analyzing the effect of various parameters such as the solvent and polymer concentrations employed for film casting , together with that of the annealing treatment . The effect of these parameters in combination with the chemical nature of the polydiene block led to different morphologies with different topographic aspects affecting the roughness (Ra) of the film. As several new applications emerge in the field of materials science, especially at the nanoscale level, new nanomaterials with improved properties are needed. Block copolymers are potentially suitable for this purpose due to their capability to self-assemble into ordered nanostructures. Currently, the development of new monomers and polymerization techniques can lead to various copolymers with interesting properties that can be controlled from their ordered nanostructures ,2,3,4,5.Immiscibility between covalently bonded blocks makes block copolymers form various nanostructures such as lamellae, spheres, cylinders, and double-gyroid ,7,8, amoBlock copolymers display many potential applications in nanotechnology ,14,15, lThe casting of block-copolymer thin films from solutions onto different substrates is one of the ways of obtaining oriented nanostructures. This procedure involves the study and control of many parameters in order to obtain the desired nanodomains. The nature of the substrate, the polymer deposition method, solvent selection, and solvent removal method will determine the obtained morphology . The choAs mentioned above, the affinity of different solvents for copolymer blocks can lead to different nanostructures and organization. Through thermodynamics, the affinity of one of the blocks for the solvent is the driving force for the formation of ordered nanostructures. The evaporation rate of the solvent for a drop-casted block-copolymer thin film can also determine the nanostructure, as, depending on the solubility of each block, one of the blocks can rearrange and move toward the surface, relative to the other block. Thermal annealing is also very important, as it can provide mobility to the polymeric chains for their ordering.d), the dipole contribution (δp), and the hydrogen-bond contribution (δh) to the solubility parameter. These parameters, combined with Equation (1),sp) parameter can be calculated. The lower the χsp parameter, the higher the affinity of the polymer for the solvent [The solubility, and subsequently, the affinity of a solvent for a polymer involves three important parameters. These are dispersion contribution  (SBS) and poly(styrene-b-isoprene-b-styrene) (SIS) are polymers that are widely used in various applications, such as in modified asphalt for studying the physical and rheological properties of asphalt during aging (SBS as the main component) , or to sThe aim of this manuscript was to investigate the effect of sample preparation  on the morphologies and surface topography of specific poly(styrene-b-isoprene-b-styrene) (SIS) and poly(styrene-b-butadiene-b-styrene) (SBS) ABA-type copolymers and their hydrogenated poly(styrene-b-ethylene)/poly(propylene-b-styrene) (SEPS) and poly(styrene-b-ethylene)/poly(ethylene-b-styrene) (SEES) analogs. Thin films of all copolymers were casted from their toluene, cyclohexane, and tetrahydrofuran solutions with concentrations of 1 or 3 wt% onto glass wafers. Samples were annealed at different temperatures . Different morphologies were obtained depending on the solvent, concentration, and annealing temperature, revealing the importance of the control of such parameters on the formation of desired nanostructured domains. These copolymers can show interesting nanostructures that can be controlled by changing different parameters. They can be used in nanopattering, as membranes or as points where molecules/particles can be introduced or trapped. It is very important to notice that the use of these triblock copolymers with polydiene as one of the blocks can lead to polymeric materials with different properties such as thermal, viscoelastic, and degradation stability. Finally, the easy modification of these commercial copolymers (due to the double bond of the polydiene) can lead to new materials that can be adjusted to the need of the market.−1  and a weight fraction, Φps = 0.3. The SBS triblock copolymer was kindly supplied by Repsol-YPF  with a total molecular weight of 75,000 g mol−1  and a weight fraction, Φps = 0.4. The hydrogenation procedure for obtaining SEPS and SEES was conducted with p-toluenesulfonyl hydrazide , 97%) and it is described elsewhere [SIS and SBS copolymers were used as received. The SIS triblock copolymer was obtained from Kraton Polymers with a total molecular weight of 58,200 g molCopolymer solutions (1 and 3 wt%) in toluene, THF, and cyclohexane were left stirring for 24 h at room temperature. Then, solutions were drop-casted onto glass wafers for 24 h in order to ensure the full evaporation of the solvent. Films were then annealed in a high-vacuum oven  for 24 h at different temperatures: room temperature, and 60, 80 or 100 °C.The surface morphology of the thin films was characterized using atomic force microscopy  . The tapping mode in air was employed using an integrated silicon tip/cantilever (125 μm in length) at a scan rate of 1.0 Hz and a resonant frequency of ~300 kHz ,62,63.The change of solvent can lead to different affinities for the blocks, and this can lead to different ordered nanostructures. It is very important to know the solubility parameters between the polymeric blocks and the solvents, so as to explain some nanostructures. It must be pointed out that, for copolymers used in this work, the middle block was a polydiene (PB or PI), and following hydrogenation, a polyolefin (polyethylene (PE) or PE/polypropylene (PP)); as such, their glass transition temperatures were lower than room temperature.sp parameters for the polymer/solvent systems based on PS, PB, PI, PE, and PEP polymers, and toluene, cyclohexane, and THF solvents, as calculated using Equation (2) [In tion (2) . Based otion (2) ,66.From Once the selectivity of the solvents for the blocks was established, ordering occurred depending on various parameters such as the annealing temperature, and the concentrations of the copolymer solution and solvent. The effects of those parameters on the obtained morphologies were subsequently analyzed. The obtained structures are gathered in It is clear that the morphologies were greatly affected by the chemical nature of the copolymers and adjustable parameters such as the solvent or annealing treatment. During solvent evaporation, blocks with affinity for the solvent tend to orient themselves toward the surface. Annealing treatment can also help some blocks move toward the surface or to better organize themselves due to the increased mobility. Generally, the middle block (polydiene and polyolefine) presented a higher affinity for toluene and cyclohexane, while the end block (PS) had a higher affinity for THF. When toluene and cyclohexane solutions were casted onto glass wafers, the middle block moved toward the outermost surface due to its affinity for the solvent and the surface tension. Copolymer domains with lower surface tension (PI and PB) present this behavior in order to minimize surface energy ,68,69. Icis/trans 1,4-, 3,4-, and 1,2-) and a small percentage of an SIS copolymer.In the case of the SEPS copolymer, highly ordered worm-like morphologies were observed, especially for thinner films obtained from 1 wt% solutions. Increasing the film thickness (3 wt% solution), a worm-like morphology was obtained, but with the presence of cylinders, oriented perpendicularly to the surface. The SIS copolymer films prepared using 3 wt% solutions showed some perpendicular cylinders together with crystal-like large domains, cylinders with a parallel orientation, or cylinders with both perpendicular and parallel orientations. For thinner films (1 wt% solutions), worm-like morphologies with some cylinders with different ordering and orientation were obtained. For SEES, a worm-like ordered morphology was observed only for samples annealed at 100 °C (for both 1 and 3 wt% solutions). For SBS, a mixture of morphologies was observed: worm-like , parallel cylinders (1 wt%), and perpendicular disordered cylinders (1 and 3 wt%). As a general observation for systems with polydiene as the middle block, SIS and SBS generally presents both types of cylinder morphologies  . In our Starting with samples annealed at RT, SEPS showed a worm-like morphology with perpendicular cylinders (3 wt%) and a mixture of parallel/perpendicular cylinders (1 wt%). Its precursor (SIS) showed parallel cylinders at 3 wt% and a mixture of worm-like morphology, parallel cylinders, and parallel/perpendicular cylinders at 1 wt%. SEES showed a disordered morphology, while its SBS precursor showed three different morphologies. For samples prepared with 1 wt% solutions, a disordered morphology was found for THF, a worm-like morphology was found for cyclohexane, and a worm-like morphology with perpendicular cylinders was found for toluene. For samples with 3 wt% solutions, a disordered morphology was obtained for THF, while parallel cylinders were obtained for cyclohexane and perpendicular cylinders were obtained for toluene.Regarding the samples annealed at 60 °C, SEPS showed some crystalline PEP domains combined with a worm-like morphology for 1 wt% solutions. For samples with 3 wt% solutions, the same morphology as that seen at RT was obtained (worm-like with perpendicular cylinders). For SIS (compared to morphologies obtained at RT), the ordering was faint, showing disordered and worm-like morphologies and perpendicular cylinders with the presence of crystal-like domains at 1 wt%, and perpendicular cylinders with crystal-like domains at 3 wt%. SEES and SBS showed disordered morphologies.For samples annealed at 80 °C, SIS SEES, and SBS showed generally disordered structures. SEPS showed a worm-like morphology at 1 wt%, while, at 3 wt%, a worm-like morphology was generated for THF, a worm-like morphology with perpendicular cylinders was generated for cyclohexane, and perpendicular/parallel cylinders were generated for toluene. The influence of solvent evaporation rate was clearly shown in the final ordering. Solvents such as THF which evaporate quickly do not give an appropriate time for relaxation of the polymeric chains, so as to achieve thermodynamically stable nanostructures.Finally, in the case of the samples annealed at 100 °C, SEPS showed only a worm-like morphology, probably due to the migration of PS to the outermost surface due to the energy obtained from the thermal treatment. SEES showed some nanostructures, such as a worm-like morphology at 1 and 3 wt% solutions in THF and cyclohexane. For the remainder, disordered structures were observed, probably due to the increased mobility of the PS chains.In the case of cyclohexane, a worm-like morphology only or that with perpendicular cylinders was observed for SEPS at 1 and 3 wt% solutions, respectively. The remainder of the samples showed disordered morphologies with the same solvent. Similar results were found for toluene, whereby SEPS showed a worm-like morphology with crystals at 1 wt%, and a worm-like morphology with perpendicular cylinders at 3 wt%. The remainder of the samples showed disordered morphologies. Finally, for THF, SEPS adopted a worm-like morphology at both 1 and 3 wt% solutions, while SIS and SEES (3 wt%) showed perpendicular cylinders and a worm-like morphology, respectively.Generally, a worm-like morphology was favored for THF, while a cylindrical one was favored for toluene and cyclohexane. THF favored a worm-like morphology and perpendicular cylinders with large domains, while toluene favored a worm-like morphology with crystals, disordered perpendicular cylinders, and parallel/perpendicular cylinders. Finally, cyclohexane favored a worm-like morphology with perpendicular cylinders and perpendicular cylinders with large domains. In the case of THF, with the highest evaporation rate in combination with chain mobility after annealing, blocks did not have enough time for the generation of ordered morphologies, obtaining a worm-like morphology or a mixture of morphologies. Even if worm-like morphologies were observed for all solvents, the majority (12) were obtained for THF, and 10 for cyclohexane and toluene . The opposite effect could be observed for the ordered morphologies . Cyclohexane presented eight cases , and toluene presented seven cases , while only three cases were obtained with THF (for SIS).Some general considerations could be derived after analyzing the effect of different parameters, where the highest order was shown in the SEPS morphologies. Temperature also played a significant role, obtaining the highest ordered morphologies at RT. For samples with a PEP middle block, crystals were formed at 60 °C and at 80 °C. At higher temperatures, the mobility of the PS end blocks, due to the thermal energy, led to worm-like ordered domains. Even though the middle block was generally present at the outermost surface (for cyclohexane and toluene), the temperature made it possible for PS chains to migrate to the outermost surface, leading to worm-like morphologies. For samples casted from cyclohexane and toluene, the middle block was expected to be at the outermost surface mixed with PS for higher annealing temperatures, due to its increased mobility. For both solvents, SEPS showed quite a similar morphology, with differences in ordering, orientation, and the presence or absence of crystals. The affinity between blocks and solvent also played an important role. For toluene, the affinity for PS and PEP was similar , while, for cyclohexane, the affinity for PS was 0.360, and, for PEP, it was 0.160. Thus, the evaporation rate of cyclohexane from the middle block was slower when compared to that of PS. Therefore, the middle block had more chance to relax and assume a stretch configuration, while the PS blocks tended to aggregate so as to minimize contact ,48. ThisIn order to analyze, in more detail, the effect of temperature, solvent, concentration, and nature of blocks on sample morphology and topography, a series of identical samples prepared under different conditions were evaluated. Starting with the effect of solvent and annealing temperature, two series obtained from an SEPS sample with THF and toluene are presented at different temperatures in Regarding SEPS samples casted from toluene, The evolution of a worm-like morphology can be seen, together with the appearance of crystals 100 nm in size and in a polygonal form at 60 °C, which grew at 80 °C (125 nm). Due to the lower evaporation rate of toluene, samples had a longer time for ordering ; thus, eMoreover, a detailed analysis of the film roughness for all samples is presented in In order to obtain some basic conclusions concerning the effect of solution concentration, the nature of the solvent, and the chemical nature of the middle block on the morphology and topography, In order to analyze the effect of solution concentration, morphologies obtained for the SIS copolymer casted from toluene solutions of 1 and 3 wt%, annealed at RT, are shown as an example a,b. Filmg), and, for the case of cyclohexane, the outermost surface is mainly PI . The affinity of toluene is higher for PI, while that of THF is higher for PS. Therefore, a mixture of blocks in the outermost surface can be expected. The much slower evaporation rate of toluene creates almost 100% parallel cylinders , 38 nm (THF), and 32 nm (toluene), in agreement with the affinity for the solvents. Similarly, sizes of 22 nm (cyclohexane), 23 nm (THF), and 36 nm  were obtained for PEP. At 3 wt% concentration, the results were similar those mentioned above for the worm-like morphology. For more organized morphologies and with an increase in temperature , sizes seem to be bigger, around 22 nm for PS at RT, increasing to 30 nm at 60 °C, while, for PEP, a size of around 10 nm was found at RT, increasing to 28 nm at 60 °C, all in cyclohexane. Similarly, for the case of toluene, sizes were bigger up to 60 °C; however, at 80 °C, they become smaller, probably due to the nucleation of PEP crystals of around 104 nm at 60 °C and 124 nm at 80 °C. Also, compared to crystals obtained in cyclohexane (64 nm at 60 °C), it is clear that the affinity of PEP for toluene was a crucial factor for the enhanced size of the crystals. For the case of cyclohexane with 3 wt%, the size of PEP domains decreased with temperature (20 to 19 to 17 to 12 nm), while that of PS domains showed an irregularity (25 to 23 to 24 to 29 nm), obtaining the highest size after annealing at 100 °C.Finally, for SIS, it should be noted that the worm-like morphology for samples of toluene with 1 wt% produced sizes of 28 nm 22 nm for PS and PI, respectively. It seems that a polydispersity of sizes existed and it was affected by temperature, solution concentration, and solvent. Both PS and PI showed a better affinity for toluene than cyclohexane, resulting in large crystal-like domains for both polymers , as obtained in films casted from 1 wt% solutions of both solvents, annealed at RT.From the results, useful conclusions concerning the effect of solvent nature, solution concentration, and annealing temperature on the ordering of ABA-type SIS and SBS copolymers and their hydrogenated SEPS and SEES analogs could be derived.Worm-like and cylindrical morphologies were the most common morphologies observed with some differences, such as the appearance of crystals, a mixture of parallel and perpendicular cylinders, and a worm-like morphology with cylinders. Depending on the nature of the block and the solvent, a different degree of ordering and orientation could be observed. Regarding the annealing temperature, at RT, almost all types of morphologies were observed. For intermediate annealing temperatures (60 and 80 °C), intermediate structures and the appearance of crystal-like domains were observed, while, for samples annealed at 100 °C, disordered and worm-like nanostructures were observed.The affinity of the solvent for each block, in combination with the evaporation rate, was a key factor for the final obtained nanostructure. THF had an affinity for PS, while toluene/cyclohexane had an affinity for polydiene, leading to the formation of worm-like morphologies (THF) and cylindrical ones (toluene/cyclohexane). The most ordered nanostructures were observed for SEPS, indicating that immiscibility among blocks was also an important parameter.The affinity of the solvent for each block offered an extra volume to that block, while the remainder tended to segregate in order to avoid the solvent. Therefore, blocks were organized onto the glass substrate in different ways depending on the solvent and temperature.In conclusion, the ordering of block copolymers into different nanostructures is a multi-faceted procedure, in which, in addition to the nature of the block, other parameters of film preparation play a significant role: (i) solvent (polarity and evaporation rate), (ii) temperature (thermodynamic equilibrium), and (iii) polymer concentration. All these parameters can be varied to obtain desired ordered nanostructures with different orientations and degrees of ordering.


Studies on sex-specific socioeconomic gradients in objectively evaluated diabetes among older adults are scarce.We used cross-sectional data of 9,893 adults aged 65 years and older in Aichi Prefecture without long-term care insurance from the Japan Gerontological Evaluation Study (JAGES) in 2010 (Response rate: 66.3%). We collected demographic, socioeconomic  and behavioral information using a mail-in self-reported survey. Blood samples for the objectively evaluated diabetes and self-reported medical history were collected at annual municipal health checkups. Poisson regression analysis stratified by sex with multiple imputations was conducted to calculate prevalence ratio and 95% confidence interval.A clear income gradient in diabetes prevalence was observed among women, from 11.7% in the lowest income quartile (Q1) to 7.8% in the highest (Q4). Among men, the findings were 17.6% in Q1 to 15.1% in Q4. The prevalence ratios for diabetes with incomes Q1 to Q4 were 1.43  for women and 1.16  for men after adjusting for age and other socioeconomic factors. Even after adjusting for marital status, body mass index, other metabolic risk factors, and lifestyle factors, the income-based gradient remained among women. Education and occupation were not significantly associated with diabetes in the study population.Only women showed an income-based gradient in diabetes. Monitoring income gradient in diabetes is important in public health actions, even in older populations. Future longitudinal and intervention studies should evaluate the causal link of income to diabetes onset, determine the mechanisms of the potential sex differences in the income/diabetes association, and identify ways to mitigate the income-based inequality. During the past 4 decades, the global prevalence of adult diabetes has nearly doubled, increasing from 4.7% to 8.5%.3–11 and some Asian countries, including South Korea,12 China,13,14 Taiwan,15 and Japan.16,17 Except for one study in China,14 inverse relationships between socioeconomic status (SES) and diabetes prevalence or incidence have been observed according to occupational class,3,18,19 income level,10,18,20 and educational attainment.3,10,18–22 However, few studies have investigated the social gradient in diabetes among older adults, and the findings of studies using data among older populations have been inconsistent with respect to the association between SES and diabetes. For example, a Finnish study has shown that the low-income group had a higher diabetes prevalence compared with the high-income group among women, whereas among men the high-income group was more likely to have diabetes.6 Studies in Germany23 and the United Kingdom5 have shown that neither income, education, nor occupation was associated with the incidence of diabetes.Socioeconomic disparities in diabetes prevalence and incidence have been well documented in Western countries3,6,18–20,22 In Asian countries, only Lee et al in South Korea investigated sex differences in the association between SES and diabetes, similarly showing a stronger SES-diabetes association among women than men.21 No evidence of sex differences in the association between SES and diabetes has been reported from other parts of Asia, including Japan.To date, many studies have suggested sex differences in the association between SES and diabetes. Most studies have reported more obvious social gradients in diabetes among women than men in Western countries.N = 7,432), are not large , which may be a limitation in detecting the between-group gaps. Moreover, in the recent studies, the definitions of diabetes vary. For example, diabetes was defined using self-reports,6 first diabetes medication prescribed,5 and oral glucose tolerance tests.23 Specifically, self-report of having diabetes could induce reporting bias. The validity study by Goto et al found that positive predictive value of self-report diabetes was 75.7%, whereas negative predictive value was 96.5% in the Japanese population.24 The bias may go toward null on the association between SES and diabetes prevalence, given that health-conscious people with high health literacy recognize their health status more accurately. According to a Japanese nationally representative survey, health-conscious people are likely to be more educated.25 To our knowledge, no studies have investigated the association between objectively diagnosed diabetes and SES among the older population.These studies, other than the study in the United Kingdom  whether there is an association between SES and diabetes prevalence among Japanese older adults, and (2) whether there is a sex difference in this association, using large-scale cross-sectional data with objectively measured biomarkers of diabetes.26We used cross-sectional data of the 2010 wave of the Japan Gerontological Evaluation Study (JAGES). In 2010, in JAGES we sent the questionnaires to 169,215 community-dwelling individuals over 65 years without long-term care insurance. From 31 municipalities in 12 out of 47 prefectures throughout Japan, participants were randomly selected from the public residence registries in 15 large municipalities; in the 16 smaller municipalities, all eligible residents got the mail-in survey. In total 112,123 subjects answered the questionnaire (response rate: 66.3%). After excluding the individuals with missing in demographic characteristics, 102,869 subjects were valid for analysis. Since JAGES exclude long-term care insurance takers from the study participants, we cannot compare the characteristics of the participants with the national census directly. However, the sex ratio of the total JAGES 2010 individuals was mostly comparable to that of the national census . JAGES female population was younger than that of the national census , whereas the age structure of the male population was mostly identical.N = 306) or SES variables  , a total 6,813  participants were eligible for the analysis. We applied multiple imputation methods for the individuals having one or more missing data. Thus, the final study sample was 9,893 . Approvals were received by the Ethics Committee in Research of Human Subjects at Nihon Fukushi University for the JAGES protocol (No. 10-05) and by the Ethics Committee of Chiba University, Faculty of Medicine for the use of the data (No. 1777).In addition to these data, we obtained data of 9,893 JAGES participants with results of annual health checkups from five municipalities in Aichi Prefecture that participated in JAGES. After excluding participants with data missing for HbA1c, fasting blood glucose, casual blood glucose, or information of medication , and high-density lipoprotein (HDL) cholesterol. The HbA1c ratios were reported as the Japan Diabetes Society (JDS) values, then calculated for the values of the National Glycohemoglobin Standardization Program (NGSP) following a conversion formula.28 we defined diabetes mellitus as having HbA1c of over 6.5% based on the NGSP and fasting blood sugar ≥126 mg/dL (≥7.0 mmol/L) and/or casual blood sugar ≥200 mg/dL (≥11.1 mmol/L). People regularly taking hypoglycemic agents or insulin were also considered to have diabetes. We used the following criteria of the Japanese Society of Internal Medicine29 to define other metabolic risks: hypertriglyceridemia (TG ≥150 mg/dL), low HDL cholesterol (HDL <40 mg/dL), or taking appropriate medication and hypertension (systolic blood pressure ≥140 mm Hg and/or diastolic blood pressure ≥90 mm Hg or taking antihypertensive drugs).Based on the report from the Committee of the JDS on the Diagnostic Criteria of Diabetes Mellitus,30Information of participants’ annual household income, educational attainment, and longest occupation were collected using JAGES questionnaires. To adjust for differences in household size, we equivalized household income to per person in a household, dividing annual household income by the square root of the number of individuals per household. Because the income level of the participants was slightly higher than that of the entire JAGES population in 2010 , to applWe used categorical 5-year age groups, marital status, body mass index (BMI), other metabolic risks defined above, current smoking, current alcohol intake, physical activities, and dietary intake habits as covariates; these factors could be mediators of the association between SES and diabetes. Marital status was categorized as married, widowed, separated/unmarried, and other. BMI was classified into four groups: BMI <18.5, 18.5–24.9, 25.0–29.9, and ≥30.0. Other lifestyle factors included smoking status (nonsmoker or current smoker/ex-smoker), alcohol intake (nondrinker or drinker/ex-drinker), and walking time per day as physical activity (<30 min or ≥30 min). As for dietary intake habits, we included frequencies of the consumption of meat or fish (<1 servings/day or ≥1 servings/day) and fruit or vegetables (<1 servings/day or ≥1 servings/day).31,32 Under the chained equation method, we performed multinomial logistic regression for the categorical variables and ordinal logistic regression for the ordinal variables. We treated occupation and marital status as nominal variables and categorized diabetes, income, education, BMI, hypertension, triglyceridemia, smoking habit, alcohol intake, walking duration per day, and eating habit as ordinal variables, including dichotomous variables. Model 1 was adjusted for age and each SES indicators  separately. Model 2 was adjusted for age and all SES indicators. Model 3 was additionally adjusted for marital status, BMI, hypertension, low HDL, high TG, smoking status, alcohol intake, walking time per day, meat/fish intake, and fruit/vegetable intake.First, we calculated the prevalence of diabetes by the levels of socioeconomic indicators. Chi-squared test for sex was performed both in Table P-value for the interaction term between income and sex = 0.18, P-value for the interaction term between education and sex = 0.20). However, as Hawkes et al mentioned,33 irrespective of the statistical significance, the gender-stratified analysis is essential to address the determinants of ill health by gender. Accordingly, we decided to analyze the data stratified by sex. Also, to investigate the validity of our missing-at-random assumption for multiple imputations, we conducted a sensitivity analysis using the complete case dataset ; among women, prevalence values were 11.7% in Q1, 11.6% in Q2, 9.6% in Q3, and 7.8% in Q4 (P = 0.03) , 13.5% in Q2, 15.7% in Q3, and 15.1% in Q4 (highest income) , PRs of diabetes for Q1, Q2, and Q3 were 1.43 , 1.33 , and 1.22   the clear income gradient in diabetes prevalence was only observed among women but not among men; and 2) among men and women, there was no clear gradient in diabetes prevalence by years of education and longest occupation.12,21 Robbins et al have proposed, as potential reasons, that women culturally have difficulties in health care access than men, fewer opportunities for regular exercise, unhealthy lifestyle behaviors, disadvantaged nutritional factors, more psychological stress, more depression, and more negative pre- or peri-natal environmental factors.18 Other scholars have suggested the different roles of obesity in the association between income and diabetes by sex. In a Swedish study, Agardh et al found that among the low-income group, BMI explained their excess risk for subjectively diagnosed type 2 diabetes by 21% among men and 35% among women.3 Nonetheless, a study from Canada that investigated the association between self-reported diabetes and SES found that BMI did not explain the associations between income and diabetes both among men and women.20 In the present study, further adjustment for covariates, including BMI, did not substantially alter the association between income and diabetes for both sexes. To clarify the reasons for the sex difference, further studies are needed.The socioeconomic gradient was potentially more marked among women, which was consistent with recent studies in other countries.34 Second, the access limitation also leads to the social isolation and exclusion because of the lack of opportunities for social interactions, leading to mental stresses. Stress science and endocrinological studies have suggested the direct effects of stress hormones on blood glucose levels and insulin intolerance, as well as health behaviors.34 Potential gender differences in our result could be explained by the psychosocial functions of income, including health beliefs, attitudes, and lifestyles, which may differ between men and women even at the same income levels.21 Specifically, as suggested by Saito et al, the loss of social interactions due to the lack of income might affect women more than men among Japanese older adults.35 Lastly, although the detailed mechanisms are unknown, sex differences in the gene-related tolerance for diabetes may also explain the stronger association among women found in our study.36,37We found a gradient in diabetes by income but not by education or occupation. In theory, income has both materialistic and psychosocial functions, and they may explain the income gradient in diabetes distinctively. First, low income means limited access to material goods and services to prevent diabetes, such as balanced diet and necessary preventive care.18 but consistent with results from older populations.6 Socioeconomic status in older people should be interpreted differently from that at younger ages.38 In many countries, older people are likely to have lower educational attainment. Among our study participants, the percentage of people with university or higher level educations was small in the age group investigated: 18.3% for men and 10.9% for women , which is in line with known occupation-based health disparities around the world . Second, generalizability is limited, as our data were obtained only from regions of central Japan and the study does not include older people with long-term care insurance. Third, selection bias should also be discussed. Our study participants were more health conscious than the general population, as participants were limited to those who underwent health checkups. In Japan, about 38% of the population received health checkups in 2010.In conclusion, we found a clear income-based gradient in diabetes among Japanese older adults and the gradient was potentially more remarkable among women, but this was not the case for education and longest occupation. This was the first large-scale study clarifying the socioeconomic disparity in diabetes among Japanese older population. Given the findings of this study, monitoring income gradient in diabetes is important in public health actions, even in older populations. Future longitudinal and intervention studies should evaluate the causal link of income to diabetes onset, determine the mechanisms of the potential sex differences in the income/diabetes association, and identify ways to mitigate the income-based inequality.


The differential diagnosis of tuberculous pleural effusion (TPE) is challenging. In recent years, artificial intelligence (AI) machine learning algorithms have started being used to an increasing extent in disease diagnosis due to the high level of efficiency, objectivity, and accuracy that they offer.Data samples on 192 patients with TPE, 54 patients with parapneumonic pleural effusion (PPE), and 197 patients with malignant pleural effusion (MPE) were retrospectively collected. Based on 28 different features obtained via statistical analysis, TPE diagnostic models using four machine learning algorithms (MLAs), namely logistic regression, k-nearest neighbors (KNN), support vector machine (SVM) and random forest (RF) were established and their respective diagnostic performances were calculated. The respective diagnostic performances of each of the four algorithmic models were compared with that of pleural fluid adenosine deaminase (pfADA). Based on 12 features with the most significant impacts on the accuracy of the RF model, a new RF model was designed for clinical application. To demonstrate its external validity, a prospective study was conducted and the diagnostic performance of the RF model was calculated.The respective sensitivity and specificity of each of the four TPE diagnostic models were as follows: logistic regression – 80.5 and 84.8%; KNN– 78.6 and 86.6%; SVM – 83.2 and 85.9%; and RF – 89.1 and 93.6%. The sensitivity and specificity of pfADA were 85.4 and 84.1%, respectively, at the best cut-off value of 17.5 U/L. RF was the superior method among the four MLAs, and was also superior to pfADA. The newly designed RF model (based on 12 out of 28 features) exhibited an acceptable performance rate for the diagnosis of TPE with a sensitivity and specificity of 90.6 and 92.3%, respectively. In the prospective study, its sensitivity and specificity were 100.0 and 90.0%, respectively.Establishing a model for the diagnosis of TPE using RF resulted in a more effective, economical, and faster diagnostic method. This method could enable clinicians to diagnose and treat TPE more effectively. Tuberculous pleurisy is a common disease that causes pleural effusion. In 2014, approximately 1.5 million tuberculosis patients died worldwide , and theIn recent years, research into the use of artificial intelligence (AI) in the field of medicine has increased. Machine learning is a type of AI that allows computers to learn without being explicitly programmed for a given task. Using machine learning algorithms (MLAs) such as support vector machine (SVM), k-nearest neighbor (KNN), and random forest (RF), highly efficient, objective, and accurate disease diagnosis models can be constructed. Based on structural MRI data, Bisenius et al. applied tIn this study, we selected logistic regression, KNN, SVM, and RF to construct TPE diagnostic models. By comparing the respective diagnostic performances of these four models, the most effective model was selected for the differential diagnosis of TPE. We also compared the diagnostic performances of pfADA versus the four MLA methods.Data from patients diagnosed with TPE, parapneumonic pleural effusion (PPE), and malignant pleural effusion (MPE) who had undergone thoracentesis between January 2003 and August 2018 were retrospectively collected.Mycobacterium tuberculosis in pleural fluid/sputum/bronchial aspirate; (3) epithelioid caseous granuloma or positive acid-fast staining in pleural or lung tissue; (4) moderately or strongly positive 5 U tuberculin skin test, positive T-cell spot test (T-SPOT), or positive M. tuberculosis antibody test, and a clinical response to anti-tuberculosis treatment; (5) typical symptoms of tuberculosis with no evidence of additional respiratory diseases, and a marked response to anti-tuberculosis treatment. A clinical response to anti-tuberculosis treatment refers to symptomatic relief, remission or elimination of pleural effusion in patients who have been followed up for at least 12 months after receiving anti-tuberculosis treatment.TPE diagnosis was confirmed when pleural effusion exhibited exudativity and met at least one of the following conditions –12: (1) MPE was diagnosed if pleural effusion was exudative and met one of the following criteria :(1) maliPPE was diagnosed if patients met all the following criteria : (1) exuThe exclusion criteria were as follows: (1) patients with transudative pleural effusion; (2) patients without pfADA results; (3) patients in the TPE and PPE groups who were unable to provide information during follow-up visits.The following features were evaluated: patients’ gender and age, symptoms , history of smoking, hematologic parameters , C-reactive protein (CRP), ADA, lactate dehydrogenase (LDH), carcinoembryonic antigen (CEA)), pleural fluid parameters . In cases where more than one thoracocentesis had been performed, the statistical analysis was performed using only the data from the first pleural fluid sample prior to commencing treatment. Hematological data were obtained from the blood samples taken nearest to the first thoracentesis.ADA activity was measured using enzymatic colorimetry . LDH levels were measured using the lactic acid substrate method . CRP levels were measured via scattering immunoturbidimetry . CEA levels were measured using chemiluminescent immunoassay kits . ESR was determined using an Italian TEST1TH Automatic Blood Sedimentation Instrument. Specific gravity was measured via dry chemical analysis . Total and differential cell counts in blood and total red/white blood cells in pleural fluid were measured using a Japanese Sysmex XN-3000 Automatic Blood Analyzer. Differential cell counts in pleural fluid were counted manually. Total protein, glucose, and chloride concentrations were measured using an Hitachi 7600 Automatic Biochemical Analyzer and original reagents.P < 0.05, and statistically significant variables were introduced into the diagnostic models.Statistical analysis was performed using the SPSS version 20.0 software . The qualitative variables were presented as numbers and percentages, and the continuous variables were presented as medians and ranges. The differences in qualitative variables between patients with and without TPE were assessed using the Chi-square test. The differences between the continuous variables were analyzed using the Mann-Whitney U Test. Statistical significance was set at For this study, we selected four MLAs to establish models for the diagnosis of TPE, namely logistic regression, KNN, SVM, and RF. The workflow for constructing the models was as follows Fig. :Fig. 1WIn the process of establishing the models, logistic regression, KNN, and SVM required data preprocessing. The data were scaled proportionally so that the unit restrictions could be removed without changing the original data distribution, and the missing values were set to the normalized average values. The RF model did not require data preprocessing, and the original data were input directly for splitting.The four methods’ parameters were set as follows: (1) for logistic regression, the maximum number of iterations was 100, the regularization coefficient was 1, and the minimum convergence error was 0.000001; (2) for KNN, the nearest neighbor number was 5; (3) for SVM, the positive penalty factor was 1.0, the negative penalty factor was 1.0, and the convergence coefficient was 0.001; (4) for RF, the number of trees in the forest was 100, the minimum amount of leaf node data was 2, the minimum proportion of leaf node data to parent node data was 0, the maximum depth of a single tree was infinite, and the amount of random data input by a single tree was 100,000.The diagnostic performances of the four algorithmic models  were calculated based on the confusion matrix which included the predicted and actual classification data. Since the data were randomly allocated to a 70% training set and a 30% test set, we conducted 20 model-building tests on each algorithm to determine the average sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), positive likelihood ratio (PLR), negative likelihood ratio (NLR), and accuracy of each of the four algorithmic models as the final results. The best cut-off value and diagnostic performance of pfADA were assessed using receiver operating characteristic (ROC) curve analysis.Among the four algorithmic models, we found that RF offered the best diagnostic performance. The respective impacts of each feature on the accuracy of the RF model were rank ordered. In the RF model, larger Gini index average reduction values indicated a greater effect of a particular feature on the accuracy of the classification model.During the study, 1262 patients with pleural effusion were reviewed, of which 819 were excluded from the analysis for the following reasons: (1) 188 patients exhibited transudative pleural effusion; (2) 213 patient had not undergone thoracentesis; (3) 10 patients lacked pfADA results; (4) 179 patients in the TPE and PPE group were unable to provide information during follow-up visits; (5) 229 patients exhibited pleural effusion with unclear causes. Finally, 443 patients met the analysis criteria, namely 192 with TPE, 54 with PPE, and 197 with MPE. The patients’ malignancies associated with pleural effusion were as follows: lung cancer (172 patients); pleural mesothelioma (3 patients); breast cancer (5 patients); lymphoma (2 patients); pancreatic cancer (1 patient); gastric cancer (3 patients); duodenal ampullary carcinoma (1 patient); thyroid cancer (5 patients); pleural bidirectional differentiation malignant tumor (epithelioid hemangioendothelioma) (1 patient); laryngeal carcinoma (1 patient); papillary carcinoma of the nasal cavity (1 patient); adenocarcinoma with an unknown primary site (2 patients).Among the 36 features, 28 differed significantly between TPE and non-TPE patients Table . The folThe best cut-off value for pfADA in the diagnosis of TPE was 17.5 U/L with a sensitivity of 85.4%, a specificity of 84.1% and an accuracy of 84.7%. The TPE diagnostic performances of the four algorithmic models and pfADA are presented in Table According to the Gini index average reduction values, the respective impact of each feature on the accuracy of the RF model (from high to low) were as follows: pfADA, pfCEA, age, total blood WBC, M% in blood, L% in pleural fluid, N% in pleural fluid, fever, night sweats, pleural fluid total protein, pfLDH, serum CEA, total WBC in pleural fluid, ESR, glucose concentrations in pleural fluid, platelet count, CRP, N% in blood, fatigue, bloody pleural fluid, L% in blood, serum LDH, Rivalta test results, serum ADA, chest pain, history of smoking, anorexia, and cough.Since the lower-ranking features had negligible impacts on the accuracy of the classification model, the Gini index average reduction values are only given for the first 12 features Fig. .Fig. 4TTo reduce the number of features used in the RF model for the sake of clinical application, we selected 12 features to establish the new RF model. The process was the same as in Fig. Mycobacterium tuberculosis in bronchial aspirate), 20 cases of non-TPE . Compared with the final diagnosis, only 2 cases were misdiagnosed as TPE (1 case of PPE and 1 case of MPE). The sensitivity, specificity and accuracy for diagnosing TPE were 100.0, 90.0, and 92.6%, respectively.To demonstrate external validity of our study, we prospectively collected data from 27 patients with pleural effusion from October 2018 to August 2019. The patients were 18 to 86 years old and the median age was 66 years old. 15 patients (55.6%) were female. Using our RF model, 9 patients were diagnosed as TPE and 18 patients were diagnosed as non-TPE. After a series of examinations, the final confirmed diagnosis was as follows: 7 cases of TPE  algorithm for detecting active pulmonary tuberculosis on chest radiographs. The study results showed that DLAD is superior to thoracic radiologists in terms of image classification and lesion localization .Logistic regression is a classical statistical method. The output result of the logistic regression model is a probabilistic value of 0 to 1, with a value of 0.5 indicating a double classification. Logistic regression is widely used in medical diagnostics. For example, based on pfADA, Interferon-γ (IFN-γ), decoy receptor (DcR) 3 and soluble tumour necrosis factor receptor 1 (TNF-sR1) measurements, Shu et al.  construcThe decision process involved in the KNN method consists of a majority vote. When a test sample is input, the voting occurs based on the categories included in the k-nearest training samples, and the test sample is categorized according to the category with the largest number of votes. Chen et al.  applied SVM is another MLA that can be used for classification. SVM principally works by constructing a hyperplane to maximize the distance between two types of samples and the hyperplane. Levman et al.  used theRF is another classifying method that comprises multiple decision trees that integrate input information via an If-Then rule to construct a tree classifier. According to the constructed model, new input information is assigned to the leaf nodes via the root node, and the final level result represents the final classification result. However, when the decision tree is excessively deep, over-fitting may occur, potentially resulting in inaccurate results. RF adopts the concept of integrated learning to synthesize the classification results of each decision tree to prevent over-fitting, thus yielding more accurate and stable results. Xiao et al.  used theAt present, few studies have been conducted on the use of AI machine learning alogrithms in the diagnosis of TPE, and no comparisons have been drawn between the diagnostic performance of MLAs and that of pfADA. In this study, we constructed diagnostic models for TPE using four MLAs  and compared the respective diagnostic performances of these four models to select the superior one for the differential diagnosis of TPE. We also compared the diagnostic performance of MLAs versus that of pfADA. 28 features with statistical differences between the TPE group and the non-TPE group were introduced into the model, including age, symptoms, haematological parameters, and pleural fluid measurements. All of these measures are routinely used in clinical practice and were, therefore, quickly and easily obtainable without the need for specialized equipment. Therefore, the models presented in this paper are broadly applicable. The results show that RF exhibits the best diagnostic performance among the four algorithms with a sensitivity, specificity, accuracy and AUC of 89.1, 93.6, 91.6%, and 0.971, respectively. SVM, KNN, and logistic regression exhibited similar diagnostic performances. Previous studies have demonstrated that RF exhibits superior performance for the classification of various diseases. Chen et al.  employedTo facilitate clinical application, we selected the 12 features with the most significant impacts on the accuracy of the RF model to construct a new RF model. The results show that the diagnostic performance of the new model is similar to that of the RF model constructed with 28 features. Reducing the number of features in the model is highly significant because it may reduce medical expenses and is more convenient in clinical application.Finally, we conducted a preliminary prospective study to demonstrate external validity of our research. So far, only 27 patients have been enrolled. While small, the current result from our prospective study confirms the validity of our original study. That is to say, RF has high sensitivity, specificity, and accuracy in diagnosing TPE.The limitations of this study are that the data was sourced from a single center population, and the number of subjects was small. In the future, a multi-center prospective study which includes a large sample size should be conducted to establish a more accurate TPE diagnostic model.Using AI machine learning algorithms to establish a model for the diagnosis of TPE may improve diagnostic performance. In this regard, RF is superior to logistic regression, KNN, SVM, and pfADA. Establishing a model for the diagnosis of TPE using RF may provide a more effective, economical, and faster diagnostic method based on routine clinical data to assist clinicians in making better diagnoses and treatment decisions.


ARRDC5, DCC, ALK, and ITGA1, which have been found previously associated with CRC, and E2F3 and NID2, which are potentially related to CRC since they have known associations with other types of cancer. We performed pairwise and three-way interaction analysis on the 44 SNPs using information theoretical techniques and found 17 pairwise (p < 0.02) and 16 three-way (p ≤ 0.001) interactions among them. Moreover, functional enrichment analysis suggested 16 functional terms or biological pathways that may help us better understand the etiology of the disease.Colorectal cancer (CRC) has a high incident rate in both men and women and is affecting millions of people every year. Genome-wide association studies (GWAS) on CRC have successfully revealed common single-nucleotide polymorphisms (SNPs) associated with CRC risk. However, they can only explain a very limited fraction of the disease heritability. One reason may be the common uni-variable analyses in GWAS where genetic variants are examined one at a time. Given the complexity of cancers, the non-additive interaction effects among multiple genetic variants have a potential of explaining the missing heritability. In this study, we employed two powerful ensemble learning algorithms, random forests and gradient boosting machine (GBM), to search for SNPs that contribute to the disease risk through non-additive gene-gene interactions. We were able to find 44 possible susceptibility SNPs that were ranked most significant by both algorithms. Out of those 44 SNPs, 29 are in coding regions. The 29 genes include  The goal of most genome-wide association studies (GWAS) is to examine common genetic variations across the entire human genome in order to identify single-nucleotide polymorphisms (SNPs) that are associated with diseases or phenotypic traits . GWAS suVarious statistical and computational approaches can be used to identify genetic variants associated with human diseases. Those approaches can be divided into two categories: uni-variable and multi-variable analyses. In the uni-variable analysis, single-locus tests examine each SNP independently for its association with the phenotype. The effect size (or penetrance) of a variant is calculated and scored based on its significance of association with the disease phenotype. Many single-SNP-based methods were used for initial GWAS analyses, but had limited success in detecting genetic risk factors and explaining disease heritability .Recent research has shifted toward using or developing multi-variable approaches that examine interactions among genetic variants . It is mHLA-B gene , to search for risk susceptibility SNPs associated with colorectal cancer (CRC). We used a CRC GWAS dataset collected from the Canadian province of Newfoundland and Labrador. Both algorithms were able to capture the non-linear gene-gene interactions associated with the disease status, and to rank SNPs based on their disease associations through either main effects or interaction effects. We identified 44 SNPs ranked as the most significant by both algorithms, including both known CRC related genes such as The colorectal (CRC) GWAS case-control data were collected from CRC patients and healthy individuals with matching age, gender, and geographical distributions within the province of Newfoundland and Labrador, Canada. The CRC Transdisciplinary (CORECT) consortium coordinated the genotyping of data. Genotyping was conducted using a custom Affymetrix genome-wide platform (the Axiom CORECT Set) on two physical genotyping chips (pegs) for two datasets with around 1.2 and 1.1 million SNPs . The firr2 > 0.6, LD window size 2,000). Missing genotypes were imputed using the most frequent alleles in the population. Last, the genotypes were re-coded using numerical values {0, 1, 2} with 0 standing for homozygous reference, 1 for heterozygous variant, and 2 for homozygous variant. After the steps of quality control and imputation, the final preprocessed and balanced dataset had 186,251 SNPs and 944 samples (472 being cases and 472 being controls).Next, we performed per-sample and per-marker quality control steps and linkage disequilibrium (LD) pruning on the CRC data using PLINK. In the per-sample quality control, sex check was performed, sex chromosomes were excluded, and samples with more than 1% missing genotypes and outlier heterozygosity rate were removed. In the per-marker quality control, SNPs with missing call rates higher than 5%, with minor allele frequencies (MAFs) less than 5%, or with Hardy-Weinberg equilibrium values greater than 0.0001 were removed. Then, we pruned SNPs that were dependent on each other  with the approval number HIC 01.70. Our study was recognized by HREA as the use of secondary data which have already been collected and de-identified, and did not require a clearance.Most computational methods in informatics find it prohibiting to analyze high dimensional GWAS data  due to tFeature selection is frequently used as a data filtering step in machine learning when the original data contain noisy or irrelevant features, or attributes, that could compromise the prediction power of learning algorithms . FeatureIn our previous study , six feaMany preliminary GWAS employed uni-variable approaches where interactions between multiple variables could be overlooked . MoreoveEnsemble learning methods use an aggregation of predictors known as base learners. To produce a final prediction, the predictions of the base learners are weighted and the overall predictions are decided through majority voting for classifications and averaging for regressions. It has been shown that ensemble learning methods are powerful at reducing variance and overfitting by utilizing a collection of diverse base learners such as classification and regression trees (CART) . In thisbagging (short for bootstrap aggregating) that uses bootstrapped samples of the training data to train classification or regression models separately  predictor variables are randomly picked from the total M variables in the original data. Then the best variable/partition-point among the mtry variables is picked and used to split the node into two daughter nodes. Each tree grows to its maximum size, that is, when there is only one sample in the leaves. To make a classification for a new testing sample, each tree casts a vote for the predicted class and the majority vote will be the final prediction result. Intuitively, reducing mtry will reduce the correlation between any pair of trees in the ensemble. For classification, the default value of mtry is In random forests, arameter .The random forests algorithm is effective in uncovering interactions among genes that do not exhibit strong main effects . The algmtry (number of random variables to make best split at each tree node) and ntree (number of trees), and using greater values, work well generally for large GWAS datasets.mtry and ntree in our implementation. For mtry we selected the values of {100, 200, 300, 500, 1,000}, and the selected values for ntree were {500, 1,000, 2,000}. Other parameters used the package default settings. Therefore, we had 15 different combinations of mtry and ntree.In this study, we used a very fast implementation of random forests provided in an R package called “ranger” . The “raaverage CV accuracy using a specific combination of parameters was computed as the averaged accuracy across all testing samples. The parameter combination with the best average CV accuracy will be picked.In addition, we performed a 10-fold CV. The original dataset was partitioned into 10 equal subsets, and for each of the 10 iterations, one subset was picked as the testing data and the rest nine subsets served as the training data. Thus, each sample was used in the testing data exactly once, and the CV accuracy was computed as the percentage of correctly classifying all the testing samples. For each parameter combination and each fold of the CV , we repeated the random forests algorithm 10 times given the stochastic nature of bootstrapping and selecting variables to grow the trees. Since each execution of the random forests will yield a unique predictive model, we computed the accuracy of classifying each testing sample as the fraction of it being correctly predicted by the 10 models resulted by the 10 executions of the algorithm. Then the When the best combination of parameters was picked, the predictive models learned using it were then used to rank features according to the importance of the features in classification. For random forests, we used Gini Index as the quantification of feature importance . Gini Inboosting where the base learners evolve over time and make weighted votes , where m = 1, 2,…,n.trees, denote the approximation that maps the input variables x to the desired output y. At iteration m, a new approximation mF(x) is constructed through improving on the previous one m−F1(x) by adjusting it using the gradient of the loss function ∇L).Gradient boosting machine is a boosting machine learning algorithm in which a weighted combination of predictors are used to make the final prediction . A set on.trees (the number of trees), interaction.depth , and shrinkage (the learning rate or step-size reduction). The testing values of these parameters were as follows: n.trees = {100, 500, 1,000, 2,000}, interaction.depth = {1, 2, 10}, and shrinkage = {0.001, 0.01, 0.1}. Therefore, we had 36 different combinations of the three parameters. Other parameters of GBM, such as n.minobsinnode , bag.fraction (the fraction of the training set samples randomly selected to construct a new tree), and train.fraction (the fraction of samples used to fit a GBM model), were set to default values as 10, 0.5, and 0.5, respectively.For implementation, we used an R package called “gbm” . SimilarSimilar to random forests implementation, we used a 10-fold CV and repeated the algorithm 10 times for each combination of parameters and each fold of the CV. The best combination of parameters found was then used to rank the features. Feature Importance was estimated based on the number of times a feature is selected for splitting and the improvement to the model as a result of each split, weighted by the number of observations each split operates, and averaged over all trees . Again, Both the random forests and GBM algorithms provide importance estimations on each SNP based on their contribution to the accurate classification. Such importance scores by the ensemble algorithms help to identify the top-ranked SNPs that are most possibly associated with the disease of CRC. It has been suggested that tree-based ensemble learning algorithms are able to detect interaction effects among multiple SNP features. Therefore, we employ an information gain method , 2013b tI between SNP A and the disease outcome D as the main effect of the individual SNP A, since it captures how much information of the disease status the genotype of SNP A provides. Then the information gain IG of combining two SNPs A and B on explaining the disease status D is computed by subtracting the main effects of A and B, that is, I and I from the total mutual information I by joining A and B to explain D. Essentially, the information gain IG captures the gained information of combining A and B on explaining D, that is, the synergistic and non-additive interaction effect between SNPs A and B.The information gain method considers the genotype of each SNP and the disease outcome as random variables. It first computes the mutual information A, B, and C, are considered, the information gain IG is computed by subtracting all the main effects and pairwise interaction effects from the total mutual information I between joining A, B, and C together and the disease status D. Such an information gain metric measures the pure three-way synergistic interaction among three SNPs on explaining the disease outcome.Similarly, when three SNPs, p-value was assessed for each computed information gain by counting the portion of permuted datasets that had a greater value of the information gain than the real data.Permutation testing can be used to assess the significance level of a computed information gain value. In each permutation, the disease status labels of the data are randomly shuffled, and the information gain values are computed again for the permuted data. In this study, we performed a 1,000-fold permutation testing, that is, 1,000 permutations were collected to provide a null distribution of the assumption that there was no association between the genotypes and the disease outcome. Then the http://www.ensembl.org) and the National Center for Biotechnology Information (NCBI) (https://www.ncbi.nlm.nih.gov/) databases. The databases provide biological information on the allele, chromosome, and gene information for each SNP.We also annotated the identified top SNPs with functional information using online resources including the ENSEMBL  bioinformatics tool  to perfoIn this section, we first show the result of feature selection and then the application results of the two ensemble learning methods on the CRC GWAS dataset. We performed the parameter tuning and used the parameter combinations of the random forest and the GBM algorithms that yielded the best classification accuracies. Meanwhile, both algorithms can rank the SNP variables based on their contributions to the classification. We identified the set of the best-ranked SNPs by both algorithms as our key possible susceptibility genetic markers, which were further investigated and validated through gene-gene interaction and pathway enrichment analyses.We ran the TuRF feature selection method on all 186,251 SNPs. Each SNP was assigned a score based on its contribution to the disease status. Since TuRF is a multi-variable feature selection algorithm, such a contribution may include both the individual main effect or the effect of interacting with other SNPs. mean + 3SD was chosen which filtered the top 2,798 SNPs. The feature subset was an appropriate size such that the training of the ensemble learning algorithms could be finished in a reasonable time frame, meanwhile, enough features were included in the subsequent analysis to identify possible gene-gene interactions.To reduce the computational overhead of the downstream ensemble learning analysis, we then picked a score threshold and only included the potential SNPs with TuRF importance scores higher than the threshold. The threshold mtry = {100, 200, 300, 500, 1,000} and ntree = {500, 1,000, 2,000}. Thus, there were 15 combinations of parameter values. Recall that we ran the algorithm 100 times for each combination, the average CV accuracy and area under the curve (AUC) were computed over 100 runs.The parameter tuning for the random forests algorithm included adjusting the values of two parameters, ntree, and different curves represent using different values of the other parameter mtry. The highest CV accuracy of 75% and AUC of 0.84 were achieved when mtry = 100 and ntree = 2,000. That is, the algorithm performed the best when ntree was set to the maximum value and mtry was set to the minimum value. This suggests that greater values of ntree and lower values of mtry are preferable for analyzing GWAS data. Both increasing the number of trees and reducing the number of randomly picked variables to construct each node of a tree enhance the diversity of trees in the ensemble. The result suggests that the genetic explanation of complex diseases may be highly heterogeneous, that is, a large number of diverse classification models that use different combinations of SNP attributes are needed to approximate the mapping from the genotypes to the disease outcome.n.trees = {100, 500, 1,000, 2,000}, interaction.depth = {1, 2, 10}, and shrinkage = {0.001, 0.01, 0.1}. Again, for each of the 36 combinations of parameter combinations, 10 runs were collected for each of the 10-fold CV, and the average CV accuracy and AUC were computed over 100 runs.The GBM algorithm had three parameters, that is, shrinkage value, and shows the average CV accuracy (left) and AUC (right) as a function of the parameter n.trees. The highest CV accuracy was 74% and the CV AUC was 0.82, achieved when n.trees was set to 2,000, interaction.depth was 10, and shrinkage was 0.1. This suggests that, for GWAS data analysis, GBM performs better when greater values of interaction.depth, n.trees, and shrinkage are used.mtry = 100 and ntree = 2,000, and of the GBM algorithm, that is, n.trees = 2,000, interaction.depth = 10, and shrinkage = 0.1, to assess the importance of the SNP features. In random forests, the Gini Index importance of a feature was estimated based on the impurity of splitting testing samples on the tree node using the feature. Since we repeated the algorithm 100 times (10 times for each fold of the 10-fold CV), the final importance score (between 0 and 1) of a feature was computed as the averaged impurity score over 100 runs. The averaged importance score of each feature using GBM was computed similarly.After parameter tuning, we chose the optimal parameter combinations of the random forests algorithm, that is, ARRDC5 is ranked as the most important by both algorithms.p-values of SNPs and their ranking among all 186,251 SNPs are also shown in the table. These 44 SNPs and their chromosome and gene information are listed in p < 0.02) with their information gain measures and the p-values. The maximum information gain was 1.3% for the interaction between SNPs rs2010907 and rs3760948 (p = 0.002), meaning that the synergistic interaction effect between the two SNPs explained 1.3% of the disease outcome. Six pairs of coding SNPs, that is, SNPs from genes, had significant interaction effects.We calculated the information gain between all p ≤ 0.001) SNP trios with their information gain measures and p-values. The strongest three-interaction was found among SNPs from genes OTOP1, EPHX2, and LINC with a strength of 2.55% and a significance level of p < 0.001, suggesting that the pure three-way synergistic effect among the three SNPs can explain 2.55% of the disease outcome. There were six trios of significant interacting SNPs all from coding regions of DNA.We then calculated the three-way information gain among all p < 0.05. tobacco use disorder with 14 out of 29 genes in the category and a significance level of 3.9 × 10−5.We submitted the identified 29 genes to the DAVID software, and chose categories of Disease, GO, and Pathway for a functional enrichment analysis. We set the gene count threshold as five, that is, only functional categories including more than 4 of the 29 identified genes were considered, and the significance cutoff as 0.05, that is, Fisher’s exact test Identifying genetic markers associated with complex human diseases helps us better understand the disease etiology in order to better diagnose, treat, and even prevent diseases. Given the complexity of human diseases, especially cancers, the causing factors are more plausibly interactions among multiple genetic attributes instead of individual contributions. However, searching for combinations of attributes imposes a significant challenge for bioinformatics and genome-wide association research, since thousands to a million possible genetic attributes can be included for investigation.Powerful machine learning algorithms have been used for mining high-volume data in fields such as engineering, finance, and social sciences, and have started to see applications in analyzing high-dimensional biomedical data as well.DCC, ALK, ITGA1, E2F3, and NID2, and unknown but potential CRC association genes.In this article, we explored the application of two ensemble learning algorithms, random forests, and GBM, in identifying interacting genetic attributes associated with CRC. We studied a GWAS CRC dataset collected from the Canadian province of Newfoundland. We performed data preprocessing and filtering using the TuRF feature selection algorithm. By parameter tuning, we optimized the parameters for both random forests and GBM for GWAS data analyses. Both ensemble learning algorithms produced rankings on the importance of SNP contribution to the disease classification. By comparing the rankings provided by both algorithms, we identified a set of 44 top ranked SNPs from both coding and non-coding regions of DNA . The codALK, JPH1, and DCC. In addition, functional enrichment analysis on the set of the 29 identified genes suggested 16 significantly enriched functional terms including four diseases: tobacco use disorder, chemical dependency, metabolic diseases, and cardiovascular diseases, and 12 GO terms  was ranked the highest by both random forests and GBM (GLIS3) and non-coding SNP rs1816647 (SNP rs3760948 from gene arrestin domain-containing 5 ( and GBM . It was  and GBM  and the s1816647 . This isDCC) is a tumor suppressor in CRC . Anaplastic lymphoma kinase (ALK) gene is directly related to colorectal adenocarcinoma, and may affect treatments for advanced CRC  and significant (p = 0.001) three-way interaction. Gene α1-integrin (ITGA1) and transcription factor E2F3 have been found related to the prevention of tumor progression in colon cancer patients (NID2) has been found to be associated with lung cancer (The gene “deleted in CRC” (r in CRC  and is wnced CRC . Genes Dpatients . Gene nig cancer .INPP5D) had strong and significant pairwise interactions with another enzyme gene 1-Phosphatidylinositol-4,5-bisphosphate phosphodiesterase beta-4 (PLCB4) and gene cadherin-4 (CDH4), as well as a non-coding SNP rs11985944. We have not seen any published literature associating INPP5D with CRC and related diseases, and it can be a possible risk association gene to investigate in biology.Our results also suggested novel genes associated with the disease of CRC. For instance, enzyme gene phosphatidylinositol-3,4,5-trisphosphate 5-phosphatase 1 (tobacco use disorder (p = 3.9 × 10−5)  . In a st × 10−5) . Another cancers . The tumOur future studies include: (1) utilizing more computational power in order to include more SNP attributes in the classifier training and feature importance analysis and (2) exploring or combining other machine learning algorithms, including neural networks and evolutionary algorithms, for the search of gene-gene interactions.Machine learning algorithms have seen increasing applications in bioinformatics and computational biology thanks to their powerful abilities of automatic learning and modeling complex relationships among a large number of features. Applications of machine learning techniques require domain-specific tailoring and careful design since an application area has unique problem definitions and challenges. In the context of GWAS, we need algorithms that are able to detect the non-linear, non-additive interactions among multiple genetic factors that contribute to the disease outcome. This article demonstrates a novel design of an informatics framework of using two ensemble learning algorithms to search for interacting genetic factors associated with cancer and validating the results through statistical interaction analysis and biological functional enrichment analysis. With further biological experiments, our bioinformatics findings may help us better understand the disease etiology of CRC. We also hope our study can inspire more bioinformatics tool developments for human disease association studies.