Manual Testing Research Articles

Extended reality to assess post-stroke manual dexterity: contrasts between the classic box and block test, immersive virtual reality with controllers, with hand-tracking, and mixed-reality tests

BackgroundRecent technological advancements present promising opportunities to enhance the frequency and objectivity of functional assessments, aligning with recent stroke rehabilitation guidelines. Within this framework, we designed and adapted different manual dexterity tests in extended reality (XR), using immersive virtual reality (VR) with controllers (BBT-VR-C), immersive VR with hand-tracking (BBT-VR-HT), and mixed-reality (MD-MR).ObjectiveThis study primarily aimed to assess and compare the validity of the BBT-VR-C, BBT-VR-HT and MD-MR to assess post-stroke manual dexterity. Secondary objectives were to evaluate reliability, usability and to define arm kinematics measures.MethodsA sample of 21 healthy control participants (HCP) and 21 stroke individuals with hemiparesis (IHP) completed three trials of the traditional BBT, the BBT-VR-C, BBT-VR-HT and MD-MR. Content validity of the different tests were evaluated by asking five healthcare professionals to rate the difficulty of performing each test in comparison to the traditional BBT. Convergent validity was evaluated through correlations between the scores of the traditional BBT and the XR tests. Test-retest reliability was assessed through correlations between the second and third trial and usability was assessed using the System Usability Scale (SUS). Lastly, upper limb movement smoothness (SPARC) was compared between IHP and HCP for both BBT-VR test versions.ResultsFor content validity, healthcare professionals rated the BBT-VR-HT (0[0–1]) and BBT-MR (0[0–1]) as equally difficult to the traditional BBT, whereas they rated BBT-VR-C as more difficult than the traditional BBT (1[0–2]). For IHP convergent validity, the Pearson tests demonstrated larger correlations between the scores of BBT and BBT-VR-HT (r = 0.94;p < 0.001), and BBT and MD-MR (r = 0.95;p < 0.001) than BBT and BBT-VR-C (r = 0.65;p = 0.001). BBT-VR-HT and MD-MR usability were both rated as excellent, with median SUS scores of 83[57.5–91.3] and 83[53.8–92.5] respectively. Excellent reliability was found for the BBT-VR-C (ICC = 0.96;p < 0.001), BBT-VR-HT (ICC = 0.96;p < 0.001) and BBT-MR (ICC = 0.99;p < 0.001). The usability of the BBT-VR-C was rated as good with a median SUS of 70[43.8–83.8]. Upper limb movements of HCP were significantly smoother than for IHP when completing either the BBT-VR-C (t = 2.05;p = 0.043) and the BBT-VR-HT (t = 5.21;p < 0.001).ConclusionThe different XR manual tests are valid, short-term reliable and usable tools to assess post-stroke manual dexterity.Trial registrationhttps://clinicaltrials.gov/ct2/show/NCT04694833; Unique identifier: NCT04694833, Date of registration: 11/24/2020.

The Evolving Role of Artificial Intelligence in Software Testing: Prospects and Challenges

Artificial intelligence (AI) refers to a machine's capacity for operations typically performed by human intelligence, such as learning, thinking, solving problems, and making decisions. Machine learning, neural networks, expert systems, and rule-based systems are all used in artificial intelligence. AI employs methods and algorithms to process data, draw conclusions from patterns and laws, and enhance performance over time. A software application or product's intended functionality is evaluated and verified through the process of software testing. The benefits of testing include the prevention of bugs, decreased development costs, and improved performance. Through test generation, test data generation, and automated test script writing, AI can be used in software testing to enhance the quality of our product and the manual testing processes. Software testing is a time-consuming, laborious, and tiresome process. Automation solutions have been created to help with automating some testing process operations in order to increase quality and delivery time. As continuous integration and delivery (CI/CD) pipelines are added, automation systems gradually lose part of their usefulness. The testing community is looking to AI to fill the gap because AI has the capacity to check the code for flaws and defects without the need for any human intervention and much more quickly than humans. In this study, we want to comprehend the effects of AI technology on various STLC tasks or components of software testing. The study also makes an effort to pinpoint and explain some of the biggest challenges faced by software testers when implementing AI in testing. The report also suggests several significant potential contributions of AI to the field of software testing.

Music Generation Based on Bidirectional GRU Model

Lately, substantial advancements in the realm of deep learning have given rise to new approaches for autonomously generating music. This study has devised a generative framework intended to produce musical melodies. This framework capitalizes on bidirectional gated recurrent units (GRU) as its foundational architecture. To impart knowledge to the model, a collection of classical piano compositions in MIDI format has been employed as the training dataset. One implements a stacked architecture of bidirectional GRU layers to capture long-term musical patterns. The addition of dropout regularization prevents overfitting. Generated samples are evaluated both quantitatively through model loss, as well as qualitatively by manual listening tests. According to the analysis, the approach can produce coherent musical melodies with reasonable structure. This demonstrates the potential for deep bidirectional models to learn musical syntax and generate new compositions. Limitations include lack of long-term musical form and repetitive patterns. Future work should explore architectures to improve coherence over longer time spans, as well as integrate other modalities like rhythm and harmony. These results provide a strong foundation for automated music generation systems.

Effects of Instability Neuromuscular Training Using an Inertial Load of Water on the Balance Ability of Healthy Older Women: A Randomized Clinical Trial.

The reflexive responses to resist external forces and maintain posture result from the coordination between the vestibular system, muscle, tendon, and joint proprioceptors, and vision. Aging deteriorates these crucial functions, increasing the risk of falls. This study aimed to verify whether a training program with water bags, an Instability Neuromuscular training (INT) using the inertial load of water, could positively impact balance ability and dynamic stability. This study was conducted with twenty-two healthy older women aged ≥ 65 (mean age: 74.82 ± 7.00 years, height: 154.20 ± 5.49 cm, weight: 55.84 ± 7.46 kg, BMI: 23.55 ± 3.58 kg/m2). The participants were randomly allocated into two groups: a group that used water bags and a control group performing bodyweight exercises. The intervention training lasted 12 weeks, with 2 sessions per week totaling 24 sessions, each lasting 60 min. The pre- and post-tests were compared using t-tests to examine within- and-between-group differences. The effect size was examined based on the interaction between group and time using a two-way repeated measures ANOVA. The Modified Timed Up and Go manual (TUG manual), Sharpened Romberg Test (SRT), and Y-balance test (YBT) were conducted to assess dynamic stability, including gait function, static stability, and reactive ability. In comparison between groups, the waterbag training group showed a decrease in task completion time associated with an increase in walking speed in the TUG manual test (p < 0.05), and an increase in static stability and reaction time in the Sharpened Romberg test with eyes closed (p < 0.05), and an increase in single-leg stance ability in both legs in the Y-balance test (p < 0.05). All statistical confidence interval levels were set 95%. INT using the inertial load of water enhanced the somatosensory system and gait related to dynamic stability in older women. Therefore, the clinical application of this training program is expected to reduce the risk of falls in healthy older women, improving dynamic stability related to gait.

Diagnostic accuracy of clinical, radiological and device-driven tests for the detection of First Ray Instability: A systematic review

IntroductionFirst Ray Instability (FRI) and especially hypermobility leads to the collapse of the medial longitudinal arch's structural framework, which reduces the foot's ability to become a rigid lever for propulsion, resulting in progressive foot deformities. Early detection of FRI with prompt intervention helps prevent degenerative foot deformities. Various manual, device-based and radiographic diagnostic tests for FRI quantification have been described in the literature. We aim to conduct an up-to-date, comprehensive, systematic review of the literature reporting on diagnostic tests to evaluate FRI. MethodologyElectronic databases (Medline, Embase and PubMed) and bibliography lists were searched until May 2021 for studies evaluating diagnostic tests for FRI. MeSH terms were used to conduct the literature search. The authors screened all produced abstracts. Selected articles were further assessed in full based on inclusion and exclusion criteria. The relevant studies were qualitatively assessed and grouped into tables based on tests. Results18,176 studies were identified. Thirty-two full-text articles were included for assessment. Ten articles were excluded based on evaluation criteria. 18 studies were included for qualitative assessment: two studies describing manual diagnostic tests, three evaluating device-driven tests, six image-guided studies and seven comparison studies assessing a new test versus an established one. ConclusionGold standard tests in defining FRI need to be improved. Manual tests exhibit significant subjective variability. Radiographic tests, while accurate, are complex and cumbersome to perform and, therefore, are not widely applied. Dorsal rulers have demonstrated mixed results and shown variability when compared to instruments. The focus has been on assessing FRI in hallux valgus (HV). More studies are needed to investigate FRI in the absence of HV.

A novel geometric predictive algorithm for assessing compressive elastic modulus in MEX additive processes, based on part nonlinearities and layers stiffness, validated with PETG and PLA materials

MEX (Material Extrusion) is an intrusive technological process that inherently induces alterations in the elastic and mechanical parameters of plastic materials. Manufacturers provide initial mechanical parameters for plastic filaments, which undergo modifications during MEX manufacturing, influenced by intrinsic manufacturing factors such as temperature and pressure changes, as well as geometric and technological parameters of the 3D additive process. These factors, compounded by the inherent geometric nonlinearities in plastic components, directly impact the post-manufacture mechanical and elastic properties of the material. Presently, material characterization in MEX manufacturing relies on manual experimental testing, necessitating new tests for any variation in manufacturing parameters. In this scenario of mechanical uncertainty, rigorously validating component behavior involves costly experimental trials. Intending to solve the problems of MEX components manufacturing, the paper presents an innovative methodology based on the use of a new predictive algorithm created by the researchers capable of obtaining the elastic modulus of a plastic material manufactured with MEX and its mechanical behavior in the elastic zone under compressive loads. The predictive algorithm only needs as input the compressive elastic modulus of the isotropic plastic material filament and the manufacturing parameters of the MEX process. The smart developed algorithm calculates the stiffness of each layer considering the number of holes in the projected area. The innovative predictive algorithm has been experimentally and numerically validated using PETG (Polyethylene Terephthalate Glycol) material and PLA (Polylactic Acid) on test specimens and on a case study of variable topology. The results show deviations from [0.2%–4.3%] for PETG and [0.4%] for PLA concerning the experimental tests and [1.1%–13.5%], to the numerical analyses. In this line, the presented algorithm greatly improves the results obtained by the simulation software since this software currently can not consider the geometric and technological parameters associated with the 3D manufacturing process of the component. The predictive algorithm is valid for each print pattern and manufacturing direction. The new algorithm improves the existing state of the art significantly since this algorithm extends its utility to most plastic polymer materials suitable for MEX 3D printing, provided that the mechanical and elastic properties of the filament are known. Its versatility extends to complex component geometries subjected to uniaxial compression loads, eliminating the need for mechanical analysis software or expensive experimental validations.

Development of toluidine red particle agglutination-based turbidimetric immunoassay for anticardiolipin antibody detection in syphilis

IntroductionSerological tests of non-treponemal and treponemal types are the most frequently used for syphilis diagnosis. Nontreponemal tests are used to monitor disease activity. Toluidine red unheated serum test (TRUST), as one of nontreponemal tests, is generally applicable to hospitals at different levels. However, accurate judgment of TRUST results is inseparable from an experienced and accurate operator. To reduce current shortcomings of manual TRUST method, we attempted to convert the manual TRUST test into automatic TRUST test, that is, to determine the degree of aggregation of toluidine red particles by detecting the absorbance value of serum after reaction with toluidine red particles. Methods50 μL of serum sample and 80 μL toluidine red particles were added to 96-well plate. Then, the 96-well plate was placed on a microplate reader at medium grade for 8 min to mix. Then, plasma reagin reacted with toluidine red particles and promoted the aggregation of toluidine red particles to form a large clot, which eventually caused a decrease in the absorbance at 540 nm. ResultsThe results showed that the specificity of the automatic TRUST test was 100%, the sensitivity was 87%. And this method showed 93.5% correlation with manual TRUST test. The developed method is simple and involves less subjectivity in reading results, opening new avenues for syphilis diagnostic testing. ConclusionTurbidimetric immunoassay can avoid the shortcomings of subjective interpretation, time-consuming and manual operation of manual TRUST method, and is more suitable for large-scale screening in health examination.

Epidemiologic analysis of antimicrobial resistance in hospital departments in China from 2022 to 2023

Bacterial drug resistance monitoring in hospitals is a crucial aspect of healthcare management and a growing concern worldwide. In this study, we analysed the bacterial drug resistance surveillance in our hospital from 2022 Q1 to 2023 Q2. The main sampling sources were respiratory, blood, and urine-based, and the main clinical infections were respiratory and genitourinary in nature. Specimens were inoculated and cultured; bacterial strains were isolated using a VITEK® 2 Compact 60-card automatic microorganism identifier (bioMerieux, Paris, France) and their matching identification cards were identified, and manual tests were supplemented for strain identification. The most common Gram-positive bacteria detected were Staphylococcus aureus, followed by Enterococcus faecalis (E. faecalis), Staphylococcus epidermidis (S. epidermidis), and Staphylococcus haemolyticus (S. haemolyticus). The most common Gram-negative bacteria detected were Escherichia coli, Klebsiella pneumoniae, and Pseudomonas aeruginosa. The most prevalent multidrug-resistant bacteria were those producing extended-spectrum beta-lactamases, followed by methicillin-resistant Staphylococcus aureus, followed by carbapenem-resistant Enterobacterales. This study suggests that the prevention and control of infections in the respiratory and genitourinary systems should be the focus of anti-infective work and that the use of antimicrobials should be reduced and regulated to prevent the emergence and spread of resistant bacteria.

Adaptive attention fusion network for cross-device GUI element re-identification in crowdsourced testing

The rapid growth of mobile devices has ushered in an era of different device platforms. Different devices require a consistent user experience, especially with similar graphical user interfaces (GUIs). However, the different code bases of the various operating systems as well as the different GUI layouts and resolutions of the various devices pose a challenge for automated software testing. Crowdsourced software testing (CST) has emerged as a viable solution where crowdsourced workers perform tests on their own devices and provide detailed bug reports. Although CST is cost-effective, it is not very efficient and requires a large number of workers for manual testing. The potential of optimizing CST reproduction testing through computer vision remains largely untapped, especially when considering the uniformity of GUI elements on different devices. In this study, we present a novel deep learning model specifically designed to re-identify GUI elements in CST reproduction test scenarios, regardless of the underlying code changes on different devices. The model features a robust backbone network for feature extraction, an innovative attention mechanism with learnable factors to enhance the features of GUI elements and minimize interference from their backgrounds, and a classifier to determine matching labels for these elements. Our approach was validated on a large GUI element dataset containing 31,098 element images for training, 115,704 element images from real apps for testing, and 67 different background images. The results of our experiments underline the excellent accuracy of the model and the importance of each component. This work is a major step forward in improving the efficiency of reproduction testing in CST. The innovative solutions we propose could further reduce labor costs for CST platforms.

Overall Scheme Design of Fault Diagnosis for Complex Electronic Systems

Traditional manual testing methods are no longer able to meet the maintenance needs of modern shipboard fire control system board-level circuits. Therefore, the development of intelligent and universal automatic test systems has become an important issue for shipboard fire control system fault detection. This article analyzes the performance and functional requirements of the automatic test system, develops the automatic test system based on PXI bus instruments, and designs an overall solution with signal time-frequency analysis capabilities that can carry out intelligent fault diagnosis for a variety of board-level circuits. On this basis, the design of hardware circuit and software solution is completed. The automatic test system was analyzed on hardware indicators and software functions. On the hardware side, the hardware resource selection was completed based on the hardware indicator requirements. On the software side, the overall software architecture design and the design of each functional module were completed based on the system software functional requirements. Finally, the composition and functions of each module of the system software and the overall operation process are introduced.

Design of Fault Diagnosis Method Based on Circuit Frequency Domain Characteristics Fault Dictionary Method

In recent years, with the rapid development of integrated circuits and the widespread application of smart chips, the internal structure of complex electronic systems have become increasingly complex, which greatly increases the difficulty of board-level circuit testing and fault diagnosis of complex electronic systems. At present, traditional manual testing methods are no longer able to meet the maintenance requirements of modern complex electronic system board-level circuit. Therefore, the development of intelligent and universal automatic testing systems has become an important issue in fault detection of complex electronic systems. Based on the testing requirements of the automatic test system(ATS), this paper designs the corresponding overall scheme, software scheme and hardware circuit, and studies the fault diagnosis and test of board-level circuit of complex electronic system, and proposes to apply the traditional Bode diagram to the engineering practice of ATS. Fault detection is realized by utilizing the frequency domain characteristics of the object under test.

Disparities in DMT treatment: Demographic and neurocognitive differences between MS patients currently treated versus not treated with disease-modifying therapies

BackgroundCurrent treatment guidelines recommend consideration of disease-modifying therapy (DMT) for all multiple sclerosis (MS) patients, but barriers to access have begun to be identified. In particular, prior studies have found that people with higher education have better access to DMTs, perhaps explained by the association of higher education with higher income. And while the majority of people with MS are women, being male is also associated with higher income. These factors argue for the need to better understand whether there are differences in DMT uptake based on sex and education. Finally, in addition to well-documented benefits of DMTs for slowing disease progression, there is growing evidence to suggest benefits of DMTs for cognitive functioning. ObjectiveDetermine whether rates of DMT treatment differ based on education and sex. Secondarily, we investigate whether neurocognitive test performance differs in treated versus not treated groups. MethodsIn cross-sectional data, mixed effects linear regression evaluated differences in education and sex of those treated versus not treated with DMTs. Models included the following predictors: age, disease duration, MS subtype, sex/education, disability, atrophy, and T2 lesion volume. Propensity score weights were extracted to obtain unbiased estimates of the relationship between DMT status and each outcome of interest. The same models evaluated performance differences between groups on an iPad-based processing speed test (PST) and manual dexterity test (MDT). ResultsControlling for covariates, individuals with less education (OR=1.09, 95 % CI=[1.03, 1.14], p = 0.003) and women (OR=0.80, 95 % CI=[0.72, 0.90], p < 0.001) were less likely to be currently treated with DMTs. Small effect size association was shown for DMT treatment with better performance on PST (beta=0.09, CI=[0.06, 0.13], p < 0.001) and MDT (beta=0.05, CI=[0.01,0.08], p = 0.011). ConclusionsWomen and people with lower education had a lower likelihood of being currently treated with DMTs. After controlling for all relevant variables, an independent (small) association of DMT treatment to better performance on tests of processing speed and fine motor dexterity was found. Reasons for disparities remain to be investigated in future work, and may include employment status, health insurance coverage, or sex differences in risk tolerance.

Review of outcomes measures used to evaluate upper extremity surgical treatment of cervical spinal cord injury with recommendations for the future

Cervical spinal cord injury is a life-altering event that profoundly affects an individual’s upper extremity function. Nerve transfer surgeries have been shown to restore more natural movement and fine motor control in this population. At present, there is no consensus on how to evaluate the efficacy of these restorative surgeries. The purpose of this work was to perform a comprehensive review of the existing literature and describe the outcome measures used. We hypothesized that the assessments will be heterogeneous across studies and will incompletely capture the effect of nerve transfers on upper extremity motion in cervical spinal cord injury. A search strategy was designed and a review of multiple databases (Embase.com, Ovid-Medline All, and Scopus) yielded 481 articles; 26 unique studies met inclusion criteria and underwent analysis. Both manual muscle strength testing and video content were presented in the majority of studies. Outcome assessments including myometry, functional outcomes measures (such as the grasp and release test), patient-reported outcomes (including generic, extremity, and disease-specific types), and custom de novo questionnaires were used variably across studies. Future work should focus on standardizing outcomes measures in the field and developing and incorporating kinematic analysis to quantify the intricate, coordinated, and precise movement attained after nerve transfer surgery in the setting of cervical spinal cord injury.

Usefulness Assessment of Automated Strabismus Angle Measurements Using Innovative Strabiscan Device.

The variability of the obtained results of manual tests assessing the angle of strabismus depends on the experience, skills, and training of the examiner. The authors hope that this new measuring device will provide a more sensitive and repeatable method for detecting small strabismus angles compared to the gold standard-PCT. The purpose of this article is to present an innovative strabismus angle demonstration device, called Strabiscan, to provide automated measurements of eye deviation and to compare the obtained results of these measurements to the traditional manual method. For patients with manifest strabismic disease (n = 30) and a group of healthy subjects (n = 30), a detailed history was taken and routine ophthalmologic examinations were performed, including best-corrected distance visual acuity, assessment of refractive error using an autorefractometer after cycloplegia, biomicroscopic evaluation of the anterior segment of the eye and evaluation of the eye fundus by indirect ophthalmoscopy. Subsequently, each patient and healthy subject was subjected to a prismatic cover-uncover test using a manual method, after which the presence of strabismus was detected and its angle assessed using a Strabiscan demonstration device. In the control group using the Strabiscan demonstration device, small-angle latent strabismus ≤ 3DP was diagnosed in 83% of patients, while >3DP was found in 13%. In contrast, using the prismatic cover-uncover test, latent strabismus ≤ 3DP was diagnosed in only 13% of patients, and latent strabismus with an angle > 3DP was found in 13% of patients. No statistically significant differences were noted in the measurements of strabismus angles made by the different methods. The Strabiscan demonstration device allows quick and accurate assessment of the strabismus angle. Compared to the prismatic cover-uncover test, it has a higher sensitivity for detecting low-angle latent strabismus. Measurements with the Strabiscan do not require the presence of additional assistants for the test.

Effect of cognitive multisensory rehabilitation on upper extremity function in stroke patients: A randomized controlled trial

The aim of this study was to examine the impact of upper extremity (UE) rehabilitation based on neurocognitive multisensory therapy in stroke patients, focusing on assessing its effects on the recovery of UE function and suggesting it as a post-stroke therapeutic method. The study was conducted as a randomized, prospective, controlled trial with a pre- and post-experimental design. Thirty stroke patients were equally divided into study and control groups and evaluated before and after treatment. Outcome measures included the Action Research Arm Test (ARAT), Manual Function Test (MFT), Motor Evaluation Scale for Upper Extremity in Stroke Patients (MESUPES), and Fugl Myer Assessment upper extremity (FMA-UE). Both groups received a specific physiotherapy program, while the study group also received Cognitive Multisensory Rehabilitation (CMR). All analyses were performed using SPSS (version 25). There were no significant differences between the groups in age, weight, height, BMI, duration of illness, MMSE, sex, or spasticity grade distribution (p &gt; 0.05). Post-treatment comparison between both groups showed a statistically significant increase in the ARAT, MFT, MESUPES, and FMA-UE scores in the study group compared to the control group (p &lt; 0.05). For stroke patients, CMR intervention is considered a beneficial neuro-rehabilitation strategy for enhancing upper extremity sensorimotor capabilities through physical therapy.

A novel deep learning model for diabetic retinopathy detection in retinal fundus images using pre-trained CNN and HWBLSTM

Diabetic retinopathy (DR) is a global visual indicator of diabetes that leads to blindness and loss of vision. Manual testing presents a more difficult task when attempting to detect DR due to the complexity and variances of DR. Early detection and treatment prevent the diabetic patients from visual loss. Also classifying the intensity and levels of DR is crucial to provide necessary treatment. This study develops a novel deep learning (DL) approach called He Weighted Bi-directional Long Short-term Memory (HWBLSTM) with an effective transfer learning technique for detecting DR from the RFI. The collected fundus images initially undergo preprocessing to improve their quality, which includes noise removal and contrast enhancement using a Hybrid Gaussian Filter and probability density Function-based Gamma Correction (HGFPDFGC) technique. The segmentation procedure divides the image into subgroups and is crucial for accurate detection and classification. The segmentation of the study initially removes the optical disk (OD) and blood vessels (BVs) from the preprocessed images using mathematical morphological operations. Next, it segments the retinal lesions from the OD and BV removed images using the Enhanced Grasshopper Optimization-based Region Growing Algorithm (EGORGA). Then, the features from the segmented retinal lesions are learned using a Squeeze Net (SQN), and the dimensionality reduction of the extracted features is done using the Modified Singular Value Decomposition (MSVD) approach. Finally, the classification is performed by employing the HWBLSTM approach, which classifies the DR abnormalities in datasets as non-DR (NDR), non-proliferative DR (NPDR), moderate NPDR (MDNPDR), and severe DR, also known as proliferative DR (PDR). The proposed approach is implemented on APTOS as well as MESSIDOR datasets. The outcomes proved that the proposed technique accurately identifies the DR with minimal computation overhead compared to the existing approaches. Communicated by Ramaswamy H. Sarma

CDC Laboratory Recommendations for Syphilis Testing, United States, 2024.

This report provides new CDC recommendations for tests that can support a diagnosis of syphilis, including serologic testing and methods for the identification of the causative agent Treponema pallidum. These comprehensive recommendations are the first published by CDC on laboratory testing for syphilis, which has traditionally been based on serologic algorithms to detect a humoral immune response to T. pallidum. These tests can be divided into nontreponemal and treponemal tests depending on whether they detect antibodies that are broadly reactive to lipoidal antigens shared by both host and T. pallidum or antibodies specific to T. pallidum, respectively. Both types of tests must be used in conjunction to help distinguish between an untreated infection or a past infection that has been successfully treated. Newer serologic tests allow for laboratory automation but must be used in an algorithm, which also can involve older manual serologic tests. Direct detection of T. pallidum continues to evolve from microscopic examination of material from lesions for visualization of T. pallidum to molecular detection of the organism. Limited point-of-care tests for syphilis are available in the United States; increased availability of point-of-care tests that are sensitive and specific could facilitate expansion of screening programs and reduce the time from test result to treatment. These recommendations are intended for use by clinical laboratory directors, laboratory staff, clinicians, and disease control personnel who must choose among the multiple available testing methods, establish standard operating procedures for collecting and processing specimens, interpret test results for laboratory reporting, and counsel and treat patients. Future revisions to these recommendations will be based on new research or technologic advancements for syphilis clinical laboratory science.

Manual pre-pubic compression test: A bedside examination tool in babies with atypical genitalia

Manual pre-pubic compression test: A bedside examination tool in babies with atypical genitalia

Instrumental and clinical diagnosis of interdigital Morton neuroma

Background: Interdigital neuroma is one of the most common diseases in foot surgery. It is a common cause of metatarsalgia leading to debilitating pain. At the same time, the diagnosis of neuroma can be a difficult task for an orthopedic surgeon due to the complex anatomy of the forefoot and a large number of soft-tissue and bone structures. Besides, the existing manual testing methods are not pathognomonic specifically for neuroma. The clinical symptoms and history, as well as a physical exam and instrumental evidence are important in assessing and grading the disease. However, there are no recommendations in the modern Russian literature regarding the need for routine visualization of neuroma. The reproducibility of the study, the absence of ionizing radiation and the possibility of obtaining a second opinion, as well as high sensitivity makes magnetic resonance imaging an important diagnostic tool in the diagnosis of neuroma. Aim: To evaluate the importance of the anamnesis and complaints and the sensitivity of clinical tests and magnetic resonance imaging of the foot in the diagnosis of interdigital neuromas. Methods: The study presents a retrospective analysis of the medical histories and outpatient records of 28 patients (8 men and 20 women) treated at the EMC ECSTO in the period from 2017 to 2022. The examination was performed according to a standardized protocol: collection of complaints and anamnesis, palpation, manual testing, including Mulder's click test. As a part of examination, magnetic resonance imaging of the foot was performed for all the patients. The average patient’s age at the time of the surgical treatment was 45 years. The resection of a part of the affected nerve with the subsequent histological examination was considered a gold standard for the treatment and verification of interdigital neuromas. In all the cases, the histopathological study confirmed the diagnosis. Results: The sensitivity of magnetic resonance imaging in the diagnosis of interdigital neuromas was 86%, the sensitivity of the Mulder test was 61%. Pain during the palpation of the affected interdigital space was determined in 100% of cases. Conclusion: The combination of a manual examination and magnetic resonance imaging, along with the analysis of complaints and anamnesis makes it possible to diagnose Morton's neuroma in most cases.

Manual Testing Vital Role: A Research Perspective

Software testing is an important component of the software development life cycle (SDLC) that verifies and validates that a software program runs as intended. It entails carefully understanding the program to detect flaws, ensuring compliance with requirements, and ensuring that it matches user expectations. Software testing employs a variety of strategies, including manual testing, automated testing, and a combination of both. Manual testing is an important component of software development life cycle (SDLC), in which testers are humans who complete the test cases without the use of automation tool. It is an organized process in which testers manually check and validate the performance of a software application to make sure that it meets the proper requirements. Manual testing is classified into many groups, including functional testing, regression testing, integration testing, and user acceptance testing. Manual testing involves following developed test cases, examining the application's functionality, and identifying faults or errors by interacting with the product as end users would. This procedure helps to verify that the program is reliable, functional, and usable. Manual testing is especially useful for scenarios where automated testing is either impossible or too costly, such as exploratory or ad hoc testing