Screening Procedure Research Articles

Image Enhancement Technique Based on Deep Learning and Hybrid Particle Swarm Optimization for Lung Tumor Classification

Lung cancer is one of the leading causes of death in the modern world. The survival rate is low due to the difficulties in detecting lung cancer in its later stages, when symptoms are present. Consequently, it is essential to recognize issues early. Cancer detection research throughout the whole body is a formidable challenge. Detecting lung cancers is a top priority. Here, computed tomography (CT) images are considered for the identification of lung cancers; other screening procedures include X-ray, CT, and sputum cytology. Computed tomography has a wide variety of applications in the medical field. Early detection and treatment may increase survival rates, and CT scans are the best approach to examine a lung tumor. The illness may have advanced or reached an untreatable stage when nodules are discovered. You should pay close attention to the nodule's size, tumor kind, and border type when you examine it physically. The importance of detecting and treating lung cancer early cannot be overstated. Machine learning classification might be greatly enhanced by delving into the wealth of research on image processing for lung cancer detection. Thanks to advancements in picture segmentation, it is now possible to more readily distinguish between different kinds of tumors and track the evolution of diseases. While watershed and area growth segmentation did provide encouraging results when applied to lung cancer CT images, a two-level segmentation approach could provide even more precise outcomes. It is also possible to employ support vector machines and neural networks to identify the subtype of lung cancer. By including a PSO algorithm that optimizes hyper parameters and filters into the CNN model, feature extraction may be made more adaptive. Our approach takes on tumor form and picture variability by combining the optimization capabilities of PSO with the strong feature learning of deep learning

Understanding diagnostic delays among newly diagnosed breast cancer patients at a tertiary cancer care center in a low-middle-income country like Bangladesh.

Breast cancer poses a significant health challenge for women globally, with survival rates varying widely between and within countries. The correlation between delayed diagnosis and lower survival rates is well-established, emphasizing the importance of addressing barriers to early presentation to improve survival rates in low-middle-income countries like Bangladesh. The objective of the study was to assess how often breast cancer patients present late, pinpoint the underlying reasons for the delay, and examine any potential links between delayed presentation and socio-demographic factors in our context. A prospective cross-sectional study was conducted at Ahsania Mission Cancer and General Hospital from July 2023 to December 2023. The study focused on newly diagnosed breast cancer patients attending the outpatient department. Following the provision of consent, the patient's information was obtained through face-to-face interviews and a review of their medical records. Diagnostic delay was defined as 90 days or more from the onset of symptoms to the initiation of medical treatment (by expert physicians). The collected data was stratified based on various socio-demographic variables, including age, marital status, education status, and socio-economic status. Data analysis and visualization were performed using Microsoft Excel and SPSS V 25. The study involved 242 participants; the majority (126, 52.06%) were aged 41 to 60 and came from a low-income, uneducated social background. Stage II breast cancer was the most common presentation (137, 56.6%). The average duration from diagnosis was 5.18 months, and nearly half of the patients (112, 46.28%) experienced more than 3 months of diagnostic delay. The delay in diagnosis was significantly associated with the patient's socio-economic status and the stage of cancer. The primary cause of delayed diagnosis was a lack of awareness about breast cancer symptoms, particularly the painless nature of breast lumps, which patients either did not notice or did not consider serious enough to seek medical attention, followed by the initial pursuit of alternative treatments. To facilitate earlier breast cancer diagnosis, policies should prioritize community-based education programs and adequate screening procedures, as well as expanded healthcare access.

Screening Procedures for Self‐Reactive Substances, Differential Scanning Calorimetry‐Onset and Heat‐Flux Criteria

AbstractWe present a new screening procedure for the determination of the thermal stability of potential candidates for the class of self‐reactive substances. For the classification of self‐reactive substances, elaborate testing is necessary to determine the applicable type, according to the regulations. Annex 6 of the UN Manual of Tests and Criteria permits that the classification procedure for self‐reactive substances may be omitted, if the estimated self accelerating decomposition temperature (SADT) is greater than 75 °C for a 50 kg package using an appropriate calorimetric technique, but no technique is described. Based on stationary theories of thermal explosion, we developed a screening approach using differential scanning calorimetry (DSC) onset and heat‐flux criteria, respectively, at relevant temperatures. The proposed approach is validated with experimental data.

Virtual Colonoscopy: Retrospective Comparison of the Findings in Supine and Prone Positions.

Virtual Colonoscopy: Retrospective Comparison of the Findings in Supine and Prone Positions.

Can screening and decontamination procedures performed on an outpatient basis reduce colonization with Staphylococcus aureus and mitigate associated complications in patients undergoing elective hospital procedures? A controlled intervention study (STAUfrei).

Can screening and decontamination procedures performed on an outpatient basis reduce colonization with Staphylococcus aureus and mitigate associated complications in patients undergoing elective hospital procedures? A controlled intervention study (STAUfrei).

Serum Lactate Dehydrogenase Levels Reflect the Lung Injury Extension in COVID-19 Patients at Hospital Admission.

Several hematological and biochemical parameters have been related to the COVID-19 infection severity and outcomes. However, less is known about clinical indicators reflecting lung involvement of COVID-19 patients at hospital admission. Computed tomography (CT) represents an established imaging tool for the detection of lung injury, and the quantitative analysis software CALIPER has been used to assess lung involvement in COVID-19 patients. Herein, the relationship between the lung involvement expressed by CALIPER interstitial lung disease (ILD) percentage and a set of blood parameters related to tissue oxygenation and damage in COVID-19 patients at hospital admission was evaluated. We performed a retrospective and a prospective study involving 321 and 75, respectively, COVID-19-positive patients recruited from Pisa University Hospital. The association between CALIPER ILD percentages and selected blood parameters was investigated by a regression tree approach, after multiple imputations of the dataset missing values. High serum lactate dehydrogenase (LDH) values appeared to be predictive of high CALIPER ILD percentages at hospital admission in both retrospective and prospective datasets, even if the predictive performance of the algorithm was not optimal. LDH levels could be evaluated as a tool for early identification of COVID-19 patients at risk of extensive lung injury, as well as in fast screening procedures before hospitalization.

Post-Stroke Cognitive Impairment: A Narrative Review of the Comprehensive Screening and Detecting Process.

To examine screening procedures and tools for post-stroke cognitive impairment (PSCI) to guide future practices and research. Searches in PubMed, CINAHL, PsycINFO, and Google Scholar included articles from 2013 to 2023 focusing on individuals with first ever ischemic stroke and confirming PSCI within 1 year. Thematic analysis was synthesized narratively. Eight studies (two cross-sectional and six prospective cohorts) with 25,443 participants were reviewed. Screening for PSCI was typically performed within 3 to 6 months post-stroke. Montreal Cognitive Assessment and Mini-Mental State Examination were the most commonly used tools, but cutoff scores varied widely. Screening involved pre- and post-stroke cognitive screening and identifying risk factors. Significant variability exists in PSCI assessment tools, cutoff, and timing. Further research is needed to standardize screening protocols, focusing on criteria, timing, accuracy, and feasibility. Early and repeated screening with risk management can improve PSCI prevention. [Journal of Gerontological Nursing, 51(3), 19-27.].

NSOAS24: a new global marine gravity model derived from multi-satellite sea surface slopes

Abstract. Judging by the early release of the NSOAS22 model, there were some known issues, such as boundary connection problems in blockwise solutions and a relatively high noise level. By solving these problems, a new global marine gravity model, NSOAS24, is derived based on sea surface slopes (SSSs) from multi-satellite altimetry missions. Firstly, SSSs and along-track deflections of vertical (DOVs) are obtained by retracking, resampling, screening, differentiating, and filtering procedures on the basis of altimeter waveforms and sea surface height measurements. Secondly, DOVs with a 1′×1′ grid interval are further determined using Green's function method, which applies directional gradients to constrain the surface, least-squares fit to constrain noisy points, and tension constraints to smooth the field. Finally, the marine gravity anomaly is recovered from the gridded DOVs according to the Laplace equation. Throughout the entire processing procedure, improvements in accuracy are expected for the NSOAS24 model due to the following changes, e.g., supplementing recent mission observations and removing old mission data, optimizing the step size during Green's function method, and special handling in nearshore areas. These optimizations effectively resolved the known issues of signal aliasing and the “hollow phenomenon” in coastal zones. Typical altimetry-derived marine gravity models are the DTU series released by the Technical University of Denmark and the S&S series released by the Scripps Institution of Oceanography (SIO), University of California San Diego (UCSD). Their latest models, DTU21 and SS V32.1, were used for comparison and validation. Numerical verification was conducted in three experimental areas (the Mariana Trench area, Mid-Atlantic Ridge area, and Antarctic area, representing low-, mid-, and high-latitude zones) with DTU21, SS V32.1, and shipborne data. Taking NSOAS22 for comparison, NSOAS24 showed improvements of 1.2, 0.7, and 1.0 mGal in the three test areas by validating with SS V32.1, while declines of 0.6, 0.5, and 0.3 mGal and 0.2, 0.4, and 0.3 mGal occurred in SD statistics with DTU21 and shipborne data. Finally, NSOAS24 was assessed using two sets of shipborne data (the early National Centers for Environmental Information (NCEI) dataset and the later dataset from the Japan Agency for Marine-Earth Science and Technology (JAMSTEC), the Marine Geoscience Data System (MGDS), the French Oceanographic Cruises Directory (FOCD), and the French Naval Hydrographic and Oceanographic Service (SHOM)) on a global scale. Generally, NSOAS24 (6.33 and 4.95 mGal) showed a comparable accuracy level with DTU21 (6.20 and 4.71 mGal) and SS V32.1 (6.40 and 5.53 mGal) and better accuracy than NSOAS22 (6.64 and 5.64 mGal). The new model is available at https://doi.org/10.5281/zenodo.12730119 (Zhang et al., 2024).

A Review of Machine Learning and Deep Learning Methods for Person Detection, Tracking and Identification, and Face Recognition with Applications.

This paper provides a comprehensive analysis of recent developments in face recognition, tracking, identification, and person detection technologies, highlighting the benefits and drawbacks of the available techniques. To assess the state-of-art in these domains, we reviewed more than one hundred eminent journal articles focusing on current trends and research gaps in machine learning and deep learning methods. A systematic review using the PRISMA method helped us to generalize the search for the most relevant articles in this area. Based on our screening and evaluation procedures, we found and examined 142 relevant papers, evaluating their reporting compliance, sufficiency, and methodological quality. Our findings highlight essential methods of person detection, tracking and identification, and face recognition tasks, emphasizing current trends and illustrating a clear transition from classical to deep learning methods with existing datasets, divided by task and including statistics for each of them. As a result of this comprehensive review, we agree that the results demonstrate notable improvements. Still, there remain several key challenges like refining model robustness under varying environmental conditions, including diverse lighting and occlusion; adaptation to different camera angles; and ethical and legal issues related to privacy rights.

Astrocyte-Specific Phenotyping of FAD4T as an Alzheimer's Disease Mouse Model.

Alzheimer's disease (AD) is the most prevalent neurodegenerative disease, characterized by memory decline and behavioral changes. Its pathological features include senile plaques, neurofibrillary tangles, and reactive gliosis, comprising abnormal accumulations of β-amyloid peptide (Aβ) and hyperphosphorylated tau protein surrounded by reactive astrocytes and microglia. Recently, it has emerged that severe reactive astrocytes and MAOB-dependent production of GABA and H2O2 are the real causes of learning and memory impairment and neurodegeneration. Diverse mouse models for AD have been developed to clarify pathological mechanisms and discover therapeutic strategies and drugs. However, there are many shortfalls and discrepancies among them. A new AD mouse model named FAD4T has been developed to overcome various shortcomings. Here, we employed astrocyte-focused screening procedures to examine the pathological features of FAD4T as an AD model. Our results revealed that the FAD4T mice showed abnormal accumulation of Aβ plaques in overall brain regions at 6 and 12 months. We found astrocytic hypertrophy with a significant elevation of GFAP and LCN2. However, the expressions of MAOB and iNOS, a severe reactive astrocyte marker, were unchanged. Electrophysiological and behavioral analysis indicated aberrant tonic GABA release, reduced neuronal activity, and impaired CA1-specific memory. These findings demonstrate that FAD4T mice mimic pathological and functional features of AD, different from other AD mouse models. These findings demonstrate that FAD4T mimics some features of AD patients but lacks other important features, such as severe reactive astrocytes and neurodegeneration. This astrocyte-focused screening method offers valuable tools for advancing AD research and developing new therapeutic strategies.

The Design and Evaluation of a Simulation Tool for Audiology Screening Education: Design Science Approach.

The early identification of hearing loss and ear disorders is important. Regular screening is recommended for all age groups to determine whether a full hearing assessment is necessary and allow for timely treatment of hearing problems. Procedural training is needed for new speech-language pathology students as well as continuing education for those trained to perform this screening procedure. Limited availability and access to physical training locations can make it difficult to receive the needed training. The aims of this study were to (1) develop a new hearing screening simulation software platform and (2) assess its effectiveness in training a group of graduate-level speech-language pathology students in hearing screening procedures. An audiology simulator modeled after the commercial Grason-Stadler GSI39 combination audiometer and tympanometer device was developed to serve as a precursor to traditional face-to-face clinical instruction. A description of the simulator development process, guided by a design science approach, is presented. The initiation phase established the initial criteria for the simulator design. This was followed by an iterative process involving prototype development, review, and critique by the clinical faculty. This feedback served as input for the subsequent iteration. The evaluation of the final prototype involved 33 speech-language pathology graduate students as part of an introductory audiology class. These students were randomly assigned to control (receiving in-person instruction) and test (in-person instruction and simulation tool use) groups. Students in both groups were subsequently evaluated as they performed audiology screenings on human participants and completed a 25-item pretest and posttest survey. Nonparametric Mann-Whitney U tests were conducted on the mean differences between pretest and posttest ordinal survey response data to compare the control and intervention groups. The results indicated that the students who used the simulation tool demonstrated greater confidence in their ability to (1) explain hearing screening procedures to a child (P=.02), (2) determine whether otoscopy results are normal (P=.02), and (3) determine whether otoscopy results are abnormal (P=.03). Open-ended responses indicated that the students found that the hands-on experience provided by the simulator resulted in an easy-to-use and useful learning experience with the audiometer, which increased their confidence in their ability to perform hearing screenings. Software-based education simulation tools for audiology screening may provide a beneficial approach to educating students and professionals in hearing screening training. The tool tested in this study supports individualized, self-paced learning with context-sensitive feedback and performance assessment, incorporating an extensible approach to supporting simulated subjects.

"Naked Nickel"-Catalyzed Heteroaryl-Heteroaryl Suzuki-Miyaura Coupling.

In this article we report that the air-stable "naked nickel" [Ni(4-CF3stb)3] is a competent catalyst in the catalytic Suzuki-Miyaura cross-coupling reaction (SMC) between heteroaryl bromides and heteroaromatic boron-based nucleophiles. The catalytic system is characterized by its ability to avoid decomposition or deactivation in the presence of multiple Lewis-basic sites. The protocol permits the formation of C‒C bonds between two heteroaryl moieties in the absence of complex exogenous ligands, thus minimizing screening procedures and simplifying reaction setups. This method accommodates combinations of distinct 6-membered heteroaryl bromides and 5- and 6-membered heterocyclic B-based nucleophiles.

Implementation of a blood proficiency testing scheme for bacterial screening of platelet components.

The European Directorate for the Quality of Medicines & HealthCarehas been co-ordinating an external quality assessment programme for blood establishment (BE) laboratories since 2010. To further expand the study portfolio, a new bacterial blood proficiency testing scheme (B-PTS) for platelet components (PCs) has been developed and validated in a pilot study. Sterile samples and those containing a low-count quantity of bacteria were prepared in spiking devices. Suitability of storage and shipping conditions for the samples was evaluated under different environmental conditions. Participants received the spiking devices, prepared the potentially contaminated PCs on site and tested them according to their routine screening procedures. Humidity compromised the quality of the samples. Optimized storage of the samples by adding a desiccant ensured satisfactory quality. In the pilot study, 9 of the 11 participants correctly identified the positive samples as being bacterially contaminated. The newly developed bacterial B-PTS was successfully implemented in a pilot study. It enables an inter-laboratory comparison to determine the performance of BEs for the testing of bacterial contaminations in PCs.

“Naked Nickel”‐Catalyzed Heteroaryl‐Heteroaryl Suzuki–Miyaura Coupling

In this article we report that the air‐stable “naked nickel” [Ni(4‐CF3stb)3] is a competent catalyst in the catalytic Suzuki‐Miyaura cross‐coupling reaction (SMC) between heteroaryl bromides and heteroaromatic boron‐based nucleophiles. The catalytic system is characterized by its ability to avoid decomposition or deactivation in the presence of multiple Lewis‐basic sites. The protocol permits the formation of C‒C bonds between two heteroaryl moieties in the absence of complex exogenous ligands, thus minimizing screening procedures and simplifying reaction setups. This method accommodates combinations of distinct 6‐membered heteroaryl bromides and 5‐ and 6‐membered heterocyclic B‐based nucleophiles.

Digital screening for cognitive decline in the Spanish language for older adults.

Recent technological advances in digital assessment of auditory and cognitive function may be used to circumvent the costs associated to screening for cognitive decline in the general population. Pre-clinical cognitive screening could have a transformative impact for preventive care in our increasingly old world population. However, advances in digital assessment need to be adapted for Spanish-speaking populations as innovation occurs mainly in English. This study explores the potential of a novel screening battery for cognitive decline that utilizes digital tests of cognitive and auditory function adapted to the Spanish language. Participants were evaluated on standard clinical scales and questionnaires, and on a digital battery of auditory and cognitive tests with potential clinical value to screen cognitive decline. We report the ability to detect minimal cognitive impairment (MCI; 3/10 tests) and dementia (10/10 tests) of each digital test and the full battery. We further show concurrent validity for cognitive screening with the Montreal Cognitive Assessment (MoCA) and describe the shared variance across tests in the battery. Lastly, we show multiple regression models predicted with medium sensitivity (57%) and high specificity (97%) the dementia cases, and with high sensitivity (93%) but low specificity (31%) the MCI cases. Overall, this study demonstrates discriminatory value and concurrent validity to screen for cognitive decline in older adults using open-access digital auditory and cognitive tests in the Spanish language. Follow-up studies with larger and more diverse samples will be instrumental in achieving cognitive screening procedures for the general population.

Impact of loss of PRLHR on aneuploidy and initiation of bladder cancer.

854 Background: The question still remains for the presence of yet unknown genetic modifiers in the initiation and/or progression of bladder cancer (BC). It remains challenging to identify the key factors, the mechanism and potential target behind. A genome-wide CRISPR library colony formation assay was performed using UROtsa cells, an immortalized ureter epithelium cell line, in order to identify novel genes that promote the transformation of normal urothelial cells. As one of the top candidate genes, the Prolactin Releasing Hormone Receptor (PRLHR) was identified. To date, only a few studies addressed the impact and the correlation of PRLHR in cancer and reports in the context of BC are missing altogether. Methods: We assessed the gene and protein expressions via public database, qPCR and Western blot analyses in a set of nontumour cell strains (Y235T, UROtsa) and a set of BC cell lines (RT4, T24). Following the shRNA knockdown in the PRLHR-proficient cells(Y235T) and the ectopic PRLHR expression in the cell lines with low/absent PRLHR expression(T24), we performed functional experiments, such as soft agar colony formation assay, MTT, invadopodia and gelatine degradation assays. We determined the invasion and migration abilities of these genetically modified cells by using the and wound-healing, Boyden chamber and porcine bladder ex vivo organ culture invasion assay. Metaphase spread assay and Immunofluorescence was performed in modified Y235T cells for chromosome counting and microscopy observation (centrsomes, chromosomal misalignments and lagging chromosomes). Results: The available in silico expression data and expression analyses in BC cell lines and matched normal/tumour patient samples revealed the repression of PRLHR early in BC tumorigenesis. Soft agar experiments confirmed that ectopic PRLHR impairs colony formation of T24 cells, providing indirect evidence that the CRISPR-Cas9 mediated deficiency of PRLHR promoted UROtsa cells transformation in the screening assay. Functional experiments using the MTT and the wound healing assays showed that ectopic PRLHR improved the viability and migration abilities of BC cells. The Boyden chamber assay and the porcine bladder organ culture model revealed that PRLHR impairs BC cell invasion, likely by the inhibition of the AKT/PKB pathway. Moreover, we observed an abnormal number of centrosomes, an increasing number of lagging chromosomes in the anaphase and telophase of the PRLHR-knockdown cells during mitosis. Conclusions: The screening procedure identified PRHLR as a new candidate tumour suppressor factor, and that loss/down-regulation of this protein contributes to the progression of bladder cancer. Downregulation of PRLHR leads to aneuploidy and genome instability in urothelial cells. PRLHR might serve as a novel biomarker or potential target for BC.

Congenital deafness: from screening to management

Congenital deafness: from screening to management

Effects of plant active substances in rheumatoid arthritis-a systematic review and network meta-analysis.

Plant active substances are extensively utilized in treating rheumatoid arthritis (RA). Despite numerous experimental and clinical studies on plant active substances their efficacy remains largely unsubstantiated. The widespread use of these extracts as therapeutic measures for RA is problematic due to the lack of compelling evidence. Our research aims to assess the impact of plant active substances on RA by conducting a network meta-analysis. We systematically searched four electronic databases-PubMed, EMBASE, the Cochrane Central Register of Controlled Trials, and Web of Science-from their inception to August 2024. The main focus was on assessing primary outcomes, including the Visual Analogue Scale (VAS), inflammatory markers, Swollen Joint Count (SJC), Tender Joint Count (TJC), and Disease Activity Score on 28 joints (DAS28). We performed data analysis using StataMP 15.1 software and ranked the therapeutic effects based on the Surface Under the Cumulative Ranking Curve (SUCRA) probability values. Based on screening procedures, 18 eligible studies were incorporated into the analysis. These studies encompassed a total of 1,674 RA patients and investigated 10 different plant active substance therapies. Specifically, 10 studies included VAS indicators, 17 studies included inflammatory marker indicators, 14 studies included DAS28 indicators, 13 studies included SJC indicators, and 13 studies included TJC indicators. Based on SUCRA values, quercetin appeared to be the most effective treatment for decreasing serum VAS levels (67.3%). Furthermore, curcumin emerged as the most promising option for reducing inflammatory marker levels (72.3%), SJC (75.6%), and TJC (76.2%). Lastly, with respect to DAS28, resveratrol emerged as the optimal choice (74.3%). According to the network meta-analysis (NMA), curcumin exhibited superior efficacy compared to placebo in decreasing SJC and TJC. Additionally, curcumin demonstrated greater effectiveness in reducing inflammatory markers. Quercetin was more effective in reducing VAS, and resveratrol was more effective in reducing DAS28. Patients with RA may benefit from these findings. Insightful information from this study is helpful for RA patients to consider using plant active substance therapies. For their efficacy and safety to be confirmed, more proof is needed.

The Role of Artificial Intelligence in the Detection of Cardiac Amyloidosis: A Systematic Review.

Cardiac amyloidosis (CA) is an underdiagnosed condition that occurs when misfolded amyloid proteins deposit in cardiac tissue causing progressive myocardial dysfunction. Artificial intelligence (AI) technology demonstrates strong potential to enhance the identification of various cardiovascular diseases, including CA. This systematic review examines how AI systems identify CA. This study explores AI's application as a diagnostic tool for CA. We searched five databases (PubMed/MEDLINE, Scopus, Web of Science, Embase, and IEEE Xplore) for relevant studiesusing thePreferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Ten of the 629 studies we discovered were determined to be suitable for the current systematic review.We used the PICOS (P:population, I:intervention or exposure, C:comparison, O:outcome, S:study type) framework to collect relevant data from the included studies. The screening procedure was blinded and used predefined inclusion and exclusion criteria. It was undertaken in two phases. Whenever disagreements emerged, the reviewers deliberated and concluded. After the screening procedure, 10 studies in all were judged suitable for this review. Through the analysis of standard laboratory data, transthoracic echocardiography,electrocardiogram,medical records, cardiac magnetic resonance,and whole-body scintigraphy, these studies assessed the potential usefulness of AI models in diagnosing CA. AI models have been useful as a CA diagnostic tool, outperforming professional cardiologists in one instance or being on par with them in others.

Technology-Based Interventions for the Treatment of Peripartum Depression: An In-Depth Systematic Review

Abstract Technology-based interventions have the potential to be effective in reducing peripartum depression and to increase the access to treatment. Previous systematic reviews focus on the efficacy of the interventions, giving little attention to the programs themselves. The aims of this review are to provide a comprehensive inventory of the program’s components and structure, to analyze their efficacy, and to offer general guidelines for interventions development. A systematic search was conducted in five databases and reported using PRISMA guidelines. An in-depth synthesis and analysis was performed globally as well as on each intervention program. Eighteen reports were included covering 11 different interventions. Overall, the interventions are effective, leading to medium-large reductions of peripartum depression symptoms. All relevant aspects were analyzed such as the most common components, number of sessions, completion rates, extra features, or screening procedure. Guidelines for program development were formulated based on the analysis, covering elements relevant for increasing efficacy as well as treatment adherence. Aspects regarding content as well as mode of delivery are considered.