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Abstract Sexual dysfunction (SD) is prevalent in patients with mental health disorders and can significantly impair their quality of life. Early recognition of SD in a clinical setting may help patients and clinicians to optimize treatment options of SD and/or other primary diagnoses taking SD risk into account and may facilitate treatment compliance. SD identification is often overlooked in clinical practice; we seek to explore whether patients with a high risk of SD can be identified at the individual level by assessing known risk factors via a machine learning (ML) model. We assessed 135 subjects referred to a tertiary mental health clinic in a Western Canadian city using health records data, including age, sex, physician’s diagnoses, drug treatment, and the Arizona Sexual Experiences Scale (ASEX). A ML model was fitted to the data, with SD status derived from the ASEX as target outcomes and all other variables as predicting variables. Our ML model was able to identify individual SD cases—achieving a balanced accuracy of 0.736, with a sensitivity of 0.750 and a specificity of 0.721—and identified major depressive disorder and female sex as risk factors, and attention deficit hyperactivity disorder as a potential protective factor. This study highlights the utility of SD screening in a psychiatric clinical setting, demonstrating a proof-of-concept ML approach for SD screening in psychiatric patients, which has marked potential to improve their quality of life.

Background: : Bipolar disorder (BD) is a prevalent mental health illness with a direct impact on patient's well-being. Self-report-based BD screening questionnaires such as the Mood Disorder Questionnaire (MDQ) is economical and clinically validated. We use a machine-learning approach to test whether utilizing our composite screening application - EarlyDetect (ED), designed for assessing an array of mental health illness, can enhance bipolar disorder screening over MDQ. Methods: : This was a retrospective, naturalistic study at a tertiary mental health centre in western Canada. Participants (n = 955; 56.4% female; mean age 35.4; 18.7% BD) completed ED and underwent a clinical interview with a blinded psychiatrist for diagnostic accuracy. Elastic net and leave-one-out cross-validation was used to make more confident predictions at an individual level. Results: : Using composite scoring, the balanced accuracy of our tool was 80.6%, with a sensitivity of 73.7% and a specificity of 87.5%. Compared with the MDQ original scoring method, the fully composite ED model improved balanced accuracy by 6.9%, sensitivity by 14.5%, while maintaining specificity. Limitations: : Patients were assessed using clinical psychiatric evaluations, which are subjective. There is also the potential for self-reporting bias. BD subtypes were not differentiated. The cross-sectional design of this study rules out conclusions of causality. Conclusions: : Our results show improved BD detection accuracy using composite measures.

BackgroundThe Leprosy Post-Exposure Prophylaxis (LPEP) program explored the feasibility and impact of contact tracing and the provision of single dose rifampicin (SDR) to eligible contacts of newly diagnosed leprosy patients in Brazil, India, Indonesia, Myanmar, Nepal, Sri Lanka and Tanzania. As the impact of the programme is difficult to establish in the short term, we apply mathematical modelling to predict its long-term impact on the leprosy incidence.MethodologyThe individual-based model SIMCOLEP was calibrated and validated to the historic leprosy incidence data in the study areas. For each area, we assessed two scenarios: 1) continuation of existing routine activities as in 2014; and 2) routine activities combined with LPEP starting in 2015. The number of contacts per index patient screened varied from 1 to 36 between areas. Projections were made until 2040.Principal findingsIn all areas, the LPEP program increased the number of detected cases in the first year(s) of the programme as compared to the routine programme, followed by a faster reduction afterwards with increasing benefit over time. LPEP could accelerate the reduction of the leprosy incidence by up to six years as compared to the routine programme. The impact of LPEP varied by area due to differences in the number of contacts per index patient included and differences in leprosy epidemiology and routine control programme.ConclusionsThe LPEP program contributes significantly to the reduction of the leprosy incidence and could potentially accelerate the interruption of transmission. It would be advisable to include contact tracing/screening and SDR in routine leprosy programmes.

Summary: Background: Innovative approaches are required for leprosy control to reduce cases and curb transmission of Mycobacterium leprae. Early case detection, contact screening, and chemoprophylaxis are the most promising tools. We aimed to generate evidence on the feasibility of integrating contact tracing and administration of single-dose rifampicin (SDR) into routine leprosy control activities. Methods: The leprosy post-exposure prophylaxis (LPEP) programme was an international, multicentre feasibility study implemented within the leprosy control programmes of Brazil, India, Indonesia, Myanmar, Nepal, Sri Lanka, and Tanzania. LPEP explored the feasibility of combining three key interventions: systematically tracing contacts of individuals newly diagnosed with leprosy; screening the traced contacts for leprosy; and administering SDR to eligible contacts. Outcomes were assessed in terms of number of contacts traced, screened, and SDR administration rates. Findings: Between Jan 1, 2015, and Aug 1, 2019, LPEP enrolled 9170 index patients and listed 179 769 contacts, of whom 174 782 (97·2%) were successfully traced and screened. Of those screened, 22 854 (13·1%) were excluded from SDR mainly because of health reasons and age. Among those excluded, 810 were confirmed as new patients (46 per 10 000 contacts screened). Among the eligible screened contacts, 1182 (0·7%) refused prophylactic treatment with SDR. Overall, SDR was administered to 151 928 (86·9%) screened contacts. No serious adverse events were reported. Interpretation: Post-exposure prophylaxis with SDR is safe; can be integrated into different leprosy control programmes with minimal additional efforts once contact tracing has been established; and is generally well accepted by index patients, their contacts, and health-care workers. The programme has also invigorated local leprosy control through the availability of a prophylactic intervention; therefore, we recommend rolling out SDR in all settings where contact tracing and screening have been established. Funding: Novartis Foundation.

Microfluidic processes, mainly for biological and chemical applications, have expanded rapidly in recent years. While the initial focus was on single units, principally microreactors, technological and economic considerations have caused a shift to integrated microchips in which a number of microdevices function coherently. These integrated devices have many advantages over conventional macro-scale processes. However, the small scale of operation, complexities in the underlying physics and chemistry, and differences in the time constants of the participating units, in the interactions among them and in the outputs of interest make it difficult to design and optimize integrated microprocesses. These aspects are discussed here, current research and applications are reviewed, and possible future directions are considered.

Poly-β-hydroxybutyrate (PHB) is an energy-storage polymer whose properties are similar to those of chemical polymers such as polyethylene and polypropylene. Moreover, PHB is biodegradable, absorbed by human tissues and less energy-consuming than synthetic polymers. Although Ralstonia eutropha is widely used to synthesize PHB, it is inefficient in utilizing glucose and similar sugars. Therefore a co-culture of R. eutropha and Lactobacillus delbrueckii is preferred since the latter can convert glucose to lactate, which R. eutropha can metabolize easily. Tohyama et al. [24] maximized PHB production in a well-mixed fed-batch bioreactor with glucose and (NH4)2SO4 as the primary substrates. Since production-scale bioreactors often deviate from ideal laboratory-scale reactors, a large bioreactor was simulated by means of a dispersion model with the kinetics determined by Tohyama et al. [24] and dispersion set at an optimum Peclet number of 20 [32]. The time-dependent feed rates of the two substrates were determined through a genetic algorithm (GA) to maximize PHB production. This bioreactor produced 22.2% more PHB per liter and 12.8% more cell mass than achieved by Tohyama et al. [24]. These results, and similar observations with other fermentations, indicate the feasibility of enhancing the efficiency of large nonideal bioreactors through GA optimizations.

Large-scale fed-batch fermentations are often subject to noise carried by the feed streams. This noise corrupts the process data and may destabilize the fermentation. So it is important to retrieve clear signals from noisy data. This is done by noise filters. The performances of some commonly used filters have been studied for poly-β-hydroxybutyrate production by Ralstonia eutropha. In simulated experiments, Gaussian noise was added to the flow rates of the carbon and nitrogen substrates. The filters were compared by means of the Lyapunov exponents of the outputs and their closeness to the noise-free performance. Negative exponents indicate a stable fermentation. An auto-associative neural filter performed the best, followed by a combination of a cusum filter and an extended Kalman filter. Butterworth filters were inferior and inadequate.

Microbioreactors offer many advantages over macro-scale reactors for the screening and cultivation of microbial cultures. However, some weaknesses of microbioreactors are yet to be overcome. Optimum mixing under laminar flow, miniaturized on-line measurements and control, integration of upstream and downstream microdevices with the microbioreactor on a single chip, and adequate mathematical models to optimize the performance are some major issues that need to be resolved. Nevertheless, recent research indicates rapidly growing understanding of these problems and it has presented some promising solutions that have led to useful commercial applications. While specialized reviews have covered specific aspects of microbioprocesses exhaustively, the present article reviews microbioreactors per se in a holistic way, analyzing their functions both in a stand-alone setting and in conjunction with other microdevices. We also consider technological developments arising out of the standard microbioreactor and its possible future ramifications.

Microbioreactors offer many advantages over macro-scale reactors for the screening and cultivation of microbial cultures. However, some weaknesses of microbioreactors are yet to be overcome. Optimum mixing under laminar flow, miniaturized on-line measurements and control, integration of upstream and downstream microdevices with the microbioreactor on a single chip, and adequate mathematical models to optimize the performance are some major issues that need to be resolved. Nevertheless, recent research indicates rapidly growing understanding of these problems and it has presented some promising solutions that have led to useful commercial applications. While specialized reviews have covered specific aspects of microbioprocesses exhaustively, the present article reviews microbioreactors per se in a holistic way, analyzing their functions both in a stand-alone setting and in conjunction with other microdevices. We also consider technological developments arising out of the standard microbioreactor and its possible future ramifications.

Despite its superiority over chemically synthesized petroleum-based polymers, poly-?-hydroxybutyrate (PHB) has been less successful commercially. A prime reason is the low productivity of microbial processes for PHB. High fermentation efficiency requires good modelling and optimization. Neither classical mechanistic models nor the recent cybernetic models have resulted in sufficiently high yields of PHB. So a neural network description has been proposed here. Relative to the other two approaches, neural optimization doubled the maximum PHB concentration in fed-batch fermentation with Ralstonia eutropha, the most commonly employed organism for PHB production, and it consumed less of the substrates. This advantage and their model-free nature make neural networks an attractive technique to enhance PHB productivity.

Abstract Background Although the production of poly-β-hydroxybutyrate (PHB) has many biological, energetic and environmental advantages over chemically synthesized polymers, synthetic polymers continue to be produced industrially since the productivities of fermentation processes fr PHB are not yet economically competitive. Improvement of a PHB fermentation requires good understanding and optimization under the realistic conditions of large bioreactors. Laboratory-scale studies have shown that co-cultures of Ralstonia eutropha and Lactobacillus delbrueckii generate better fermentation efficiencies than R. eutropha alone. In large bioreactors, incomplete dispersioin and perturbations in the dissolved oxygen (DO) concentration, both of which affect the fermentation, have to be considered. This study analyzes the effect of DO fluctuations on bioreactor performance for both ideal and optimally dispersed broths. Results Response coefficient analysis was employed to obtain quantitative information on the effect of DO perturbations on different variables. Three values of the Peclet number (Pe) cheracterized three levels of dispersion: Pe = 0.01 for nearly complete dispersion, Pe = 20 for optimum dispersion and Pe = 60 for insufficient dispersion. The response coefficients (RCs) of the pairs of bacterial concentrations and the main substrates, glucose and ammonium chloride, showed contrasting variations with time. Lactate, a critical intermediate, and PHB had similar RC profiles but those of lactate were one to two orders of magnitude larger than other RCs. Significantly, the optimum Pe also resulted in the largest RCs, suggesting a balance between productivity and reactor stability. Conclusion Since R. eutropha requires oxygen for its growth whereas L. delbrueckii does not, fluctuations in the DO concentartion have a strong influence on the fermentation. Apart from this, the mechanism of PHB biosynthesis indicates that control of lactate is a critical determinant of fermentation efficiency. The RC profiles indicate that, under non-ideal conditions, a compromise may be required between PHB formation and reactor stability, especially in the latter half of the process.

Abstract Background For many microbial processes, the complexity of the metabolisms and the responses to transient and realistic conditions are difficult to capture in mechanistic models. The cells seem to have an innate intelligence that enables them to respond optimally to environmental changes. Some "intelligent" models have therefore been proposed and compared with a mechanistic model for fed-batch cultures of Ralstonia eutropha. Results Two kinds of models have been proposed to describe such cellular behavior. Cybernetic models are derived through postulates of cellular intelligence and memory, and neural models use artificial intelligence through neural networks. Some competing models of both kinds have been compared for their ability to portray and optimize the synthesis of poly-β-hydroxybutyrate by Ralstonia eutropha in fed-batch cultures with finite dispersion. Neural models enabled the formation of more of the polymer than cybernetic models, with lesser utilization of the carbon and nitrogen substrates. Both types of models were decidedly superior to a mechanistic model used as a reference, thus supporting the value of intelligent descriptions of microbial kinetics in incompletely dispersed bioreactors. Conclusion Neural and cybernetic models describe and optimize unsteady state fed-batch microbial reactors with finite dispersion more effectively than mechanistic models. However, these "intelligent" models too have weaknesses, and hence a hybrid approach combining such models with some mechanistic features is suggested.