Approaches and advances in the genetic causes of autoimmune disease and their implications.

Genome-wide association studies are transformative in revealing the polygenetic basis of common diseases, with autoimmune diseases leading the charge. Although the field is just over 10 years old, advances in understanding the underlying mechanistic pathways of these conditions, which result from a dense multifactorial blend of genetic, developmental and environmental factors, have already been informative, including insights into therapeutic possibilities. Nevertheless, the challenge of identifying the actual causal genes and pathways and their biological effects on altering disease risk remains for many identified susceptibility regions. It is this fundamental knowledge that will underpin the revolution in patient stratification, the discovery of therapeutic targets and clinical trial design in the next 20 years. Here we outline recent advances in analytical and phenotyping approaches and the emergence of large cohorts with standardized gene-expression data and other phenotypic data that are fueling a bounty of discovery and improved understanding of human physiology.

Tackling psychosocial and capital constraints to alleviate poverty.

Many policies attempt to help extremely poor households build sustainable sources of income. Although economic interventions have predominated historically1,2, psychosocial support has attracted substantial interest3-5, particularly for its potential cost-effectiveness. Recent evidence has shown that multi-faceted 'graduation' programmes can succeed in generating sustained changes6,7. Here we show that a multi-faceted intervention can open pathways out of extreme poverty by relaxing capital and psychosocial constraints. We conducted a four-arm randomized evaluation among extremely poor female beneficiaries already enrolled in a national cash transfer government programme in Niger. The three treatment arms included group savings promotion, coaching and entrepreneurship training, and then added either a lump-sum cash grant, psychosocial interventions, or both the cash grant and psychosocial interventions. All three arms generated positive effects on economic outcomes and psychosocial well-being, but there were notable differences in the pathways and the timing of effects. Overall, the arms with psychosocial interventions were the most cost-effective, highlighting the value of including well-designed psychosocial components in government-led multi-faceted interventions for the extreme poor.

A p85α-osteopontin axis couples the receptor ICOS to sustained Bcl-6 expression by follicular helper and regulatory T cells.

Follicular helper T cells (TFH cells) and follicular regulatory T cells (TFR cells) regulate the quantity and quality of humoral immunity. Although both cell types express the costimulatory receptor ICOS and require the transcription factor Bcl-6 for their differentiation, the ICOS-dependent pathways that coordinate their responses are not well understood. Here we report that activation of ICOS in CD4(+) T cells promoted interaction of the p85α regulatory subunit of the signaling kinase PI(3)K and intracellular osteopontin (OPN-i), followed by translocation of OPN-i to the nucleus, its interaction with Bcl-6 and protection of Bcl-6 from ubiquitin-dependent proteasome degradation. Post-translational protection of Bcl-6 by OPN-i was essential for sustained responses of TFH cells and TFR cells and regulation of the germinal center B cell response to antigen. Thus, the p85α-OPN-i axis represents a molecular bridge that couples activation of ICOS to Bcl-6-dependent functional differentiation of TFH cells and TFR cells; this suggests new therapeutic avenues to manipulate the responses of these cells.

Fair algorithms for selecting citizens' assemblies.

Globally, there has been a recent surge in 'citizens' assemblies'1, which are a form of civic participation in which a panel of randomly selected constituents contributes to questions of policy. The random process for selecting this panel should satisfy two properties. First, it must produce a panel that is representative of the population. Second, in the spirit of democratic equality, individuals would ideally be selected to serve on this panel with equal probability2,3. However, in practice these desiderata are in tension owing to differential participation rates across subpopulations4,5. Here we apply ideas from fair division to develop selection algorithms that satisfy the two desiderata simultaneously to the greatest possible extent: our selection algorithms choose representative panels while selecting individuals with probabilities as close to equal as mathematically possible, for many metrics of 'closeness to equality'. Our implementation of one such algorithm has already been used to select more than 40 citizens' assemblies around the world. As we demonstrate using data from ten citizens' assemblies, adopting our algorithm over a benchmark representing the previous state of the art leads to substantially fairer selection probabilities. By contributing a fairer, more principled and deployable algorithm, our work puts the practice of sortition on firmer foundations. Moreover, our work establishes citizens' assemblies as a domain in which insights from the field of fair division can lead to high-impact applications.

Environmental and genetic activation of a brain-adipocyte BDNF/leptin axis causes cancer remission and inhibition.

Cancer is influenced by its microenvironment, yet broader, environmental effects also play a role but remain poorly defined. We report here that mice living in an enriched housing environment show reduced tumor growth and increased remission. We found this effect in melanoma and colon cancer models, and that it was not caused by physical activity alone. Serum from animals held in an enriched environment (EE) inhibited cancer proliferation in vitro and was markedly lower in leptin. Hypothalamic brain-derived neurotrophic factor (BDNF) was selectively upregulated by EE, and its genetic overexpression reduced tumor burden, whereas BDNF knockdown blocked the effect of EE. Mechanistically, we show that hypothalamic BDNF downregulated leptin production in adipocytes via sympathoneural beta-adrenergic signaling. These results suggest that genetic or environmental activation of this BDNF/leptin axis may have therapeutic significance for cancer.

Limiting Overdistention or Collapse When Mechanically Ventilating Injured Lungs: A Randomized Study in a Porcine Model.

Rationale: It is unknown whether preventing overdistention or collapse is more important when titrating positive end-expiratory pressure (PEEP) in acute respiratory distress syndrome (ARDS). Objectives: To compare PEEP targeting minimal overdistention or minimal collapse or using a compromise between collapse and overdistention in a randomized trial and to assess the impact on respiratory mechanics, gas exchange, inflammation, and hemodynamics. Methods: In a porcine model of ARDS, lung collapse and overdistention were estimated using electrical impedance tomography during a decremental PEEP titration. Pigs were randomized to three groups and ventilated for 12 hours: PEEP set at ⩽3% of overdistention (low overdistention), ⩽3% of collapse (low collapse), and the crossing point of collapse and overdistention. Measurements and Main Results: Thirty-six pigs (12 per group) were included. Median (interquartile range) values of PEEP were 7 (6-8), 11 (10-11), and 15 (12-16) cm H2O in the three groups (P < 0.001). With low overdistension, 6 (50%) pigs died, whereas survival was 100% in both other groups. Cause of death was hemodynamic in nature, with high transpulmonary vascular gradient and high epinephrine requirements. Compared with the other groups, pigs surviving with low overdistension had worse respiratory mechanics and gas exchange during the entire protocol. Minimal differences existed between crossing-point and low-collapse animals in physiological parameters, but postmortem alveolar density was more homogeneous in the crossing-point group. Inflammatory markers were not significantly different. Conclusions: PEEP to minimize overdistention resulted in high mortality in an animal model of ARDS. Minimizing collapse or choosing a compromise between collapse and overdistention may result in less lung injury, with potential benefits of the compromise approach.

Revealing determinants of translation efficiency via whole-gene codon randomization and machine learning.

It has been known for decades that codon usage contributes to translation efficiency and hence to protein production levels. However, its role in protein synthesis is still only partly understood. This lack of understanding hampers the design of synthetic genes for efficient protein production. In this study, we generated a synonymous codon-randomized library of the complete coding sequence of red fluorescent protein. Protein production levels and the full coding sequences were determined for 1459 gene variants in Escherichia coli. Using different machine learning approaches, these data were used to reveal correlations between codon usage and protein production. Interestingly, protein production levels can be relatively accurately predicted (Pearson correlation of 0.762) by a Random Forest model that only relies on the sequence information of the first eight codons. In this region, close to the translation initiation site, mRNA secondary structure rather than Codon Adaptation Index (CAI) is the key determinant of protein production. This study clearly demonstrates the key role of codons at the start of the coding sequence. Furthermore, these results imply that commonly used CAI-based codon optimization of the full coding sequence is not a very effective strategy. One should rather focus on optimizing protein production via reducing mRNA secondary structure formation with the first few codons.

Long-Term Effect of Randomization to Calcium and Vitamin D Supplementation on Health in Older Women : Postintervention Follow-up of a Randomized Clinical Trial.

BACKGROUND: Although calcium and vitamin D (CaD) supplementation may affect chronic disease in older women, evidence of long-term effects on health outcomes is limited. OBJECTIVE: To evaluate long-term health outcomes among postmenopausal women in the Women's Health Initiative CaD trial. DESIGN: Post hoc analysis of long-term postintervention follow-up of the 7-year randomized intervention trial of CaD. (ClinicalTrials.gov: NCT00000611). SETTING: A multicenter (n = 40) trial across the United States. PARTICIPANTS: 36 282 postmenopausal women with no history of breast or colorectal cancer. INTERVENTION: Random 1:1 assignment to 1000 mg of calcium carbonate (400 mg of elemental calcium) with 400 IU of vitamin D3 daily or placebo. MEASUREMENTS: Incidence of colorectal, invasive breast, and total cancer; disease-specific and all-cause mortality; total cardiovascular disease (CVD); and hip fracture by randomization assignment (through December 2020). Analyses were stratified on personal supplement use. RESULTS: For women randomly assigned to CaD versus placebo, a 7% reduction in cancer mortality was observed after a median cumulative follow-up of 22.3 years (1817 vs. 1943 deaths; hazard ratio [HR], 0.93 [95% CI, 0.87 to 0.99]), along with a 6% increase in CVD mortality (2621 vs. 2420 deaths; HR, 1.06 [CI, 1.01 to 1.12]). There was no overall effect on other measures, including all-cause mortality (7834 vs. 7748 deaths; HR, 1.00 [CI, 0.97 to 1.03]). Estimates for cancer incidence varied widely when stratified by whether participants reported supplement use before randomization, whereas estimates on mortality did not vary, except for CVD mortality. LIMITATION: Hip fracture and CVD outcomes were available on only a subset of participants, and effects of calcium versus vitamin D versus joint supplementation could not be disentangled. CONCLUSION: Calcium and vitamin D supplements seemed to reduce cancer mortality and increase CVD mortality after more than 20 years of follow-up among postmenopausal women, with no effect on all-cause mortality. PRIMARY FUNDING SOURCE: National Heart, Lung, and Blood Institute of the National Institutes of Health.

Mass spectrometry-based metabolomics: a guide for annotation, quantification and best reporting practices.

Mass spectrometry-based metabolomics approaches can enable detection and quantification of many thousands of metabolite features simultaneously. However, compound identification and reliable quantification are greatly complicated owing to the chemical complexity and dynamic range of the metabolome. Simultaneous quantification of many metabolites within complex mixtures can additionally be complicated by ion suppression, fragmentation and the presence of isomers. Here we present guidelines covering sample preparation, replication and randomization, quantification, recovery and recombination, ion suppression and peak misidentification, as a means to enable high-quality reporting of liquid chromatography- and gas chromatography-mass spectrometry-based metabolomics-derived data.

Semiparametric Allocation of Subjects to Cohort Strata.

BACKGROUND: We illustrate a method for stratum assignment in small cohort studies that avoids modeling assumptions. METHODS: Off-the-shelf software ( rgenoud ) made stratum assignments to minimize a loss function built on within-stratum and population-adjusted Euclidean distances. RESULTS: In 100 trials using simulated data of 300 records with a binary treatment and four dissimilar covariate treatment predictors, minimizing a loss based on Euclidean distance reduced covariate imbalance by a median of 99%. Stratification by propensity score and weighting records by the inverse of their probability of treatment reduced imbalance by 76%-89% and 83%-94%, respectively. Loss minimization applied to a cohort of 361 children undergoing immunotherapy achieved nearly complete elimination of covariate differences for important treatment predictors. CONCLUSION: With the availability of semiparametric stratum-assignment algorithms, analysts can tailor loss functions to meet design goals. Here, a loss function that emphasized covariate balance performed well under limited testing.

The causality between systemic inflammatory regulators and chronic respiratory diseases: A bidirectional Mendelian-randomization study.

INTRODUCTION: Accumulative evidence suggests the associations between systemic inflammatory regulators and chronic respiratory diseases (CRDs). However, the intrinsic causation remains implicit. Therefore, this study aimed to examine causative associations by mendelian randomization (MR) and to identify valuable active factors. METHODS: Based on data from the GWAS database, we performed MR analyses of 41 serum cytokines from 8,293 Finnish and European descent cohorts from GBMI and UKBB for five major CRDs. We mainly applied inverse variance weighted regression, supplemented by MR-Egger regression, weighted median, maximum likelihood, weighted mode, and simple mode algorithms. Moreover, sensitivity analyses were conducted using Cochrane's Q test, MR-Egger intercept, MR-PRESSO Global test and MR-Steiger filtering. Eventually, the consistency of MR results was assessed by leave-one-out. RESULTS: Our results suggest that 12 genetically predicted systemic inflammatory regulators probably participate in the progression of CRDs, including four risk factors (IL-1RA, IL-4, MIP-1A, PDGF-BB) and one protective factor (IL-6) in IPF, two protective factors (SCF, SDF-1A) in COPD, and two protective factors (SCF, SDF-1A) in asthma, two protective factors (GROA, IL-2RA) were also included in asthma, whereas only one factor (HGF) was protective against bronchiectasis. Additionally, two protective factors (FGF-BASIC, G-CSF) were identified in sarcoidosis. Sensitivity analyses showed no horizontal pleiotropy and significant heterogeneity. Finally, based on the findings of inverse MR analysis, no inverse causal association was uncovered, confirming the robustness of results. CONCLUSION: Our study unearths potential associations between systemic inflammatory modulators and common CRDs, providing new insights for inflammation-mediated CRD prevention and therapeutic approaches.

Establishment of a risk prediction model for bowel preparation failure prior to colonoscopy.

BACKGROUND: This study aimed to determine the factors that contribute to the failure of bowel preparation in patients undergoing colonoscopy and to develop a risk prediction model. METHODS: A total of 1115 outpatients were included. Patients were randomly divided into two groups: the modeling group (669 patients) and the validation group (446 patients). In the modeling group, patients were further divided into two groups based on their success and failure in bowel preparation using the Boston Bowel Preparation Scale. A logistic regression analysis model was used to determine the risk factors of bowel preparation failure, which was subsequently visualized using an alignment diagram. RESULTS: After controlling for relevant confounders, multifactorial logistic regression results showed that age ≥ 60 years (OR = 2.246), male (OR = 2.449), body mass index ≥ 24 (OR = 2.311), smoking (OR = 2.467), chronic constipation (OR = 5.199), diabetes mellitus (OR = 5.396) and history of colorectal surgery (OR = 5.170) were influencing factors of bowel preparation failure. The area under the ROC curve was 0.732 in the modeling group and 0.713 in the validation group. According to the calibration plot, the predictive effect of the model and the actual results were in good agreement. CONCLUSIONS: Age ≥ 60 years, male, body mass index ≥ 24, smoking, chronic constipation, diabetes mellitus, and history of colorectal surgery are independent risk factors for bowel preparation failure. The established prediction model has a good predictive efficacy and can be used as a simple and effective tool for screening patients at high risk for bowel preparation failure.

Exploration of the relationship between tumor-infiltrating lymphocyte score and histological grade in breast cancer.

BACKGROUND: The histological grade is an important factor in the prognosis of invasive breast cancer and is vital to accurately identify the histological grade and reclassify of Grade2 status in breast cancer patients. METHODS: In this study, data were collected from 556 invasive breast cancer patients, and then randomly divided into training cohort (n = 335) and validation cohort (n = 221). All patients were divided into actual low risk group (Grade1) and high risk group (Grade2/3) based on traditional histological grade, and tumor-infiltrating lymphocyte score (TILs-score) obtained from multiphoton images, and the TILs assessment method proposed by International Immuno-Oncology Biomarker Working Group (TILs-WG) were also used to differentiate between high risk group and low risk group of histological grade in patients with invasive breast cancer. Furthermore, TILs-score was used to reclassify Grade2 (G2) into G2 /Low risk and G2/High risk. The coefficients for each TILs in the training cohort were retrieved using ridge regression and TILs-score was created based on the coefficients of the three kinds of TILs. RESULTS: Statistical analysis shows that TILs-score is significantly correlated with histological grade, and is an independent predictor of histological grade (odds ratio [OR], 2.548; 95%CI, 1.648-3.941; P < 0.0001), but TILs-WG is not an independent predictive factor for grade (P > 0.05 in the univariate analysis). Moreover, the risk of G2/High risk group is higher than that of G2/Low risk group, and the survival rate of patients with G2/Low risk is similar to that of Grade1, while the survival rate of patients with G2/High risk is even worse than that of patients with G3. CONCLUSION: Our results suggest that TILs-score can be used to predict the histological grade of breast cancer and potentially to guide the therapeutic management of breast cancer patients.

Effects of Positive End-expiratory Pressure on Pulmonary Perfusion Distribution and Intrapulmonary Shunt during One-lung Ventilation in Pigs: A Randomized Crossover Study.

BACKGROUND: During one-lung ventilation (OLV), positive end-expiratory pressure (PEEP) can improve lung aeration but might overdistend lung units and increase intrapulmonary shunt. The authors hypothesized that higher PEEP shifts pulmonary perfusion from the ventilated to the nonventilated lung, resulting in a U-shaped relationship with intrapulmonary shunt during OLV. METHODS: In nine anesthetized female pigs, a thoracotomy was performed and intravenous lipopolysaccharide infused to mimic the inflammatory response of thoracic surgery. Animals underwent OLV in supine position with PEEP of 0 cm H2O, 5 cm H2O, titrated to best respiratory system compliance, and 15 cm H2O (PEEP0, PEEP5, PEEPtitr, and PEEP15, respectively, 45 min each, Latin square sequence). Respiratory, hemodynamic, and gas exchange variables were measured. The distributions of perfusion and ventilation were determined by IV fluorescent microspheres and computed tomography, respectively. RESULTS: Compared to two-lung ventilation, the driving pressure increased with OLV, irrespective of the PEEP level. During OLV, cardiac output was lower at PEEP15 (5.5 ± 1.5 l/min) than PEEP0 (7.6 ± 3 l/min) and PEEP5 (7.4 ± 2.9 l/min; P = 0.004), while the intrapulmonary shunt was highest at PEEP0 (PEEP0: 48.1% ± 14.4%; PEEP5: 42.4% ± 14.8%; PEEPtitr: 37.8% ± 11.0%; PEEP15: 39.0% ± 10.7%; P = 0.027). The relative perfusion of the ventilated lung did not differ among PEEP levels (PEEP0: 65.0% ± 10.6%; PEEP5: 68.7% ± 8.7%; PEEPtitr: 68.2% ± 10.5%; PEEP15: 58.4% ± 12.8%; P = 0.096), but the centers of relative perfusion and ventilation in the ventilated lung shifted from ventral to dorsal and from cranial to caudal zones with increasing PEEP. CONCLUSIONS: In this experimental model of thoracic surgery, higher PEEP during OLV did not shift the perfusion from the ventilated to the nonventilated lung, thus not increasing intrapulmonary shunt.

On exact randomization-based covariate-adjusted confidence intervals.

Randomization-based inference using the Fisher randomization test allows for the computation of Fisher-exact P-values, making it an attractive option for the analysis of small, randomized experiments with non-normal outcomes. Two common test statistics used to perform Fisher randomization tests are the difference-in-means between the treatment and control groups and the covariate-adjusted version of the difference-in-means using analysis of covariance. Modern computing allows for fast computation of the Fisher-exact P-value, but confidence intervals have typically been obtained by inverting the Fisher randomization test over a range of possible effect sizes. The test inversion procedure is computationally expensive, limiting the usage of randomization-based inference in applied work. A recent paper by Zhu and Liu developed a closed form expression for the randomization-based confidence interval using the difference-in-means statistic. We develop an important extension of Zhu and Liu to obtain a closed form expression for the randomization-based covariate-adjusted confidence interval and give practitioners a sufficiency condition that can be checked using observed data and that guarantees that these confidence intervals have correct coverage. Simulations show that our procedure generates randomization-based covariate-adjusted confidence intervals that are robust to non-normality and that can be calculated in nearly the same time as it takes to calculate the Fisher-exact P-value, thus removing the computational barrier to performing randomization-based inference when adjusting for covariates. We also demonstrate our method on a re-analysis of phase I clinical trial data.

Robustness of response-adaptive randomization.

Doubly adaptive biased coin design (DBCD), a response-adaptive randomization scheme, aims to skew subject assignment probabilities based on accrued responses for ethical considerations. Recent years have seen substantial advances in understanding DBCD's theoretical properties, assuming correct model specification for the responses. However, concerns have been raised about the impact of model misspecification on its design and analysis. In this paper, we assess the robustness to both design model misspecification and analysis model misspecification under DBCD. On one hand, we confirm that the consistency and asymptotic normality of the allocation proportions can be preserved, even when the responses follow a distribution other than the one imposed by the design model during the implementation of DBCD. On the other hand, we extensively investigate three commonly used linear regression models for estimating and inferring the treatment effect, namely difference-in-means, analysis of covariance (ANCOVA) I, and ANCOVA II. By allowing these regression models to be arbitrarily misspecified, thereby not reflecting the true data generating process, we derive the consistency and asymptotic normality of the treatment effect estimators evaluated from the three models. The asymptotic properties show that the ANCOVA II model, which takes covariate-by-treatment interaction terms into account, yields the most efficient estimator. These results can provide theoretical support for using DBCD in scenarios involving model misspecification, thereby promoting the widespread application of this randomization procedure.

Sequential covariate-adjusted randomization via hierarchically minimizing Mahalanobis distance and marginal imbalance.

In comparative studies, covariate balance and sequential allocation schemes have attracted growing academic interest. Although many theoretically justified adaptive randomization methods achieve the covariate balance, they often allocate patients in pairs or groups. To better meet the practical requirements where the clinicians cannot wait for other participants to assign the current patient for some economic or ethical reasons, we propose a method that randomizes patients individually and sequentially. The proposed method conceptually separates the covariate imbalance, measured by the newly proposed modified Mahalanobis distance, and the marginal imbalance, that is the sample size difference between the 2 groups, and it minimizes them with an explicit priority order. Compared with the existing sequential randomization methods, the proposed method achieves the best possible covariate balance while maintaining the marginal balance directly, offering us more control of the randomization process. We demonstrate the superior performance of the proposed method through a wide range of simulation studies and real data analysis, and also establish theoretical guarantees for the proposed method in terms of both the convergence of the imbalance measure and the subsequent treatment effect estimation.

Doubly adaptive biased coin design to improve Bayesian clinical trials with time-to-event endpoints.

Clinical trialists often face the challenge of balancing scientific questions with other design features, such as improving efficiency, minimizing exposure to inferior treatments, and simultaneously comparing multiple treatments. While Bayesian response adaptive randomization (RAR) is a popular and effective method for achieving these objectives, it is known to have large variability and a lack of explicit theoretical results, making its use in clinical trials a subject of concern. It is desirable to propose a design that targets the same allocation proportion as Bayesian RAR and achieves the above objectives but addresses the concerns over Bayesian RAR. We propose the frequentist doubly adaptive biased coin designs (DBCD) targeting ethical allocation proportions from the Bayesian framework to satisfy different objectives in clinical trials with time-to-event endpoints. We derive the theoretical properties of the proposed adaptive randomization design and show through comprehensive numerical simulations that it can achieve ethical objectives without sacrificing efficiency. Our combined theoretical and numerical results offer a strong foundation for the practical use of RAR in real clinical trials.

Bayesian response adaptive randomization design with a composite endpoint of mortality and morbidity.

Allocating patients to treatment arms during a trial based on the observed responses accumulated up to the decision point, and sequential adaptation of this allocation, could minimize the expected number of failures or maximize total benefits to patients. In this study, we developed a Bayesian response-adaptive randomization (RAR) design targeting the endpoint of organ support-free days (OSFD) for patients admitted to the intensive care units. The OSFD is a mixture of mortality and morbidity assessed by the number of days of free of organ support within a predetermined post-randomization time-window. In the past, researchers treated OSFD as an ordinal outcome variable where the lowest category is death. We propose a novel RAR design for a composite endpoint of mortality and morbidity, for example, OSFD, by using a Bayesian mixture model with a Markov chain Monte Carlo sampling to estimate the posterior probability distribution of OSFD and determine treatment allocation ratios at each interim. Simulations were conducted to compare the performance of our proposed design under various randomization rules and different alpha spending functions. The results show that our RAR design using Bayesian inference allocated more patients to the better performing arm(s) compared to other existing adaptive rules while assuring adequate power and type I error rate control across a range of plausible clinical scenarios.

Steady-state statistical properties and implementation of randomization designs with maximum tolerated imbalance restriction for two-arm equal allocation clinical trials.

In recent decades, several randomization designs have been proposed in the literature as better alternatives to the traditional permuted block design (PBD), providing higher allocation randomness under the same restriction of the maximum tolerated imbalance (MTI). However, PBD remains the most frequently used method for randomizing subjects in clinical trials. This status quo may reflect an inadequate awareness and appreciation of the statistical properties of these randomization designs, and a lack of simple methods for their implementation. This manuscript presents the analytic results of statistical properties for five randomization designs with MTI restriction based on their steady-state probabilities of the treatment imbalance Markov chain and compares them to those of the PBD. A unified framework for randomization sequence generation and real-time on-demand treatment assignment is proposed for the straightforward implementation of randomization algorithms with explicit formulas of conditional allocation probabilities. Topics associated with the evaluation, selection, and implementation of randomization designs are discussed. It is concluded that for two-arm equal allocation trials, several randomization designs offer stronger protection against selection bias than the PBD does, and their implementation is not necessarily more difficult than the implementation of the PBD.