Minimum Intervention Research Articles

Identification and Interpretation of Clinically Relevant Somatic Variants from Whole-Genome Sequencing Data

Whole-genome sequencing provides an opportunity for a comprehensive test enabling detection of clinically relevant genetic aberrations for patients with hematological malignancies. However in diagnostic settings, it is challenging to identify and interpret clinically relevant somatic single nucleotide variants (SNV), small insertions and deletions (INDEL), structural variants (SV) and copy-number variants (CNV) in a manner that retains the sensitivity of the analysis while at the same time enabling a rapid interpretation. SNV and INDEL are identified using the information from within the sequenced reads, and range in size smaller than 10 bp. Both SV and CNV are typically longer than 100 bp and are identified using different properties from aligned reads, which includes split reads, and orientation and distance between the read pairs. These aberrations are further divided into different categories depending upon their complexity and the method of identification, which include duplications (DUP), deletions (DEL), inversions (INV) and translocation breakpoints (BND). Although CNV is defined as an additional type of variant, the copy number gains and losses, identified using read-depth profile, can be categorized as DUP and DEL. All four types of SV arise due to complex rearrangements in the genome. Their inherent complexity combined with the methods used to identify them makes it difficult to obtain a comprehensive set of true biological events. Furthermore, identification of clinically relevant somatic variants requires further classification of all variants into germline and somatic using additional variant-type dependent processing and filtering. Finally, to enable rapid processing that meets the clinical diagnostic demands regarding turnaround times, the entire process needs to be automated with minimum human intervention from sample preparation and bioinformatic analysis all the way through clinical interpretation. To tackle these challenges, we implemented an ensemble variant calling approach in BALSAMIC. This involves combining computational methods and tools to identify SNV, INDEL and all four types of SV. These variants are then subjected to further bioinformatic processing and rigorous filtering to prepare them for clinical interpretation in our custom-developed variant interpretation software, Scout. The entire workflow is automated using in-house automation software, cg. BALSAMIC utilizes FASTQ files as input and first performs quality control and trimming using FastQC and fastp, respectively. Trimmed reads are mapped to the human reference genome using Sentieon-tools. Duplicated reads are marked and quality controlled using Picard-tools and the results are summarized by MultiQC. SNV and INDEL are called using Sentieon TNscope and TNhaplotyper. The variants are further processed using depth, quality and allele frequency based filters. The filtered SNV and INDEL from TNscope and Tnhaplotyper are merged using bcftools for tumor-only samples. Manta, Delly-SV, and TIDDIT are used to call SV while ascatNgs (tumor-normal) and Delly-CNV are used to call CNV. CNV calls are further converted from CNV to DEL and DUP to curate merged sets for each SV-type across the different SV-callers. The SV calls from Manta, Delly, TIDDIT, and ascatNgs (tumor-normal) are then merged using SVDB. Prior to annotation, SVs with low quality score in the tumor sample, high allelic frequency (possible germline) in the normal sample, or longer than 10Mb are removed. Next, SVs are annotated using Ensembl-VEP and the variants in the intragenic regions are removed. Finally, SVs are uploaded to Scout and those overlapping with clinically relevant genes (defined using an in silico gene list) constitute the final set of clinically relevant somatic SNV, INDEL, SV and CNV. This workflow has analyzed to date more than two hundred cases of different tumor types mainly including hematological malignancies. This approach identifies thousands of variants across all types, but with rigorous processing and filtering, the identification of clinically relevant somatic variants is done in a manner compatible with the clinical diagnostic demands. Software availability The bioinformatics software BALSAMIC, cg and Scout are available from https://github.com/Clinical-Genomics, and the details of the software and methods used in BALSAMIC are available from https://balsamic.readthedocs.io. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

Qutrit-Inspired Fully Self-Supervised Shallow Quantum Learning Network for Brain Tumor Segmentation.

Classical self-supervised networks suffer from convergence problems and reduced segmentation accuracy due to forceful termination. Qubits or bilevel quantum bits often describe quantum neural network models. In this article, a novel self-supervised shallow learning network model exploiting the sophisticated three-level qutrit-inspired quantum information system, referred to as quantum fully self-supervised neural network (QFS-Net), is presented for automated segmentation of brain magnetic resonance (MR) images. The QFS-Net model comprises a trinity of a layered structure of qutrits interconnected through parametric Hadamard gates using an eight-connected second-order neighborhood-based topology. The nonlinear transformation of the qutrit states allows the underlying quantum neural network model to encode the quantum states, thereby enabling a faster self-organized counterpropagation of these states between the layers without supervision. The suggested QFS-Net model is tailored and extensively validated on the Cancer Imaging Archive (TCIA) dataset collected from the Nature repository. The experimental results are also compared with state-of-the-art supervised (U-Net and URes-Net architectures) and the self-supervised QIS-Net model and its classical counterpart. Results shed promising segmented outcomes in detecting tumors in terms of dice similarity and accuracy with minimum human intervention and computational resources. The proposed QFS-Net is also investigated on natural gray-scale images from the Berkeley segmentation dataset and yields promising outcomes in segmentation, thereby demonstrating the robustness of the QFS-Net model.

Efficacy of Diet on Fatigue and Quality of Life in Multiple Sclerosis: A Systematic Review and Network Meta-analysis of Randomized Trials.

Emerging evidence suggests a role for diet in multiple sclerosis (MS) care; however, owing to methodological issues and heterogeneity of dietary interventions in preliminary trials, the current state of evidence does not support dietary recommendations for MS. The objective of this study was to assess the efficacy of different dietary approaches on MS-related fatigue and quality of life (QoL) through a systematic review of the literature and network meta-analysis (NMA). Electronic database searches were performed in May 2021. Inclusion criteria were (1) randomized trial with a dietary intervention, (2) adults with definitive MS based on McDonald criteria, (3) patient-reported outcomes for fatigue and/or QoL, and (4) minimum intervention period of 4 weeks. For each outcome, standardized mean differences (SMDs) were calculated and included in random effects NMA to determine the pooled effect of each dietary intervention relative to each of the other dietary interventions. The protocol was registered at PROSPERO (CRD42021262648). Twelve trials comparing 8 dietary interventions (low-fat, Mediterranean, ketogenic, anti-inflammatory, Paleolithic, fasting, calorie restriction, and control [usual diet]), enrolling 608 participants, were included in the primary analysis. The Paleolithic (SMD -1.27; 95% CI -1.81 to -0.74), low-fat (SMD -0.90; 95% CI -1.39 to -0.42), and Mediterranean (SMD -0.89; 95% CI -1.15 to -0.64) diets showed greater reductions in fatigue compared with control. The Paleolithic (SMD 1.01; 95% CI 0.40-1.63) and Mediterranean (SMD 0.47; 95% CI 0.08-0.86) diets showed greater improvements in physical QoL compared with control. For improving mental QoL, the Paleolithic (SMD 0.81; 95% CI 0.26-1.37) and Mediterranean (SMD 0.36; 95% CI 0.06-0.65) diets were more effective compared with control. However, the NutriGRADE credibility of evidence for all direct comparisons is very low because of most of the included trials having high or moderate risk of bias, small sample sizes, and the limited number of studies included in this NMA. Several dietary interventions may reduce MS-related fatigue and improve physical and mental QoL; however, because of the limitations of this NMA, which are driven by the low quality of the included trials, these findings must be confirmed in high-quality, randomized, controlled trials.

SLAP-IoD: Secure and Lightweight Authentication Protocol Using Physical Unclonable Functions for Internet of Drones in Smart City Environments

With the emergence of the concept of smart city and the increasing demands for a range of drones, Internet of Drones (IoD) has achieved a variety of attention by providing multiple benefits in academia and industry. IoD offers numerous services, such as traffic monitoring, environmental monitoring, and disaster management by combining infrastructure, Internet of Things (IoT), and Flying Ad-Hoc Networks (FANET) in smart city environments. However, communication among drones is vulnerable to potential security threats because the sensitive messages in various applications are exchanged via an insecure channel in IoD-based smart city environments. Since IoDs can be operated in unattended environment with minimum human interventions, smart devices (e.g., drones and sensors) deployed in IoD architectures are vulnerable to physical capture attacks. In addition, drones are resource-constrained in terms of computation and communication overheads, and it is not much viable to apply public key cryptography (PKC) that requires high computation and communication overheads. Thus, we design a secure and lightweight authentication protocol using a physical unclonable function (PUF) for IoD to guarantee reliable and useful services in smart city environments, called SLAP-IoD. We prove the security of the SLAP-IoD using informal and formal security analyses using the broadly recognized Real-Or-Random (ROR) random oracle model, and also through the formal security verification using the widely-accepted Automated Validation of Internet Security-sensitive Protocols and Applications (AVISPA) security verification. Furthermore, we compare the performance of the SLAP-IoD with related schemes. Consequently, we show that SLAP-IoD offers better security and efficiency than other related schemes and is suitable for IoD-based smart city environments.

The dentobiome and the minimum intervention in dentistry

All of us who observe the chilling figures of children with dental caries on a daily basis and more recently the more frequent presence of enamel alterations and among them incisor molar hypomineralization, we begin to think what really happens with that tissue considered the strongest. of the human body; however, it bows down to an acid attack resulting from bacterial metabolism. And the question arises: Could it be that the tooth is already born with a predisposition to develop certain microorganisms? This comprehensive review of the literature, summarizing the authors' perspectives; aims to explore the knowledge about the biome and apply it to the dental organ and make available the definition of dentobiome, as an appropriate term for dental flora. Starting from this knowledge, apply it to better understand today the execution of the philosophy of minimal intervention and the development of materials that must be biocompatible with the dental structure but must also prevent dysbiosis and establish homeostasis in the tooth.

Systematic Analysis of Ovarian Cancer Empowered with Machine and Deep Learning: A Taxonomy and Future Challenges

Machine and Deep learning has witnessed an exceptional amount of admiration in recent years. ML has ability to learn data itself by predicting uncertain conditions or future and classify categories with minimum intervention of human. While in DL, computers are able to automatically, learn useful features and representation precisely from raw data. ML and DL potentially a disruptive technology in predictive healthcare analysis. A detailed understanding and evaluation of the applications and principles of radiomics, machine and deep learning is an important task to construct possible solutions that are capable of accomplish compulsory and ethical requirements, which can enhance efficiency, quality and outcomes. Machine and deep learning extensively being use in medical image analysis, medical diagnostics and medical image technology. The wide scope and sudden progress of ML and DL has remarkably change the ways of diagnosis, prediction, classification and analyzing ovarian, lungs, brain, skin and various other types of cancer. In view of multiple applications of ML and DL, in this article a review conducted on ovarian cancer. This review comprises the detailed analysis of OC (prognosis, diagnosis, classification, evaluation) by covering all the major contributions of machine and deep learning. Furthermore, a literature taxonomy of the research conferred and emerging aspects analyzed. A section of discussion also signified to elaborate the limitations and future challenges of machine and deep learning comprises with ovarian cancer.

Challenges in the Future of Restorative Youth Justice in Ireland: Minimising Intervention, Maximising Participation

While restorative approaches encompass a small proportion of youth justice practices in Ireland, the new Youth Justice Strategy (2021–2027) aims to include more victims in restorative justice, expand family conferencing and train youth justice professionals in restorative practices. This article discusses the legal, policy and practice contexts of these developments, considering how Ireland has defined, used and researched restorative youth justice to date. It situates the ongoing efforts to meet victims’ and children’s needs, and to change criminal justice cultures, in the international criminological and legal literature on minimum intervention and child participation, analysing the possibilities and challenges facing restorative youth justice in Ireland.

Effectiveness of peer-led programs for overweight and obesity in children: systematic review and meta-analysis

Child health promotion has used peer-led interventions for decades, but their effectiveness for childhood obesity is unknown. This review assesses the effectiveness of peer-led interventions on child and adolescent obesity using a range of adiposity outcomes. We included studies that used a peer-led approach for delivering behavior change communications with a minimum intervention duration of four weeks. Studies needed to report results for any of the outcomes: BMI, BMI z-score or BMI percentile. The review included 14 studies of moderate to high quality from high-income countries. A meta-analysis involving 2506 children from 9 studies showed that programs were effective with a mean difference in BMI of -0.15 kg/m2 (95% confidence interval [-0.26, -0.03]), p = 0.01. Heterogeneity was low (I2 = 28%, p = 0.19) for children in the intervention group. The mean difference varied with subgroups with significantly greater effects from interventions that focused on physical activity alone or with longer duration of implementation. Sensitivity analysis showed similar significant findings to the primary meta-analysis. We found moderately strong evidence to support the advantageous effect of peer-led interventions for obesity prevention in children and adolescents. However, given the small number of studies included, and possible reporting bias, the results must be interpreted cautiously.

The Frequency of Tobacco Smoking and E-Cigarettes Use among Primary Health Care Patients-The Association between Anti-Tobacco Interventions and Smoking in Poland.

The aim of this study was to assess the prevalence of smoking and e-cigarette use among primary care patients during the COVID-19 pandemic and to assess the frequency of minimal anti-tobacco interventions by family doctors. A cross-sectional study was conducted from January 2020 to December 2021 encompassing 896 patients over 18 years of age who used primary health care in the city of Lodz, Poland. In total, 21.2% of the respondents were smokers, 11.6% were e-cigarette users, and 7.3% dual users. In addition, 68.4% of smokers had been asked about smoking, while 62.9% of non-smokers and 33.7% of smokers were advised to quit smoking; furthermore, 71.1% of e-cigarette users and 72.3% of dual users were asked about tobacco use, and 17.3% and 21.5%, respectively, had been advised to quit smoking. Multivariate logistic regression analysis found men and alcohol users to receive more minimal anti-tobacco advice than women and non-alcohol users (OR = 1.46; p < 0.05 and OR = 1.45; p < 0.05), socio-demographic and health correlates did not increase the chances of obtaining minimal anti-tobacco interventions among smokers. People with a medium level of education had a higher chance of receiving minimal anti-tobacco intervention from their family doctor when using e-cigarettes and when they were dual users (OR = 2.06; p < 0.05 and OR = 2.51; p < 0.05). Smokers were less likely to receive minimal anti-tobacco interventions than reported in previous studies. Measures should be implemented to increase the minimum interventions provided by GPs in their daily work among all patients, not only those who use tobacco. Non-smokers should be encouraged to abstain.

Algorithm for visualizing substation areas in electric power systems

Electric power distribution systems can be represented with mathematical graphs in which the hub nodes are substations receiving electric power from the transmission system and delivering it to the consumers via feeders consisting of transformers, junctions, cables and power lines. High-quality diagrams depicting these systems are key elements of efficient monitoring and control. Existing automatic diagram generation solutions are usually limited to drawing single feeders or contain significant simplifications of the underlying distribution system e.g., visualize multiple feeders with their interconnections removed. The goal of this research was to develop an algorithm capable of generating visually-pleasing diagrams of substation areas consisting of a single hub node and a complex network of thousands of interconnected elements. The resulting algorithm was defined as a sequence of transformations which takes a mathematical graph as input and generates a one-line diagram as output. Multiple novel approaches are incorporated to optimize the algorithm thereby allowing it to run efficiently on regular desktop computers. Also, a novel diagram quality evaluation procedure was developed to automatically measure and compare the usability of each generated diagram. The algorithm was tested on 2510 different substation areas of various complexity extracted from the network models of real-life, European distribution systems. The quality of the generated diagrams was measured with the novel evaluation procedure and verified with a human domain experts on a subset of the resulting diagrams. Based on these analyses, it was concluded that the generated diagrams are usable in newly built supervisory and control systems with minimum human intervention.

Intrinsic and extrinsic quality of data for open data repositories

This work assesses the quality of Internet of Things data not only as an intrinsic quality on how well it represents the related phenomenon but also, on how much information it contains to educate an artificial entity. The quality metrics here proposed are tested with real datasets. Also, they are implemented on OpenCPU, so the open data repositories can use them off-the-shelf to rate their datasets without computational cost and minimum human intervention, making them more attractive to potential users and gaining visibility and impact.

A virtual evaluation of options for managing risk of hospital congestion with minimum intervention

Hospital congestion is a common problem for the healthcare sector. However, existing approaches including hospital resource optimization and process improvement might lead to huge cost of human and physical structure changes. This study evaluated less disruptive interventions based on a hospital simulation model and offer objective reasoning to support hospital management decisions. This study tested a congestion prevention method that estimates hospital congestion risk level (R), and activates minimum intervention when R is above certain threshold, using a virtual hospital created by simulation modelling. The results indicated that applying a less disruptive intervention is often enough, and more cost effective, to reduce the risk level of hospital congestion. Moreover, the virtual implementation approach enabled testing of the method at a more detailed level, thereby revealed interesting findings difficult to achieve theoretically, such as discharging extra two medical inpatients, rather than surgical inpatients, a day earlier on days when R is above the threshold, would bring more benefits in terms of congestion reduction for the hospital.

Walking Promotion in Pregnancy and Its Effects on Insomnia: Results of Walking_Preg Project (WPP) Clinical Trial.

Introduction: Insomnia is a frequent condition during pregnancy. The aim of this study was to assess if a walking promotion program from the 12th Gestational Week (GW) of pregnancy helps to prevent insomnia and improve the quality of sleep at third trimester. Materials and Methods: A prospective, randomized, and controlled trial was conducted with 270 pregnant women divided into 3 groups in parallel: maximum intervention group, I1 (pedometer and goal of 10,000 steps/day), minimum intervention group, I2 (pedometer without a goal), and control group (no intervention). All groups received recommendations about physical activity in pregnancy. A structured interview was performed at 13th, 20th, and 32nd GW, collecting pedometer mean steps/day, Athens Insomnia Scale (AIS), and Pittsburgh questionnaire (PSQI). Lineal regression models were conducted to determine the association between mean steps/day at 31st GW and AIS or PSQI score. Results: At 19th GW, groups I1 and I2 reached a mean of 6267 steps/day (SD = 3854) and 5835 steps/day (SD = 2741), respectively (p > 0.05). At 31st GW mean steps/day was lower for I2 (p < 0.001). Insomnia and poor sleep quality prevalence increased through pregnancy, but no differences between groups, within trimesters, were found (p > 0.05). Lineal regression showed no association between the average steps/day at third trimester of pregnancy and AIS and PSQI scores. Conclusions: Our walking promotion program based on pedometers did not help to prevent insomnia in the third trimester of pregnancy.

Physical activity and exercise training in cystic fibrosis.

Physical activity and exercise training in cystic fibrosis.

In situ quality monitoring in direct energy deposition process using co-axial process zone imaging and deep contrastive learning

Many strategic industrial sectors prefer Directed Energy Deposition (DED) to other Additive Manufacturing (AM) technologies due to the high material deposition and build rates. However, the inadvertent formation of defects such as porosity, micro-cracks and microstructure anomalies hinders its adoption in industries that require specific mechanical and microstructural properties. These defects are caused by undesirable fluctuations in process conditions such as material flow rate, laser power, melt pool dynamics, environment gas composition, temperature gradients. This research proposes in situ quality monitoring of DED using images of process zone and contrastive learning-based Convolutional Neural Network (CNN). Experiments included deposition of titanium powder (Cp-Ti, grade 1) with the particle size ranging between 45 and 106 μm on the base plate (99.6 % Ti6Al4V grade 1), forming a cube geometry. The process parameters were tuned to achieve six quality grades. The video of the process zone was recorded co-axially to the laser beam during the entire manufacturing, which was eventually used as the input to train CNN's based on contrastive losses. An in situ monitoring strategy for classifying the different quality grades was demonstrated in a supervised and semi-supervised manner, with an accuracy ranging between 89 % and 97 %. The performance of the developed framework was compared to an alternative clustering technique, namely t-distributed stochastic neighbour embedding, justifying the efficiency of our approach. The developed methodology demonstrates the possibility to track workpiece manufacturing quality using simple CCD cameras with minimum interventions on the commercial machines.

A Systematic Review on the Effect of Saffron Extract on Lipid Profile in Hyperlipidaemic Experimental Animal Models.

Saffron is widely used in traditional medicine to treat various medical disorders, including hyperlipidaemia. This study aims to systematically review the effects of saffron extract (SE) on lipid profile in in vivo studies. A strategic literature search was done following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The Scopus, PubMed (MEDLINE) and Web of Science databases and hand-searching methods were utilised to identify studies published up to January 2020 that reported the effects of SE on lipid profile in a hyperlipidaemic experimental animal model. A total of six articles met the inclusion criteria. The methods of extraction were aqueous (n = 4), ethanolic (n = 1) and hydroalcoholic (n = 1) extracts. Five doses of SE ranging from 10 mg/kg to 100 mg/kg were administered to rats and hamsters, with a duration ranging from 10 days to 8 weeks. SE at doses of 40 mg/kg/day and 80 mg/kg/day significantly decreased the levels of total cholesterol (21.4%–35.4%), low-density lipoproteins (38.7%–50.0%) and triglycerides (TGs) (29.1%–45.0%) and markedly increased the level of high-density lipoproteins (36.6%–65%) in the treated group compared to the untreated group with a minimum 3-week intervention duration (P < 0.05). This systematic review demonstrated that SE exhibits hypolipidaemic effects compared to a placebo. SE has almost the same ability to reduce cholesterol levels as the standard therapy.

Ranking the effectiveness of non-pharmaceutical interventions to counter COVID-19 in UK universities with vaccinated population

Several universities around the world have resumed in-person teaching after successful vaccination campaigns have covered 70/80% of the population. In this study, we combine a new compartmental model with an optimal control formulation to discover, among different non-pharmaceutical interventions, the best prevention strategy to maximize on-campus activities while keeping spread under control. Composed of two interconnected Susceptible-Exposed-Infected-Quarantined-Recovered (SEIQR) structures, the model enables staff-to-staff infections, student-to-staff cross infections, student-to-student infections, and environment-to-individual infections. Then, we model input variables representing the implementation of different non-pharmaceutical interventions and formulate and solve optimal control problems for four desired scenarios: minimum number of cases, minimum intervention, minimum non-quarantine intervention, and minimum quarantine intervention. Our results reveal the particular significance of mask wearing and social distancing in universities with vaccinated population (with proportions according to UK data). The study also reveals that quarantining infected students has a higher importance than quarantining staff. In contrast, other measures such as environmental disinfection seems to be less important.

Searching for the urine osmolality surrogate: an automated machine learning approach.

Automated machine learning (AutoML) tools can help clinical laboratory professionals to develop machine learning models. The objective of this study was to develop a novel formula for the estimation of urine osmolality using an AutoML tool and to determine the efficiency of AutoML tools in a clinical laboratory setting. Three hundred routine urinalysis samples were used for reference osmolality and urine clinical chemistry analysis. The H2O AutoML engine completed the machine learning development steps with minimum human intervention. Four feature groups were created, which include different urinalysis measurements according to the Boruta feature selection algorithm. Method comparison statistics including Spearman's correlation, Passing-Bablok regression analysis were performed, and Bland Altman plots were created to compare model predictions with the reference method. The minimum allowable bias (24.17%) from biological variation data was used as the limit of agreement. The AutoML engine developed a total of 183ML models. Conductivity and specific gravity had the highest variable importance. Models that include conductivity, specific gravity, and other urinalysis parameters had the highest R2 (0.70-0.83), and 70-84% of results were within the limit of agreement. Combining urinary conductivity with other urinalysis parameters using validated machine learning models can yield a promising surrogate. Additionally, AutoML tools facilitate the machine learning development cycle and should be considered for developing ML models in clinical laboratories.

A Non-Intrusive Approach for Enhancing Power-System Frequency Stability

This paper presents a novel protection strategy for assuring power-system frequency stability. After many years of use, the existing (static) under-frequency load-shedding concept has proven to be reliable and fast. However, it does not adapt well to volatile operating conditions, which might often lead to a system collapse. This developed into a major obstacle, since operational conditions of modern power systems are persistently subjected to fast and unpredictable changes. Therefore, there is a need to introduce a new capability in under-frequency load-shedding that would be able to fine-tune the mismatch in active power in any conditions. From the practical aspect, a systematic implementation of improvements should not threaten system stability. In terms of cost, a minimum intervention in the existing concept is desirable. This is why we developed an effective upgrade of the existing concept by introducing a specialized group of intelligent electronic devices supported by a principal component analysis technique. This group should be operated alongside the existing solution to monitor the actual frequency conditions and to take over power-balancing when fine-tuning is required. This supports the existing load-shedding and improves the process of frequency stabilization.

Achievement of a 136-day delayed-interval delivery of a second twin with minimum intervention

The rate of multiple pregnancies in the past three decades has increased dramatically. Twin pregnancies have a higher risk of pregnancy loss owing to premature birth. Although the premature delivery of the first twin is usually followed by spontaneous birth of the second twin, there are ways to delay the delivery of the second twin to enhance survival and neonatal outcomes. However, there is insufficient evidence to support the role of any interventions such as cerclage, hospitalization, tocolytics, and/or antibiotics. As a result, the management of delayed interval for the delivery of the second twin varies depending on each case. The purpose of this study was to report the obstetric components of a successful delayed-interval delivery, with an emphasis on the current conflicts around the active management of the delayed interval delivery of the second twin. A case of a 40-year-old, G5P4, woman who had three cesarean sections is presented. She got pregnant with a dichorionic diamniotic twin. At 17 weeks of gestation, she developed sudden severe lower abdominal pain, vaginal bleeding, and spontaneous expulsion of the first fetus weighing 179 g. After delivery of the first twin, the vaginal bleeding became minimal, and the cervix was reconstituted soon, so cervical cerclage was not offered. The umbilical cord of the first fetus was cut at the level of the external cervical os, and the placenta was retained. She received intravenous fluids, anti-D prophylaxis, paracetamol, and antibiotics. The patient was keen to continue with the pregnancy and was informed about the risks and benefits. The management was mainly an outpatient basis after an initial short hospital stay of 6 days before being discharged (upon her request); during the follow-up, she did not develop any clinical manifestations of maternal infection. She was subjected to ANC at the outpatient clinic every 2 weeks, where vaginal culture, complete blood count, prothrombin time, and C-reactive protein levels and serial ultrasonography were checked consistently. The fetus showed adequate growth. After 136 days, she delivered by elective cesarean section a male baby weighing 2850 g at 37 weeks of gestation with good recovery. In certain situations, delayed delivery of the second twin of diamniotic dichorionic twin pregnancy may be a safe, simple with less interventions, and effective option to improve pregnancy outcome.