Selection of Fusion-Site Overhang Sets for High-Fidelity and High-Complexity Golden Gate Assembly.

Golden Gate Assembly depends on the accurate ligation of overhangs at fragment fusion sites to generate full-length products with all parts in the desired order. Traditionally, fusion-site sequences are selected by using validated sets of overhang sequences or by applying a handful of semi-empirical rules to guide overhang choice. While these approaches allow dependable assembly of 6-8 fragments in one pot, recent work has demonstrated that comprehensive measurement of ligase fidelity allows prediction of high-fidelity junction sets that enable much more complex assemblies of 12, 24, or even 36+ fragments in a single reaction that will join with high accuracy and efficiency. In this chapter, we outline the application of a set of online tools that apply these comprehensive datasets to the analysis of existing junction sets, the de novo selection of new high-fidelity overhang sets, the modification and expansion of existing sets, and the principles for dividing known sequences at an arbitrary number of high-fidelity breakpoints.

Electrochemical biosensors for early detection of breast cancer.

Breast cancer continues to be a significant contributor to global cancer deaths, particularly among women. This highlights the critical role of early detection and treatment in boosting survival rates. While conventional diagnostic methods like mammograms, biopsies, ultrasounds, and MRIs are valuable tools, limitations exist in terms of cost, invasiveness, and the requirement for specialized equipment and trained personnel. Recent shifts towards biosensor technologies offer a promising alternative for monitoring biological processes and providing accurate health diagnostics in a cost-effective, non-invasive manner. These biosensors are particularly advantageous for early detection of primary tumors, metastases, and recurrent diseases, contributing to more effective breast cancer management. The integration of biosensor technology into medical devices has led to the development of low-cost, adaptable, and efficient diagnostic tools. In this framework, electrochemical screening platforms have garnered significant attention due to their selectivity, affordability, and ease of result interpretation. The current review discusses various breast cancer biomarkers and the potential of electrochemical biosensors to revolutionize early cancer detection, making provision for new diagnostic platforms and personalized healthcare solutions.

The Evolution of Nucleic Acid-Based Diagnosis Methods from the (pre-)CRISPR to CRISPR era and the Associated Machine/Deep Learning Approaches in Relevant RNA Design.

Nucleic acid tests (NATs) are considered as gold standard in molecular diagnosis. To meet the demand for onsite, point-of-care, specific and sensitive, trace and genotype detection of pathogens and pathogenic variants, various types of NATs have been developed since the discovery of PCR. As alternatives to traditional NATs (e.g., PCR), isothermal nucleic acid amplification techniques (INAATs) such as LAMP, RPA, SDA, HDR, NASBA, and HCA were invented gradually. PCR and most of these techniques highly depend on efficient and optimal primer and probe design to deliver accurate and specific results. This chapter starts with a discussion of traditional NATs and INAATs in concert with the description of computational tools available to aid the process of primer/probe design for NATs and INAATs. Besides briefly covering nanoparticles-assisted NATs, a more comprehensive presentation is given on the role CRISPR-based technologies have played in molecular diagnosis. Here we provide examples of a few groundbreaking CRISPR assays that have been developed to counter epidemics and pandemics and outline CRISPR biology, highlighting the role of CRISPR guide RNA and its design in any successful CRISPR-based application. In this respect, we tabularize computational tools that are available to aid the design of guide RNAs in CRISPR-based applications. In the second part of our chapter, we discuss machine learning (ML)- and deep learning (DL)-based computational approaches that facilitate the design of efficient primer and probe for NATs/INAATs and guide RNAs for CRISPR-based applications. Given the role of microRNA (miRNAs) as potential future biomarkers of disease diagnosis, we have also discussed ML/DL-based computational approaches for miRNA-target predictions. Our chapter presents the evolution of nucleic acid-based diagnosis techniques from PCR and INAATs to more advanced CRISPR/Cas-based methodologies in concert with the evolution of deep learning (DL)- and machine learning (ml)-based computational tools in the most relevant application domains.

Instrumentos de avaliação multidimensional de fragilidade para uso no cuidado de idosos com doença cardiovascular / Multidimensional frailty assessment tools for use in the care of older adults with cardiovascular disease / Instrumentos de evaluación multidimensional de la fragilidad en la atención de adultos mayores con enfermedad cardiovascular

Objetivo: identificar quais os instrumentos disponíveis para avaliação multidimensional da fragilidade em idosos com doença cardiovascular, potencialmente aplicáveis durante a realização do Processo de Enfermagem. Método: revisão sistemática conduzida em oito bases de dados/portais, para identificação de estudos que apresentassem instrumentos multidimensionais de avaliação de fragilidade em idosos com doença cardiovascular e que fossem aplicáveis ao processo de enfermagem. Resultados: foram incluídos 19 instrumentos multidimensionais. O Brief Frailty Index for Coronary Artery Disease foi desenvolvido para uso no cuidado cardiovascular de idosos. O Frailty Index for Adults e o Maastricht Frailty Screening Tool for Hospitalized Patients foram desenvolvidos para uso no Processo de Enfermagem. Conclusão: apesar de apenas um instrumento ter sido desenvolvido para o idosos com doença cardiovascular e apenas dois serem aplicáveis ao processo de enfermagem, a maioria deles tem potencial de adaptação e validação para uso nesta população durante a avaliação de enfermagem.

Objective: to identify which tools are available for multidimensional frailty assessment of older adult with cardiovascular disease and which are potentially applicable during the Nursing Process. Method: a systematic review conducted in eight databases/portals to identify studies that presented multidimensional frailty assessment tools for older adult with cardiovascular disease and that were applicable to the nursing process. Results: a total of 19 multidimensional tools were included. The Brief Frailty Index for Coronary Artery Disease was developed for use in the cardiovascular care of older adult. The Frailty Index for Adults and the Maastricht Frailty Screening Tool for Hospitalized Patients were developed for use in the Nursing Process. Conclusion: although only one tool was developed for older adults with cardiovascular disease and only two are applicable to the nursing process, most of them have the potential to be adapted and validated for use in this population during nursing assessment.

Objetivo: identificar qué instrumentos están disponibles para la evaluación multidimensional de la fragilidad en personas mayores con enfermedad cardiovascular, que se puedan aplicar en el Proceso de Enfermería. Método: revisión sistemática realizada en ocho bases de datos/portales, para identificar estudios que presentaran instrumentos multidimensionales para la evaluación de la fragilidad en adultos mayores con enfermedad cardiovascular y que fueran aplicables al proceso de enfermería. Resultados: se incluyeron 19 instrumentos multidimensionales. El Brief Frailty Index for Coronary Artery Disease se desarrolló para usarlo en el cuidado cardiovascular de las personas mayores. El Frailty Index for Adults y la Maastricht Frailty Screening Tool for Hospitalized Patients se elaboraron para ser usados en el Proceso de Enfermería. Conclusión: aunque sólo se elaboró un instrumento para adultos mayores con enfermedad cardiovascular y sólo dos son aplicables al proceso de enfermería, la mayoría de ellos tienen el potencial para ser adaptados y validados para ser usados en esa población en la evaluación de enfermería.

Pharmacy students' perception and knowledge of chat-based artificial intelligence tools at a Nigerian University.

BACKGROUND: Chat-based Artificial Intelligence (AI) tools, such as ChatGPT®, are becoming integral to various aspects of pharmacy education. However, their integration into the curriculum faces challenges due to students' varying levels of knowledge and perceptions. This study aimed to evaluate pharmacy students' knowledge and perception of chat-based AI tools at Afe Babalola University, Ado-Ekiti, Nigeria (ABUAD). It also assessed their familiarity with these tools and their usage patterns. METHOD: A cross-sectional online survey was conducted from March to April 2024 among undergraduate pharmacy students, selected through random sampling. Student knowledge was categorised as good or poor while perception was grouped into positive or negative. Data analysis was conducted using Statistical Product and Service Solutions version 27. RESULTS: A total of 252 students participated in this study with the majority being female (72.2%). Most students (88%, n = 222) were familiar with chat-based AI tools, with ChatGPT® being the most commonly used (82.8%) for assignments and studying. Students generally showed a positive perception of the tools, with 85.3% believing it enhances academic performance. Concerns were raised about potential distractions (65.7%) and the risk of academic dishonesty (65.1%). Students with prior AI education (p < 0.001), higher levels of study (p = 0.011), and prior awareness (p < 0.001) demonstrated significantly higher knowledge scores. CONCLUSION: Pharmacy students at ABUAD demonstrated good knowledge of chat-based AI tools and generally positive perceptions towards its use. The study underscores the need to integrate AI education into the pharmacy curriculum to address knowledge gaps and better prepare students for future technological advancements.

FBXW7 as a Factor of the African American and White Breast Cancer Racial Disparity.

INTRODUCTION: African American women have a breast cancer mortality rate 40% higher than Caucasian women. Many contributing factors account for this racial disparity, such as socioeconomic status and the age when women give birth, but even after considering such factors, studies have found that the racial disparity persists, suggesting that genetic factors may play a crucial role in this breast cancer racial inequality. METHODS: This study utilizes the All of Us database, The Cancer Genome Atlas (TCGA), and an array of bioinformatics tools to integrate differential mutation and gene expression analyses, aiming to identify genes potentially associated with this racial disparity. Although previous studies have identified genes associated with this breast cancer racial disparity through mutation or gene expression analysis, no studies have considered both simultaneously. Ultimately, this study considers both mutation and gene expression to discover novel genes linked to this racial disparity. RESULTS: After mutation analysis, this study identified FBXW7, a gene involved in the destruction of oncogenic proteins, as being associated with this racial inequality. FBXW7  was the only gene that presented differences in both mutation frequency and gene expression between African Americans and Caucasians. The other four candidate genes, such as COL12A1, whose upregulation plays a critical role in tumor progression, may also be linked to this racial inequality. CONCLUSION: By combining both mutation and gene expression analysis, this research offers a unique perspective into this issue. Furthermore, the identification of FBXW7 provides insight into this racial disparity, which can contribute to the pursuit of more effective or personalized treatment for both Caucasian and African American breast cancer patients. Finally, the multi-level method presented could possibly apply to other racial disparities, providing a distinctive perspective that cannot be found with other methods.

Kid-Sized Dentistry: A Pioneering First-of-Its-Kind Study on Customizing Dental Tools and Technology for Pediatric Care.

Introduction At the core of pediatric dentistry lies a profound understanding of child development, behavior management, and tailored treatment modalities. Similarly, tailoring dental tools and technology to address the distinct requirements of children is an essential aspect of pediatric dental practice. Kid-sized dentistry is the customization of dental tools and technology to effectively meet the unique needs of children. This study is the first to comprehensively explore all dimensions of resizing dental tools and technology for children, synthesizing scattered literature on the topic into a unified analysis, a trailblazing contribution to the field. Therefore, the aim of this article is to address these research gaps by conducting both a comprehensive analysis and a cross-sectional study. Methods To gather information for the subject, we conducted searches using both free-text terms and specific MeSH terms. We searched for articles published in English up to July 2024 across various electronic databases. Utilizing keywords, we initially obtained approximately 25-30 relevant literature studies, from which we selected 15-20 which were more topic-specific. We conducted an original survey to assess the knowledge, attitude, and practices of pediatric and general dentists regarding kid-sized dentistry in the Jabalpur zone. This survey employed a validated, structured questionnaire designed to collect data comprehensively. Results Despite high awareness, there remains a gap in the actual use of these tools and technology, particularly among general dentists. Conclusion In the realm of dentistry, every specialty brings a unique lens through which patient care is viewed. Within this mosaic, the pediatric dentist offers a particularly invaluable perspective, one that is grounded in the delicate nuances of treating children. Pediatric dentistry, in particular, focuses on the specialized needs of children, with an emphasis on understanding child development, behavior management, and customized treatment approaches. Central to this field is the adaptation of dental tools and technologies to suit pediatric patients. Insights from pediatric clinicians have contributed to advancements in dental equipment, improving its suitability for children. Increasing awareness of these innovations among both general and pediatric dentists can enhance this progress. This study underscores the need for ongoing education and advocacy to ensure all dental professionals have access to the best tools for treating young patients.

Methanothermobacter thermautotrophicus and Alternative Methanogens: Archaea-Based Production.

Methanogenic archaea convert bacterial fermentation intermediates from the decomposition of organic material into methane. This process has relevance in the global carbon cycle and finds application in anthropogenic processes, such as wastewater treatment and anaerobic digestion. Furthermore, methanogenic archaea that utilize hydrogen and carbon dioxide as substrates are being employed as biocatalysts for the biomethanation step of power-to-gas technology. This technology converts hydrogen from water electrolysis and carbon dioxide into renewable natural gas (i.e., methane). The application of methanogenic archaea in bioproduction beyond methane has been demonstrated in only a few instances and is limited to mesophilic species for which genetic engineering tools are available. In this chapter, we discuss recent developments for those existing genetically tractable systems and the inclusion of novel genetic tools for thermophilic methanogenic species. We then give an overview of recombinant bioproduction with mesophilic methanogenic archaea and thermophilic non-methanogenic microbes. This is the basis for discussing putative products with thermophilic methanogenic archaea, specifically the species Methanothermobacter thermautotrophicus. We give estimates of potential conversion efficiencies for those putative products based on a genome-scale metabolic model for M. thermautotrophicus.

Are risk assessment tools more accurate than unstructured judgments in predicting violent, any, and sexual offending? A meta-analysis of direct comparison studies.

We conducted a pre-registered meta-analysis of studies that directly compared the predictive validity of risk assessment tools to unstructured judgments of risk for violent, any, or sexual offending. A total of 31 studies, containing 169 effect sizes from 45,673 risk judgments, met inclusion criteria. Based on the results of three-level mixed-effects meta-regression models, the predictive validity of total scores on risk assessment tools was significantly higher than that of unstructured judgments for predictions of violent, any, and sexual offending. Tools continued to outperform unstructured judgments after accounting for risk of bias. This finding was also robust to variations in population, assessment context, and outcome measurement. Although this meta-analysis provides support for the use of risk assessment tools, it also highlights limitations and gaps that future research should address.

Teaching Medical Students to Communicate Risks Like Military Intelligence Analysts.

Communication about health often involves descriptions of risk: the probability or likelihood of an unfavorable outcome. Communicating risk helps individuals make choices about their own health by building understanding of potential outcomes and providing context for the importance of procedures, health interventions, and lifestyle choices. However, medical education in the United States does not provide future physicians with adequate statistical literacy to communicate risk effectively and rarely encourages them to practice communicating risk in pre-clinical years. Risk communication in military intelligence, a field with formalized risk language and training, offers a unique perspective into potential improvements for medical risk communication. With backgrounds in the military, public health, communication, surgery, and medical education, the authors offer the following recommendations to improve risk communication for medical students. (1) Encourage the use of numerical absolute risk when communicating among health practitioners to avoid varied interpretations of what different risk descriptors ("uncommon," "likely," or "low") might mean; (2) build efficient, teachable skills in use of patient-facing risk communication tools like comparative probabilities and visual aids; and (3) practice estimating risk through role-play of risk communication between medical students and standardized patients. By improving risk communication in medical education, future doctors will be better equipped to build trust through open communication and improve the health of the patients and the communities for whom they care.

Effectiveness of digital health interventions to increase cardiorespiratory fitness: A systematic review and meta-analysis.

Background: Interventions using commercial digital health tools do favorably affect health outcomes. However, the effect of digital tools on cardiorespiratory fitness, a more novel indicator cardiovascular risk, is unclear. Purpose: Synthesize the digital health intervention literature and answer the following question: What is the effect of interventions using mobile health apps, wearable activity trackers, and/or text messaging on cardiorespiratory fitness? Methods: A systematic review and a meta-analysis (PROSPERO CRD42023423925) were conducted to evaluate the immediate digital health intervention effect on adult cardiorespiratory fitness. In March 2023, a search of databases Embase, MEDLINE, CINHAL, and Cochrane Library was completed. Studies were included if the intervention used a mobile health app, text messaging, and/or activity tracker. Studies were excluded if an objective measure of fitness was not used; the sample included children; the setting was hospital-based; and the digital health technology was only used for data collection or described as virtual reality. Using a random-effects model, two separate meta-analyses were completed: one for single-group studies and one for multi-group studies. Standardized mean difference effect sizes (Cohen's d) were calculated. Study quality was evaluated with the Cochrane Risk of Bias tool and ROBINS-I tool. Results: Fifty-three studies (3657 individuals) with pre-post designs (12 single-group, 41 multi-group) were included. Most studies targeted participants with a specific chronic health condition. Digital health interventions in the single-group studies had a moderate-to-large effect size (d = 0.62, 95% confidence interval (CI) [0.41-0.84], p < 0.001), and multi-group studies had small-to-moderate effect size (d = 0.38, 95% CI 0.21-0.55, p < 0.001). Significant heterogeneity of effects was observed in both the single-group and multi-group studies. Conclusions: Interventions using text messaging, a mobile app, or activity tracker alone or in combination are effective in improving cardiorespiratory fitness in adults, particularly for those with a chronic health condition.

Evaluation of the specificity of the 2023 European Society of Cardiology classification for infective endocarditis.

BACKGROUND: The 2023 Duke-ISCVID and 2023 ESC classifications have recently issued independent diagnostic criteria for infective endocarditis (IE), updating the 2015 ESC criteria. OBJECTIVES: The specificity of the 2023 ESC criteria should be evaluated and compared to the two other classifications in IE suspected patients. METHODS: We retrospectively collected the characteristics of patients hospitalised in Bichat University Hospital, in 2021, who had been evaluated for suspicion of IE, and in whom IE diagnosis was finally rejected. All were classified by 2015 ESC, 2023 Duke-ISCVID, and 2023 ESC. RESULTS: In total 130 patients were analysed. Mean age was 62 years, 64.6% were male, 30.0% had prosthetic cardiac valve or valve repair, 16.2% had cardiac implanted electronic device, and 23.1% other cardiac conditions. Overall, 2, 5 and 5 patients were falsely classified as definite IE with the 2015 ESC, 2023 Duke-ISCVID and 2023 ESC criteria, respectively. The corresponding specificities were 99% (95% CI [94%; 100%], 96% (95% CI [91%; 99%]), and 96% (95% CI [91%; 99%]). CONCLUSION: The 2023 ESC and the 2023 Duke-ISCVID criteria are highly specific, although slightly less than the 2015 ESC criteria, for ruling out the diagnosis of definite IE.HIGHLIGHTS2023 Duke-ISCVID and 2023 ESC criteria are recently issued diagnostic classifications2023 ESC criteria have an excellent specificity, equivalent to the 2023 Duke-ISCVID one2023 ESC criteria and the 2023 Duke-ISCVID are less specific than the 2015 ESC criteriaSpecificities were quite similar according to the nature of the cardiac valve (native or prosthetic valve) or the duration of antibiotic therapy.

How should climate actions be planned? Model lessons from published action plans.

To effectively protect against the increasingly pervasive effects of climate change, countries and cities around the world are tasked with formulating and implementing climate actions that effectively respond to the challenges ahead. However, choosing the optimal climate actions is complex, since it is necessary to consider many external impacts as early on as the planning phase. Our novel methodology uncovers and integrates into first-of-its-kind decision support framework the identified climate actions of 443 European cities (from 32 countries) and the city structure-related features that influence the basic success of strategy creation into a first-of-its-kind decision support framework. Depending on their budget, population density, development and energy consumption portfolio, the results highlight that the analyzed European cities need to adopt a different way of thinking. The research results lay the foundation for the decision support of evidence-based climate action planning and contribute towards strengthening the role of cities worldwide in the fight against climate change in the future.

Comparing the performance of multiple trigger tools in identifying medication-related hospital readmissions.

BACKGROUND: Multiple trigger tools have been developed to identify medication-related hospital (re)admissions (MRRs); however, the accuracy of these tools in real-world clinical practice is uncertain. The objective of this study was to compare the accuracy of four different trigger tools (OPERAM, STOPP/START criteria, ADR-tool, and QUADRAT) to identify MRRs compared with clinical adjudication. METHODS: We conducted a secondary analysis of patients readmitted within 30 days to seven departments of a teaching hospital. In the primary study, which involved a retrospective chart review of 1111 readmissions, MRRs and their potential preventability were clinically adjudicated by physicians and pharmacists. In the current study, four trigger tools were applied by a different physician and pharmacist panel. Patients of all ages were included. Trigger tools included both explicit items specifying the event and the associated medication and implicit items requiring clinical knowledge. The accuracy of each trigger tool was assessed by calculating the proportion of clinically adjudicated MRRs each tool identified overall as well as according to explicit and implicit triggers separately. The accuracy of each tool to identify potentially preventable MRRs was also calculated. RESULTS: Of 1111 readmissions, 181 were adjudicated as medication-related (mean age 69 years, 56% male); 72 (40%) MRRs were potentially preventable. The original OPERAM tool identified 166 (92%) MRRs (62% through explicit triggers). The STOPP/START criteria identified 23 (13%, 7% through explicit triggers), the ADR tool identified 51 (28%, all explicit triggers), and the QUADRAT tool identified 76 (42%; all explicit triggers) MRRs. Of the 72 potentially preventable MRRs, OPERAM identified 59 (82%), STOPP/START identified 18 (25%), ADR identified 20 (28%), and QUADRAT identified 21 (29%). CONCLUSION: The original OPERAM tool identified the highest proportion of (preventable) MRRs. However, this tool includes many implicit triggers requiring expert clinical knowledge. Future studies should assess the practicality of implementing this tool in daily practice.

Making the most of big qualitative datasets: a living systematic review of analysis methods.

Introduction: Qualitative data provides deep insights into an individual's behaviors and beliefs, and the contextual factors that may shape these. Big qualitative data analysis is an emerging field that aims to identify trends and patterns in large qualitative datasets. The purpose of this review was to identify the methods used to analyse large bodies of qualitative data, their cited strengths and limitations and comparisons between manual and digital analysis approaches. Methods: A multifaceted approach has been taken to develop the review relying on academic, gray and media-based literature, using approaches such as iterative analysis, frequency analysis, text network analysis and team discussion. Results: The review identified 520 articles that detailed analysis approaches of big qualitative data. From these publications a diverse range of methods and software used for analysis were identified, with thematic analysis and basic software being most common. Studies were most commonly conducted in high-income countries, and the most common data sources were open-ended survey responses, interview transcripts, and first-person narratives. Discussion: We identified an emerging trend to expand the sources of qualitative data (e.g., using social media data, images, or videos), and develop new methods and software for analysis. As the qualitative analysis field may continue to change, it will be necessary to conduct further research to compare the utility of different big qualitative analysis methods and to develop standardized guidelines to raise awareness and support researchers in the use of more novel approaches for big qualitative analysis. Systematic review registration: https://osf.io/hbvsy/?view_only=.

Predicting Functional Outcomes Following Dysvascular Lower Limb Amputation: An Evidence Review of Personalizing Patient Outcomes.

Most research on people undergoing lower limb amputations for dysvascular disease summarizes average patient outcome risks and average associations between patient factors and these outcomes. More recently, the importance of predicting patient-specific outcomes based on individual factors (ie, personalized rehabilitation) has become evident. This article reviews the evidence and discusses the importance of the following: (1) predicting outcomes to facilitate amputation-level and prosthesis prescription decisions and (2) how prediction models can be leveraged to develop decision support tools to facilitate provider/patient shared decision-making to ensure decisions considering each individual patient's priorities and preferences. Examples of these tools are discussed and referenced.

The Problems (and possible solutions) of assessing risk, race and recidivism in long operating drug treatment courts.

Formal criminogenic risk tools can be an important control in assessing racial inequities in access to treatment courts and in evaluating both proximal and distal outcomes from those programs. To achieve this potential, however, it is important that risk tools themselves operate in a racially neutral fashion and that they operate consistently over the period assessed. Tools that are not properly calibrated by race and changes in the tools used over the life of a program are therefore significant evaluation concerns. Our paper is the first to assess the adequacy of an important risk-needs instrument, the LSI-R, across racial groups in a drug treatment court setting. The main contribution of the current study is not as a test of that instrument, which has been widely studied in other settings. Rather, because two different criminogenic risk tools were used over the study time period, we took this opportunity to explore the use of a readily constructible "proxy" measure of risk to support analysis of risk and race interactions over the life of the program.

Tools for screening maternal mental health conditions in primary care settings in sub-Saharan Africa: systematic review.

Introduction: In sub-Saharan Africa, pregnant and postpartum women with mental health problems are often missed in healthcare systems. To address this, a practical and simple screening tool for maternal mental health should be available to primary healthcare workers. An important step toward having such a tool is to assess the existing tools and their effectiveness in primary care settings. Methods: We systematically searched PubMed, LILAC, CINAHL, Google Scholar, African Index Medicus, HINARI, and African Journals Online from inception to 31 January 2023, without language restriction. Reference lists of retrieved articles were reviewed and experts in the field were contacted for studies not captured by our searches. All retrieved records were collated in Endnote, de-duplicated, and exported to Rayyan for screening. Study selection and data extraction were done by at least two reviewers using a pre-tested flow chart and data extraction form. Disagreements between reviewers were resolved through discussion. We contacted primary authors for missing or insufficient information and conducted a content analysis of the psychometric properties of the tools. Results: In total, 1,181 studies were retrieved by our searches, of which 119 studies were included in this review. A total of 74 out of 119 studies (62%) were screened for depression during pregnancy and or the postpartum period. The Edinburg Postpartum Depression Scale (EPDS) and the Patient Health Questionnaire (PHQ-9) were the most commonly used tools. In total, 12 studies reported specificity and sensitivity for tools for measuring depression (EPDS, PHQ-9, and Whooley) and psychological distress [Self Report Questionnaire (SRQ) and Kessler Psychological Distress Scale (KPDS)]. The average sensitivity and specificity of the EPDS reported were 75.5 and 76.5%, respectively, at a cut-off of ≥13. The EPDS appears to be the most acceptable, adaptable, user-friendly, and effective in screening for maternal mental health conditions during pregnancy and postpartum. However, the methodological approach varied for a particular tool, and documentation on the attributes was scanty. Conclusion: The EPDS was the most commonly used tool and considered as most acceptable, adaptable, user-friendly, and effective. Information on the performance and psychometric properties of the vast majority of screening tools was limited. Systematic review registration: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022323558, identifier CRD42022323558 (PROSPERO).