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Sunflower oil is an indispensable product of functional purpose, which at its reasonable consumption increases immunity, cleanses the body of toxins, improves the brain and heart, and as a result, having a complex effect on the whole body provides an undeniable benefit to human health. Modern development of sunflower oil purification technologies involves improvement of existing and development of new methods and technical means, environmentally safe, accompanied by minimum energy consumption and characterised by increased indicators of technological efficiency, providing the possibility of separation of unnecessary impurities and preservation of valuable components extracted from the oil for their further use. Such a promising method is the purification of sunflower oil in the electric field. The paper considers the possibility of cleaning sunflower oil by an electrostatic precipitator from suspended particles by means of an electric field acting on them. The planned experiment is described and the process of removal of unnecessary impurities is analysed. The justification of the factors and parameters of the experimental study is considered separately. The dependences of the electrical characteristics of oil on the parameters of the purification process are established. Analytical expressions for empirical dependences of energy parameters of the purification process, mode and parameters of ESP operation are determined. The adequacy of theoretical and experimental results has been checked. Having analysed the obtained results, the optimum parameters of effective cleaning and ESP operation were determined, which on the basis of the obtained regression equations should be the following: voltage at ESP electrodes U = 4.4 kV, interelectrode distance b = 0.01 m, oil temperature at the inlet θ = 42°С. The calculated energy consumption at these parameters is equal to 12 W-h/kg.

The article is aimed at creating a correct worldview about labor protection among engineering and technical workers of the agro-industrial complex engaged in the repair and maintenance of energy facilities. In our case, modern tractors and combines act as energy means. The main method is the analysis and conditions of operations for the repair and maintenance of tractors and combines, on the basis of which there is a need to properly organize the supervision of labor safety. As a result, the directions of work of the occupational safety specialist and the organization of his activities for the supervision of occupational safety have been developed and formulated, and the duties of engineering and technical workers in relation to this direction are also given. This article will be useful for masters and postgraduates studying the organization of maintenance and repair of tractors and combines, as well as engineering and technical workers of agro-industrial enterprises.

The article analyzes the factors affecting the soil, which directly affect the yield. Modern domestic agricultural machinery mainly has technical and technological solutions that make it possible to significantly advance in the direction of ecological balance of the impact of crop production on soils. These include non-fallow, combined techniques of basic and pre-sowing tillage, as well as the use of a new complex of heavy agrophilic running systems of heavy mobile power facilities.

The article provides the analysis of the criteria for the sustainable functioning of the automobile operating company in emergency situations (ES), indicating the factors that most influence this process, and the main characteristics of the company, on which the degree of its stability depends. The assessment of the stability of the automobile operating company (AOC) objects is carried out by the two most common damaging factors of emergency situations in peacetime: shock wave and heat of detonation. Tables provide characteristics of the constituent elements of such a automobile operating company and the damage rate of the elements of automobile operating companies, which characterize the damage rate and the corresponding pressure resulting from the explosion. The graph of the dependence of the destruction probability of the main production factors on the stability indicators of the object elements is presented. The characteristics of the damage rate of buildings are also presented, which will make it possible to coordinate properly the work on the stability of the object. The sequence of assessing the stability of the automobile operating company is determined. Recommendations for ensuring the protection of enterprise equipment are given. Examples of protective screens for protecting equipment and the basics of the methodology for their calculations are proposed.Basing on the results of the analysis, the constructive solution for the protective structure for AOC enterprises is proposed, designed to reduce the impact of secondary damaging factors of emergency situations on the surrounding area.

The article is intended for engineering and technological workers involved in the exploitation of road transport, for specialists of automobile enterprises who are responsible for organizing road safety. Prevention of diseases and increase of efficiency of drivers, development of such functions as attention, speed of reaction, perception of space and other professionally important psycho-physiological qualities, can be achieved only through purposeful and dosed application of specially designed complex sports and health-improving classes. The article presents the simplified methodology for determining the physical condition of the personnel of the car enterprise in order to design exercises and complexes based on it to achieve the goal. It also offers forms of physical education, which contributes to the optimal method of their organization and conduct. In addition, the frequency of classes is of great importance, which directly affects the speed of recovery of drivers. The purpose of this article was to determine the content, organizational and methodological features of complex physical exercises for drivers of vehicles. In the course of the research, the comprehensive program was designed to prevent fatigue, prevent occupational diseases and improve the efficiency of drivers. The following forms of physical exercises were included in the complex program: introductory gymnastics, physical culture minute, physical culture pause, self-massage, corrective exercises for the eyes, physical culture and health classes.

For many decades, any organization involved in the operation and maintenance of vehicles cannot imagine its activities without compressor units. Compressors are used in all sectors of the national economy, and are valued for their reliability, high efficiency, long service life. The scope of the equipment is very wide, without it the work of any large industrial enterprise is indispensable. Compressors are used in mechanical engineering, metallurgy, oil and gas industry, car services, construction and other industries. Each compressor unit is equipped with an emergency protection system that provides: automatic shutdown of the compressor; sound and light alarm. According to the requirements of the Rules, all compressor units are equipped with instrumentation: pressure gauges for measuring pressure, thermometers or other sensors for measuring temperature. The compressor is placed in a separate room, which should not be connected with the premises where explosive and chemically hazardous industries are located. In the premises of compressor units, it is not allowed to place equipment and equipment that are technologically and structurally not related to compressors.

Greening the agro-industrial complex became a problem in the last two decades of the 20th century, although some problems in this relatively new direction were raised in the 60s of the 20th century. In this article, the problem of the influence of technical means on the ecology of the agricultural engineering sphere is considered from the point of view of the impact on the human body of substances generated as a result of the technical work of the agro-industrial complex. The problem of greening the agro-industrial complex in modern conditions is of great importance in connection with the observed intensification of the development of agricultural production, which has a direct negative impact on the environment, which is the key to the health of people involved in the labor process. When studying human-machine systems in crop production, the place of ecology in the “external environment” block is determined. The dependences of the conceptual model of transformation of ecosystems under the influence of pollution are presented, which allows us to determine the vector of research in this direction.

Abstract Background Neurocognitive impairment and psychiatric symptoms have been associated with deficits in psychosocial and occupational functioning in patients with schizophrenia. This post-hoc analysis evaluates the relationships among cognition, psychopathology, and psychosocial functioning in patients with schizophrenia at baseline and following sustained treatment with antipsychotic drugs. Methods Data were obtained from a clinical trial assessing the cognitive effects of selected antipsychotic drugs in patients with schizophrenia. Patients were randomly assigned to 24 weeks of treatment with olanzapine (n = 159), risperidone (n = 158), or haloperidol (n = 97). Psychosocial functioning was assessed with the Heinrichs-Carpenter Quality of Life Scale [QLS], cognition with a standard battery of neurocognitive tests; and psychiatric symptoms with the Positive and Negative Syndrome Scale [PANSS]. A path-analytic approach was used to evaluate the effects of changes in cognitive functioning on subdomains of quality of life, and to determine whether such effects were direct or mediated via changes in psychiatric symptoms. Results At baseline, processing speed affected functioning mainly indirectly via negative symptoms. Positive symptoms also affected functioning at baseline although independent of cognition. At 24 weeks, changes in processing speed affected changes in functioning both directly and indirectly via PANSS negative subscale scores. Positive symptoms no longer contributed to the path-analytic models. Although a consistent relationship was observed between processing speed and the 3 functional domains, variation existed as to whether the paths were direct and/or indirect. Working memory and verbal memory did not significantly contribute to any of the path-analytic models studied. Conclusion Processing speed demonstrated direct and indirect effects via negative symptoms on three domains of functioning as measured by the QLS at baseline and following 24 weeks of antipsychotic treatment.

Abstract Background This study focuses on exploring the relationship between changes in appetite or eating behaviors and subsequent weight change for adult patients with schizophrenia or bipolar disorder treated with olanzapine and adjunctive potential weight mitigating pharmacotherapy. The aim is not to compare different weight mitigating agents, but to evaluate patients' characteristics and changes in their eating behaviors during treatment. Identification of patient subgroups with different degrees of susceptibility to the effect of weight mitigating agents during olanzapine treatment may aid clinicians in treatment decisions. Methods Data were obtained from 3 randomized, double-blind, placebo-controlled, 16-week clinical trials. Included were 158 patients with schizophrenia or bipolar disorder and a body mass index (BMI) ≥ 25 kg/m2 who had received olanzapine treatment in combination with nizatidine (n = 68), sibutramine (n = 42), or amantadine (n = 48). Individual patients were analyzed for categorical weight loss ≥ 2 kg and weight gain ≥ 1 kg. Variables that were evaluated as potential predictors of weight outcomes included baseline patient characteristics, factors of the Eating Inventory, individual items of the Eating Behavior Assessment, and the Visual Analog Scale. Results Predictors/correlates of weight loss ≥ 2 kg included: high baseline BMI, low baseline interest in food, and a decrease from baseline to endpoint in appetite, hunger, or cravings for carbohydrates. Reduced cognitive restraint, increase in hunger, and increased overeating were associated with a higher probability of weight gain ≥ 1 kg. Conclusion The association between weight gain and lack of cognitive restraint in the presence of increased appetite suggests potential benefit of psychoeducational counseling in conjunction with adjunctive pharmacotherapeutic agents in limiting weight gain during antipsychotic drug therapy. Trial Registration This analysis was not a clinical trial and did not involve any medical intervention.

Abstract Background To make well informed treatment decisions for their patients, clinicians need credible information about potential risk for substantial weight gain. We therefore conducted a post-hoc analysis of clinical trial data, examining early weight gain as a predictor of later substantial weight gain. Methods Data from 669 (Study 1) and 102 (Study 2) olanzapine-treated patients diagnosed with schizophrenia, schizophreniform, or schizoaffective disorder were analyzed to identify and validate weight gain cut-offs at Weeks 1–4 that were predictive of substantial weight gain (defined as an increase of ≥ 5, 7, 10 kg or 7% of baseline weight) after approximately 30 weeks of treatment. Baseline characteristics alone, baseline characteristics plus weight change from baseline to Weeks 1, 2, 3 or 4, and weight change from baseline to Weeks 1, 2, 3, or 4 alone were evaluated as predictors of substantial weight gain. Similar analyses were performed to determine BMI increase cut-offs at Weeks 1–4 of treatment that were predictive of substantial increase in BMI (1, 2 or 3 kg/m2 increase from baseline). Results At Weeks 1 and 2, predictions based on early weight gain plus baseline characteristics were more robust than those based on early weight gain alone. However, by Weeks 3 and 4, there was little difference between the operating characteristics associated with these two sets of predictors. The positive predictive values ranged from 30.1% to 73.5%, while the negative predictive values ranged from 58.1% to 89.0%. Predictions based on early BMI increase plus baseline characteristics were not uniformly more robust at any time compared to those based on early BMI increase alone. The positive predictive values ranged from 38.3% to 83.5%, while negative predictive values ranged from 42.1% to 84.7%. For analyses of both early weight gain and early BMI increase, results for the validation dataset were similar to those observed in the primary dataset. Conclusion Results from these analyses can be used by clinicians to evaluate risk of substantial weight gain or BMI increase for individual patients. For instance, negative predictive values based on data from these studies suggest approximately 88% of patients who gain less than 2 kg by Week 3 will gain less than 10 kg after 26–34 weeks of olanzapine treatment. Analysis of changes in BMI suggests that approximately 84% of patients who gain less than .64 kg/m2 in BMI by Week 3 will gain less than 3 kg/m2 in BMI after 26–34 weeks of olanzapine treatment. Further research in larger patient populations for longer periods is necessary to confirm these results.

Abstract Background Patients with acute mania respond differentially to treatment and, in many cases, fail to obtain or sustain symptom remission. The objective of this exploratory analysis was to characterize response in bipolar disorder by identifying groups of patients with similar manic symptom response profiles. Methods Patients (n = 222) were selected from a randomized, double-blind study of treatment with olanzapine or divalproex in bipolar I disorder, manic or mixed episode, with or without psychotic features. Hierarchical clustering based on Ward's distance was used to identify groups of patients based on Young-Mania Rating Scale (YMRS) total scores at each of 5 assessments over 7 weeks. Logistic regression was used to identify baseline predictors for clusters of interest. Results Four distinct clusters of patients were identified: Cluster 1 (n = 64): patients did not maintain a response (YMRS total scores ≤ 12); Cluster 2 (n = 92): patients responded rapidly (within less than a week) and response was maintained; Cluster 3 (n = 36): patients responded rapidly but relapsed soon afterwards (YMRS ≥ 15); Cluster 4 (n = 30): patients responded slowly (≥ 2 weeks) and response was maintained. Predictive models using baseline variables found YMRS Item 10 (Appearance), and psychosis to be significant predictors for Clusters 1 and 4 vs. Clusters 2 and 3, but none of the baseline characteristics allowed discriminating between Clusters 1 vs. 4. Experiencing a mixed episode at baseline predicted membership in Clusters 2 and 3 vs. Clusters 1 and 4. Treatment with divalproex, larger number of previous manic episodes, lack of disruptive-aggressive behavior, and more prominent depressive symptoms at baseline were predictors for Cluster 3 vs. 2. Conclusion Distinct treatment response profiles can be predicted by clinical features at baseline. The presence of these features as potential risk factors for relapse in patients who have responded to treatment should be considered prior to discharge. Trial registration The clinical trial cited in this report has not been registered because it was conducted and completed prior to the inception of clinical trial registries.

Abstract Background The true dose effect in flexible-dose clinical trials may be obscured and even reversed because dose and outcome are related. Methods To evaluate dose effect in response on primary efficacy scales from 2 randomized, double-blind, flexible-dose trials of patients with bipolar mania who received olanzapine (N = 234, 5–20 mg/day), or patients with schizophrenia who received olanzapine (N = 172, 10–20 mg/day), we used marginal structural models, inverse probability of treatment weighting (MSM, IPTW) methodology. Dose profiles for mean changes from baseline were evaluated using weighted MSM with a repeated measures model. To adjust for selection bias due to non-random dose assignment and dropouts, patient-specific time-dependent weights were determined as products of (i) stable weights based on inverse probability of receiving the sequence of dose assignments that was actually received by a patient up to given time multiplied by (ii) stable weights based on inverse probability of patient remaining on treatment by that time. Results were compared with those by unweighted analyses. Results While the observed difference in efficacy scores for dose groups for the unweighted analysis strongly favored lower doses, the weighted analyses showed no strong dose effects and, in some cases, reversed the apparent "negative dose effect." Conclusion While naïve comparison of groups by last or modal dose in a flexible-dose trial may result in severely biased efficacy analyses, the MSM with IPTW estimators approach may be a valuable method of removing these biases and evaluating potential dose effect, which may prove useful for planning confirmatory trials.

In this paper, we review recent advances in statistical methods for the evaluation of the heterogeneity of treatment effects (HTE), including subgroup identification and estimation of individualized treatment regimens, from randomized clinical trials and observational studies. We identify several types of approaches using the features introduced in Lipkovich et al (Stat Med 2017;36: 136-196) that distinguish the recommended principled methods from basic methods for HTE evaluation that typically rely on rules of thumb and general guidelines (the methods are often referred to as common practices). We discuss the advantages and disadvantages of various principled methods as well as common measures for evaluating their performance. We use simulated data and a case study based on a historical clinical trial to illustrate several new approaches to HTE evaluation., (© 2024 John Wiley & Sons Ltd.)

Researchers are increasingly using insights derived from large-scale, electronic healthcare data to inform drug development and provide human validation of novel treatment pathways and aid in drug repurposing/repositioning. The objective of this study was to determine whether treatment of patients with multiple sclerosis with dimethyl fumarate, an activator of the nuclear factor erythroid 2-related factor 2 (Nrf2) pathway, results in a change in incidence of type 2 diabetes and its complications. This retrospective cohort study used administrative claims data to derive four cohorts of adults with multiple sclerosis initiating dimethyl fumarate, teriflunomide, glatiramer acetate or fingolimod between January 2013 and December 2018. A causal inference frequentist model averaging framework based on machine learning was used to compare the time to first occurrence of a composite endpoint of type 2 diabetes, cardiovascular disease or chronic kidney disease, as well as each individual outcome, across the four treatment cohorts. There was a statistically significantly lower risk of incidence for dimethyl fumarate versus teriflunomide for the composite endpoint (restricted hazard ratio [95% confidence interval] 0.70 [0.55, 0.90]) and type 2 diabetes (0.65 [0.49, 0.98]), myocardial infarction (0.59 [0.35, 0.97]) and chronic kidney disease (0.52 [0.28, 0.86]). No differences for other individual outcomes or for dimethyl fumarate versus the other two cohorts were observed. This study effectively demonstrated the use of an innovative statistical methodology to test a clinical hypothesis using real-world data to perform early target validation for drug discovery. Although there was a trend among patients treated with dimethyl fumarate towards a decreased incidence of type 2 diabetes, cardiovascular disease and chronic kidney disease relative to other disease-modifying therapies-which was statistically significant for the comparison with teriflunomide-this study did not definitively support the hypothesis that Nrf2 activation provided additional metabolic disease benefit in patients with multiple sclerosis., Competing Interests: This work was funded by Eli Lilly and Company and all authors are employees of Eli Lilly and Company. This does not alter our adherence to PLOS ONE policies on sharing data and materials. Alan J.M. Brnabic was involved with the conceptualization, methodology, investigation and formal analysis of the data for the work and contributed to the original draft preparation, review and editing of the manuscript. Sarah E. Curtis was involved with the conceptualization, methodology, investigation and formal analysis of the data for the work and contributed to the review and editing of the manuscript. Joseph A. Johnston was involved with the conceptualization, methodology and investigation of the data for the work, and contributed to the original draft preparation, review and editing of the manuscript. Albert contributed to the review and editing of the manuscript. Anthony J. Zagar was involved with the methodology and investigation of the data for the work and contributed to the original draft preparation of the manuscript. Ilya Lipkovich was involved with the methodology and validation of the data for the work and contributed to the original draft preparation of the manuscript. Zbigniew Kadziola was involved with the formal analysis of the data for the work and contributed to the review and editing of the manuscript. Megan H. Murray was involved with the investigation, methodology and formal analysis of the data for the work and contributed to the original draft preparation of the manuscript. Timothy Ryan was involved with the conceptualization and investigation of the data for the work and contributed to the original draft preparation, review and editing of the manuscript. All authors have participated sufficiently in the work to agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. All authors give final approval of the manuscript to be published., (Copyright: © 2024 Brnabic et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background: Digital health studies using electronic patient-reported outcomes (ePROs) and wearables bring new challenges, including the need for participants to consistently provide trial data., Objective: This study aims to characterize the engagement, protocol adherence, and data completeness among participants with rheumatoid arthritis enrolled in the Digital Tracking of Arthritis Longitudinally (DIGITAL) study., Methods: Participants were invited to participate in this app-based study, which included a 14-day run-in and an 84-day main study. In the run-in period, data were collected via the ArthritisPower mobile app to increase app familiarity and identify the individuals who were motivated to participate. Successful completers of the run-in period were mailed a wearable smartwatch, and automated and manual prompts were sent to participants, reminding them to complete app input or regularly wear and synchronize devices, respectively, during the main study. Study coordinators monitored participant data and contacted participants via email, SMS text messaging, and phone to resolve adherence issues per a priori rules, in which consecutive spans of missing data triggered participant contact. Adherence to data collection during the main study period was defined as providing requested data for >70% of 84 days (daily ePRO, ≥80% daily smartwatch data) or at least 9 of 12 weeks (weekly ePRO)., Results: Of the 470 participants expressing initial interest, 278 (59.1%) completed the run-in period and qualified for the main study. Over the 12-week main study period, 87.4% (243/278) of participants met the definition of adherence to protocol-specified data collection for weekly ePRO, and 57.2% (159/278) did so for daily ePRO. For smartwatch data, 81.7% (227/278) of the participants adhered to the protocol-specified data collection. In total, 52.9% (147/278) of the participants met composite adherence., Conclusions: Compared with other digital health rheumatoid arthritis studies, a short run-in period appears useful for identifying participants likely to engage in a study that collects data via a mobile app and wearables and gives participants time to acclimate to study requirements. Automated or manual prompts (ie, "It's time to sync your smartwatch") may be necessary to optimize adherence. Adherence varies by data collection type (eg, ePRO vs smartwatch data)., International Registered Report Identifier (irrid): RR2-10.2196/14665., (©William B Nowell, Jeffrey R Curtis, Hong Zhao, Fenglong Xie, Laura Stradford, David Curtis, Kelly Gavigan, Jessica Boles, Cassie Clinton, Ilya Lipkovich, Shilpa Venkatachalam, Amy Calvin, Virginia S Hayes. Originally published in JMIR Human Factors (https://humanfactors.jmir.org), 07.11.2023.)

Background: Obesity is associated with an increased risk of multiple conditions, ranging from heart disease to cancer. However, there are few predictive models for these outcomes that have been developed specifically for people with overweight/obesity., Objective: To develop predictive models for obesity-related complications in patients with overweight and obesity., Methods: Electronic health record data of adults with body mass index 25-80 kg/m 2 treated in primary care practices between 2000 and 2019 were utilized to develop and evaluate predictive models for nine long-term clinical outcomes using a) Lasso-Cox models and b) a machine-learning method random survival forests (RSF). Models were trained on a training dataset and evaluated on a test dataset over 100 replicates. Parsimonious models of <10 variables were also developed using Lasso-Cox., Results: Over a median follow-up of 5.6 years, study outcome incidence in the cohort of 433,272 patients ranged from 1.8% for knee replacement to 11.7% for atherosclerotic cardiovascular disease. Harrell C-index averaged over replicates ranged from 0.702 for liver outcomes to 0.896 for death for RSF, and from 0.694 for liver outcomes to 0.891 for death for Lasso-Cox. The Harrell C-index for parsimonious models ranged from 0.675 for liver outcomes to 0.850 for knee replacement., Conclusions: Predictive modeling can identify patients at high risk of obesity-related complications. Interpretable Cox models achieve results close to those of machine learning methods and could be helpful for population health management and clinical treatment decisions., Competing Interests: Turchin reports equity in Brio Systems, consulting for Novo Nordisk and Proteomics International, and research support from Astra‐Zeneca, Eli Lilly and Company and Novo Nordisk. Lipkovich, Shinde, Ahmad and Kan are employees and stockholders of Eli Lilly and Company. None of the other authors report any conflicts of interest., (© 2023 The Authors. Obesity Science & Practice published by World Obesity and The Obesity Society and John Wiley & Sons Ltd.)

There has been much interest in the evaluation of heterogeneous treatment effects (HTE) and multiple statistical methods have emerged under the heading of personalized/precision medicine combining ideas from hypothesis testing, causal inference, and machine learning over the past 10-15 years. We discuss new ideas and approaches for evaluating HTE in randomized clinical trials and observational studies using the features introduced earlier by Lipkovich, Dmitrienko, and D'Agostino that distinguish principled methods from simplistic approaches to data-driven subgroup identification and estimating individual treatment effects and use a case study to illustrate these approaches. We identified and provided a high-level overview of several classes of modern statistical approaches for personalized/precision medicine, elucidated the underlying principles and challenges, and compared findings for a case study across different methods. Different approaches to evaluating HTEs may produce (and actually produced) highly disparate results when applied to a specific data set. Evaluating HTE with machine learning methods presents special challenges since most of machine learning algorithms are optimized for prediction rather than for estimating causal effects. An additional challenge is in that the output of machine learning methods is typically a "black box" that needs to be transformed into interpretable personalized solutions in order to gain acceptance and usability.

In this paper, we consider randomized controlled clinical trials comparing two treatments in efficacy assessment using a time to event outcome. We assume a relatively small number of candidate biomarkers available in the beginning of the trial, which may help define an efficacy subgroup which shows differential treatment effect. The efficacy subgroup is to be defined by one or two biomarkers and cut-offs that are unknown to the investigator and must be learned from the data. We propose a two-stage adaptive design with a pre-planned interim analysis and a final analysis. At the interim, several subgroup-finding algorithms are evaluated to search for a subgroup with enhanced survival for treated versus placebo. Conditional powers computed based on the subgroup and the overall population are used to make decision at the interim to terminate the study for futility, continue the study as planned, or conduct sample size recalculation for the subgroup or the overall population. At the final analysis, combination tests together with closed testing procedures are used to determine efficacy in the subgroup or the overall population. We conducted simulation studies to compare our proposed procedures with several subgroup-identification methods in terms of a novel utility function and several other measures. This research demonstrated the benefit of incorporating data-driven subgroup selection into adaptive clinical trial designs., (© 2022 John Wiley & Sons Ltd.)