Diagnostic Methodology Research Articles

Analysis of the structural integrity of wooden structures in service: case study with diagnostic methodologies and rehabilitation strategies

The rehabilitation, restoration, or reinforcement of wooden structures requires a thorough assessment of their "health" status. This evaluation is akin to Structural Integrity Assessment (SIA), which employs multidisciplinary techniques to determine the state of accumulated damage and its growth rate, enabling the prediction of the remaining service life of the structure and the scheduling of corrective actions. This case study aims to investigate the structural health of a wooden pergola in Belo Horizonte, MG, Brazil, applying SIA based on methods of propedeutics, diagnosis, and therapy. Non-destructive tests such as acoustic tomography, controlled penetration test (Resistograph), and impact test (Pilodyn) were conducted for propedeutics and diagnosis. Macroscopic visual assessment identified the wood species used in the pergola: Cumaru, Angelim Vermelho, and an unidentified species. The wood strength class was determined by the resistograph, and biotic, abiotic, and structural damages were evaluated. The structural performance analysis, considering the actual sections and the physical and mechanical characteristics of the wood, was performed using SAP 2000 software, with the verification of structural elements and connections according to the Brazilian standard NBR 7190. The study concludes by presenting therapies of a constructive nature, treatment and protection, and structural character.

Traumatic brain injury: Advances in coagulopathy (Review).

Trauma is a prevalent cause of coagulopathy, with traumatic brain injury (TBI) accompanied by coagulation disorders particularly linked to adverse outcomes. TBI is distinguished by minimal bleeding volume and unique injury sites, which precipitate complex coagulation disturbances. Historically, research into trauma-induced coagulopathy has primarily concentrated on the molecular biology and pathophysiology of endogenous anticoagulation and inflammation. Nonetheless, recognizing that cells are the fundamental units of structure and function in all living organisms, the present review aimed to distill our understanding of coagulopathy post-TBI by elucidating the intricate cellular mechanisms involving endothelial cells, neutrophils and platelets. Additionally, this study evaluates the strengths and weaknesses of various diagnostic tools and discusses the characteristics of pharmacological treatments and potential therapies for patients with TBI and coagulation disorders. The aim of this review is to amalgamate recent updates in mechanistic research and innovative diagnostic and therapeutic methodologies, thereby fostering the progression of precision medicine within this specialized domain.

Evolution of dirofilariasis diagnostic techniques from traditional morphological analysis to molecular-based techniques: a comprehensive review

Dirofilariasis, caused by the nematode Dirofilaria spp., poses significant challenges in diagnosis due to its diverse clinical manifestations and complex life cycle. This comprehensive literature review focuses on the evolution of diagnostic methodologies, spanning from traditional morphological analyses to modern emerging techniques in the context of dirofilariasis diagnosis. The review traces the historical progression of diagnostic modalities, encompassing traditional approaches such as microscopic examination, serological tests (including ELISA and IFA), radiographic imaging, ultrasonography, and necropsy, which laid the foundation for subsequent advancements. The integration of molecular diagnostics marks a significant turning point in dirofilariasis diagnosis with the adoption of polymerase chain reaction (PCR) assays and real-time PCR (qPCR) facilitating enhanced sensitivity and specificity. Furthermore, recent strides in next-generation sequencing (NGS) technologies, including whole–genome sequencing (WGS), targeted sequencing (TS), metagenomic sequencing (MS), and RNA sequencing (transcriptome sequencing), have revolutionized the landscape of dirofilariasis diagnostics. Emerging techniques such as loop-mediated isothermal amplification (LAMP), digital PCR (dPCR), and digital microfluidics are also explored for their potential to augment diagnostic accuracy. The review addresses challenges associated with standardizing molecular protocols, tackling false positives/negatives, and discusses the advantages and limitations of each technique. By providing a comprehensive overview of dirofilariasis diagnostic strategies, from traditional to cutting-edge methods, this review aims to enhance understanding of the disease’s diagnostic landscape. The insights gained have implications for improved disease management and guide future research endeavors toward refining diagnostic protocols and advancing therapeutic interventions.

Advancing Prostate Cancer Diagnosis: A Deep Learning Approach for Enhanced Detection in MRI Images.

Prostate cancer remains a leading cause of mortality among men globally, necessitating advancements in diagnostic methodologies to improve detection and treatment outcomes. Magnetic Resonance Imaging has emerged as a crucial technique for the detection of prostate cancer, with current research focusing on the integration of deep learning frameworks to refine this diagnostic process. This study employs a comprehensive approach using multiple deep learning models, including a three-dimensional (3D) Convolutional Neural Network, a Residual Network, and an Inception Network to enhance the accuracy and robustness of prostate cancer detection. By leveraging the complementary strengths of these models through an ensemble method and soft voting technique, the study aims to achieve superior diagnostic performance. The proposed methodology demonstrates state-of-the-art results, with the ensemble model achieving an overall accuracy of 91.3%, a sensitivity of 90.2%, a specificity of 92.1%, a precision of 89.8%, and an F1 score of 90.0% when applied to MRI images from the SPIE-AAPM-NCI PROSTATEx dataset. Evaluation of the models involved meticulous pre-processing, data augmentation, and the use of advanced deep-learning architectures to analyze the whole MRI slices and volumes. The findings highlight the potential of using an ensemble approach to significantly improve prostate cancer diagnostics, offering a robust and precise tool for clinical applications.

Infection Inspection: using the power of citizen science for image-based prediction of antibiotic resistance in Escherichia coli treated with ciprofloxacin

Antibiotic resistance is an urgent global health challenge, necessitating rapid diagnostic tools to combat its threat. This study uses citizen science and image feature analysis to profile the cellular features associated with antibiotic resistance in Escherichia coli. Between February and April 2023, we conducted the Infection Inspection project, in which 5273 volunteers made 1,045,199 classifications of single-cell images from five E. coli strains, labelling them as antibiotic-sensitive or antibiotic-resistant based on their response to the antibiotic ciprofloxacin. User accuracy in image classification reached 66.8 ± 0.1%, lower than our deep learning model's performance at 75.3 ± 0.4%, but both users and the model were more accurate when classifying cells treated at a concentration greater than the strain’s own minimum inhibitory concentration. We used the users’ classifications to elucidate which visual features influence classification decisions, most importantly the degree of DNA compaction and heterogeneity. We paired our classification data with an image feature analysis which showed that most of the incorrect classifications happened when cellular features varied from the expected response. This understanding informs ongoing efforts to enhance the robustness of our diagnostic methodology. Infection Inspection is another demonstration of the potential for public participation in research, specifically increasing public awareness of antibiotic resistance.

Gene pointNet for tumor classification

The rising incidence of cancer underscores the imperative for innovative diagnostic and prognostic methodologies. This study delves into the potential of RNA-Seq gene expression data to enhance cancer classification accuracy. Introducing a pioneering approach, we model gene expression data as point clouds, capitalizing on the data's intrinsic properties to bolster classification performance. Utilizing PointNet, a typical technique for processing point cloud data, as our framework's cornerstone, we incorporate inductive biases pertinent to gene expression and pathways. This integration markedly elevates model efficacy, culminating in developing an end-to-end deep learning classifier with an accuracy rate surpassing 99%. Our findings not only illuminate the capabilities of AI-driven models in the realm of oncology but also highlight the criticality of acknowledging biological dataset nuances in model design. This research provides insights into application of deep learning in medical science, setting the stage for further innovation in cancer classification through sophisticated biological data analysis. The source code for our study is accessible at: https://github.com/cialab/GPNet.

Diagnostic methodology for vehicles equipped with antilock braking system and electronic stability control

Currently, several automotive companies are joining forces through the analysis and development of active safety systems for hazard avoidance and mitigation; from which according to the current regulations in the European Union the Anti-lock Braking System (ABS) and the Electronic Stability Control (ESC) outstand as mandatory for each new vehicle. The above has motivated the study of these two safety systems. Then, this research examines the ABS and ESC systems through a proposed diagnostic methodology, which identifies the main effect of these systems on the vehicle dynamics. To achieve this goal a software was developed, looking for processing the data acquired during the development of some specific vehicle maneuvers. The diagnostic results expose: (1) an indicator about the ABS/ESC systems performance; and (2) the impact of these systems on the vehicle dynamics; also, some of the results here obtained can also be taken as reference for the Brake Assistant System and the Anti-Roll Control analysis.

The diversity of clinical Mycobacterium abscessus isolates in morphology, glycopeptidolipids and infection rates in a macrophage model.

Introduction. Mycobacterium abscessus (MABS) is a pathogenic bacterium that can cause severe lung infections, particularly in individuals with cystic fibrosis. MABS colonies can exhibit either a smooth (S) or rough (R) morphotype, influenced by the presence or absence of glycopeptidolipids (GPLs) on their surface, respectively. Despite the clinical significance of these morphotypes, the relationship between GPL levels, morphotype and the pathogenesis of MABS infections remains poorly understood.Gap statement. The mechanisms and implications of GPL production and morphotypes in clinical MABS infections are unclear. There is a gap in understanding their correlation with infectivity and pathogenicity, particularly in patients with underlying lung disease.Aim. This study aimed to investigate the correlation between MABS morphology, GPL and infectivity by analysing strains from cystic fibrosis patients' sputum samples.Methodology. MABS was isolated from patient sputum samples and categorized by morphotype, GPL profile and replication rate in macrophages. A high-content ex vivo infection model using THP-1 cells assessed the infectivity of both clinical and laboratory strains.Results. Our findings revealed that around 50 % of isolates displayed mixed morphologies. GPL analysis confirmed a consistent relationship between GPL content and morphotype that was only found in smooth isolates. Across morphotype groups, no differences were observed in vitro, yet clinical R strains were observed to replicate at higher levels in the THP-1 infection model. Moreover, the proportion of infected macrophages was notably higher among clinical R strains compared to their S counterparts at 72 h post-infection. Clinical variants also infected THP-1 cells at significantly higher rates compared to laboratory strains, highlighting the limited translatability of lab strain infection data to clinical contexts.Conclusion. Our study confirmed the general correlation between morphotype and GPL levels in smooth strains yet unveiled more variability within morphotype groups than previously recognized, particularly during intracellular infection. As the R morphotype is the highest clinical concern, these findings contribute to the expanding knowledge base surrounding MABS infections, offering insights that can steer diagnostic methodologies and treatment approaches.

Presentation of mitral valve cleft with concurrent atrial septal defect and ventricular septal defect detected by three-dimensional transesophageal echocardiography: a case report

BackgroundCleft in the mitral valve leaflet is a primary cause of congenital mitral regurgitation, stemming from developmental anomalies in the mitral valve and frequently associated with other congenital heart defects. Concurrent presence of cleft in mitral valve leaflet with atrial septal defect and ventricular septal defect is relatively rare. Echocardiography, especially transesophageal echocardiography, is essential in diagnosing cleft mitral valve leaflet and related congenital heart defects, providing critical, detailed imagery for accurate assessment. This study presents a young female patient whose anterior mitral cleft, along with atrial septal defect and ventricular septal defect, was revealed through three-dimensional transesophageal echocardiography.Case presentationA 25-year-old Iranian female, experiencing progressive dyspnea and diminished physical capacity over 3 months, was referred to our hospital. Initial examination and transthoracic echocardiography indicated severe mitral regurgitation. Further evaluation with transesophageal echocardiography corroborated these findings and identified a cleft in the anterior mitral valve leaflet, coupled with mild left ventricular enlargement and significant left atrial enlargement. The complexity of the patient’s condition was heightened by the diagnosis of cleft mitral valve leaflet in conjunction with atrial septal defect and ventricular septal defect, showing the complex nature of congenital defects.ConclusionThis case emphasizes the critical role of transthoracic echocardiography in diagnosing cleft of mitral valve leaflet and associated cardiac anomalies, showcasing its superiority over transthoracic echocardiography for detailed visualization of cardiac structures. The identification of multiple congenital defects highlights the necessity for a comprehensive diagnostic approach to manage and treat patients with complex congenital heart diseases effectively. Future research should aim to refine diagnostic methodologies to enhance patient outcomes for cleft of mitral valve leaflets and related congenital conditions.

The utilization of blood serum ATR-FTIR spectroscopy for the identification of gastric cancer

Gastric cancer represents a significant public health challenge, necessitating advancements in early diagnostic methodologies. This investigation employed attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy to conduct a multivariate analysis of human serum. The study encompassed the examination of blood samples from 96 individuals diagnosed with gastric cancer and 96 healthy volunteers. Principal component analysis (PCA) was utilized to interpret the infrared spectral data of the serum samples. Specific spectral bands exhibiting intensity variations between the two groups were identified. The infrared spectral ranges of 3500 ~ 3000 cm⁻1, 1700 ~ 1600 cm⁻1, and 1090 ~ 1070 cm⁻1 demonstrated significant diagnostic value for gastric cancer, likely attributable to differences in protein conformation and nucleic acids. By employing machine learning algorithms to differentiate between gastric cancer patients (n = 96) and healthy controls (n = 96), we achieved a sensitivity of up to 89.7% and a specificity of 87.2%. Receiver operating characteristic (ROC) analysis yielded an area under the curve (AUC) of 0.901. These findings underscore the potential of our serum-based ATR-FTIR spectroscopy examination method as a straightforward, minimally invasive, and reliable diagnostic test for the detection of gastric cancer.

Common Non-Rheumatic Medical Conditions Mimicking Fibromyalgia: A Simple Framework for Differential Diagnosis.

Fibromyalgia (FM) is a chronic non-inflammatory disorder mainly characterized by widespread musculoskeletal pain, fatigue, sleep disturbances, and a constellation of other symptoms. For this reason, delineating a clear distinction between pure FM and FM-like picture attributable to other common diseases can be extremely challenging. Physicians must identify the most significant confounders in individual patients and implement an appropriate diagnostic workflow, carefully choosing a minimal (but sufficient) set of tests to be used for identifying the most plausible diseases in the specific case. This article discusses prevalent non-rheumatological conditions commonly observed in the general population that can manifest with clinical features similar to primary FM. Given their frequent inclusion in the differential diagnosis of FM patients, the focus will be on elucidating the distinctive clinical characteristics of each condition. Additionally, the most cost-effective and efficient diagnostic methodologies for accurately discerning these conditions will be examined.

PIONEERING ADVANCEMENTS IN UROLOGY: ARTIFICIAL INTELLIGENCE, GENOMIC INSIGHTS, NOVEL TREATMENTS, DIAGNOSTICS, AND TARGETED THERAPIES TRANSFORMING PATIENT CARE

Recent advancements in urology are transforming patient care by integrating artificial intelligence (AI), genomic insights, and novel therapeutic approaches. This review explores the significant strides made in the field, particularly the application of AI in enhancing diagnostic precision and treatment strategies. The role of genomics in identifying molecular targets and pathways has enabled the development of targeted and personalized treatments, revolutionizing the management of urological disorders. We highlight novel pharmacological and non-pharmacological treatments that have shown efficacy in improving patient outcomes. Additionally, advancements in diagnostic methodologies, including non-invasive tests and precision imaging, have facilitated early and accurate disease detection. The review also addresses the emerging trends in biological therapies and the use of portable monitoring devices that support continuous care. By synthesizing current research, this article provides a comprehensive overview of the cutting-edge technologies and approaches that are setting the stage for future innovations in urology, aiming to enhance patient outcomes and optimize care delivery.

Investigating the association between symptoms and functional activity in brain regions in schizophrenia: A cross-sectional fmri-based neuroimaging study

Schizophrenia is a persistent neurological disorder profoundly affecting cognitive, emotional, and behavioral functions, prominently characterized by delusions, hallucinations, disordered speech, and abnormal motor activity. These symptoms often present diagnostic challenges due to their overlap with other forms of psychosis. Therefore, the implementation of automated diagnostic methodologies is imperative. This research leverages Functional Magnetic Resonance Imaging (fMRI), a neuroimaging modality capable of delineating functional activations across diverse brain regions. Furthermore, the utilization of evolving machine learning techniques for fMRI data analysis has significantly progressive. Here, our study stands as a novel attempt, focusing on the comprehensive assessment of both classical and atypical symptoms of schizophrenia. We aim to uncover associated changes in brain functional activity. Our study encompasses two distinct fMRI datasets (1.5T and 3T), each comprising 34 schizophrenia patients for the 1.5T dataset and 25 schizophrenia patients for the 3T dataset, along with an equal number of healthy controls. Machine learning algorithms are applied to assess data subsets, enabling an in-depth evaluation of the current functional condition concerning symptom impact. The identified voxels contribute to determining the brain regions most influenced by each symptom, as quantified by symptom intensity. This rigorous approach has yielded various new findings while maintaining an impressive classification accuracy rate of 97 %. By elucidating variations in activation patterns across multiple brain regions in individuals with schizophrenia, this study contributes to the understanding of functional brain changes associated with the disorder. The insights gained may inform differential clinical interventions and provide a means of assessing symptom severity accurately, offering new avenues for the management of schizophrenia.

Pseudobulbar Affect in Patients with Multiple Sclerosis: A Systematic Review

Multiple sclerosis (MS) is associated with a high prevalence of emotional disorders affecting the health-related quality of life of patients and their families. Pseudobulbar affect (PBA), also referred to as pathological laughing and crying, is an under-recognized and under-treated co-morbidity. We conducted a systematic literature review of 16 studies to determine the prevalence and clinical characteristics of PBA in patients with MS of all ages. Based on conservative figures available from 8/16 studies, the prevalence of PBA in the context of MS was found to range between 2% and 10% (median 10%), with higher percentages in the female population. Possible reasons for the observed variability in the prevalence data include heterogeneity of the diagnostic methodologies and common presence of confounding factors, such as co-morbid affective disorders. The clinical presentation was found to be comparable to that of PBA in the context of other neurological disorders, as it reflected the location of underlying lesions (especially in the brainstem) rather than the associated pathology. Clinicians should be prompted to consider PBA in the differential diagnosis of emotional disorders in the context of MS by using both clinical criteria and psychometric instruments. Further studies should be conducted to develop standardized diagnostic protocols and to optimize therapeutic approaches for the clinical management of this patient population.

A pre-trained language model for emergency department intervention prediction using routine physiological data and clinical narratives

IntroductionThe urgency and complexity of emergency room (ER) settings require precise and swift decision-making processes for patient care. Ensuring the timely execution of critical examinations and interventions is vital for reducing diagnostic errors, but the literature highlights a need for innovative approaches to optimize diagnostic accuracy and patient outcomes. In response, our study endeavors to create predictive models for timely examinations and interventions by leveraging the patient’s symptoms and vital signs recorded during triage, and in so doing, augment traditional diagnostic methodologies. MethodsFocusing on four key areas—medication dispensing, vital interventions, laboratory testing, and emergency radiology exams, the study employed Natural Language Processing (NLP) and seven advanced machine learning techniques. The research was centered around the innovative use of BioClinicalBERT, a state-of-the-art NLP framework. ResultsBioClinicalBERT emerged as the superior model, outperforming others in predictive accuracy. The integration of physiological data with patient narrative symptoms demonstrated greater effectiveness compared to models based solely on textual data. The robustness of our approach was confirmed by an Area Under the Receiver Operating Characteristic curve (AUROC) score of 0.9. ConclusionThe findings of our study underscore the feasibility of establishing a decision support system for emergency patients, targeting timely interventions and examinations based on a nuanced analysis of symptoms. By using an advanced natural language processing technique, our approach shows promise for enhancing diagnostic accuracy. However, the current model is not yet fully mature for direct implementation into daily clinical practice. Recognizing the imperative nature of precision in the ER environment, future research endeavors must focus on refining and expanding predictive models to include detailed timely examinations and interventions. Although the progress achieved in this study represents an encouraging step towards a more innovative and technology-driven paradigm in emergency care, full clinical integration warrants further exploration and validation.

Electromagnetic Signal Analysis for Electrical Fault Diagnosis in Synchronous Generators

This study investigates the enhancement of fault diagnostics in synchronous generators by incorporating electromagnetic signal analysis with conventional diagnostic methodologies. The critical role of synchronous generators in maintaining power system stability and efficiency in industrial and power plant environments is underscored. Finite element modeling (FEM) is employed to simulate various fault conditions, such as stator and rotor winding faults. This study proposes a method that integrates stator current and stray magnetic flux analysis to identify five distinct types of short-circuit faults in stator and rotor windings, thereby enhancing the diagnostic capabilities for electrical faults in synchronous generators. This approach successfully identifies these electrical faults using non-invasive methods, offering a cost-effective solution that enhances fault detection. These findings are based on simulation results and serve as a preliminary stage for further validation through experimental studies. This integration is crucial for the development of efficient diagnostic systems that are capable of adapting to complex fault patterns, reducing human intervention, and streamlining maintenance operations, thus improving the reliability of synchronous generators globally.

A Multi-Modal Machine Learning Methodology for Predicting Solitary Pulmonary Nodule Malignancy in Patients Undergoing PET/CT Examination

This study explores a multi-modal machine-learning-based approach to classify solitary pulmonary nodules (SPNs). Non-small cell lung cancer (NSCLC), presenting primarily as SPNs, is the leading cause of cancer-related deaths worldwide. Early detection and appropriate management of SPNs are critical to improving patient outcomes, necessitating efficient diagnostic methodologies. While CT and PET scans are pivotal in the diagnostic process, their interpretation remains prone to human error and delays in treatment implementation. This study proposes a machine-learning-based network to mitigate these concerns, integrating CT, PET, and manually extracted features in a multi-modal manner by integrating multiple image modalities and tabular features). CT and PET images are classified by a VGG19 network, while additional SPN features in combination with the outputs of VGG19 are processed by an XGBoost model to perform the ultimate diagnosis. The proposed methodology is evaluated using patient data from the Department of Nuclear Medicine of the University Hospital of Patras in Greece. We used 402 patient cases with human annotations to internally validate the model and 96 histopathological-confirmed cases for external evaluation. The model exhibited 97% agreement with the human readers and 85% diagnostic performance in the external set. It also identified the VGG19 predictions from CT and PET images, SUVmax, and diameter as key malignancy predictors. The study suggests that combining all available image modalities and SPN characteristics improves the agreement of the model with the human readers and the diagnostic efficiency.

Eagle’s syndrome: Modern systematic review and presentation of radiographic cases

Introduction: The styloid process is a bony structure of the base of the skull, which can undergo pathological structural changes. So, its elongation may possibly provoke Eagle’s syndrome, also known as stylohyoid syndrome, which is a head and neck pathological condition that rarely manifests with severely painful symptomatology. Objective: The present literature review aims to deal with the understanding of the pathophysiology, as well as the investigation of the diagnostic and therapeutic facts regarding Eagle’s syndrome. Materials – Methods: An extended research of modern literature through papers published in scientific journals has been conducted, deploying the databases PubMed and Google Scholar. Results: Eagle’s syndrome is a rare pathological condition, whose prevalence is approximately 4% of general population. In the literature, several pathogenetic patterns have been reported and they are divided into acquired and idiopathic biochemical mechanisms. In order to diagnose the disease, one should take the painful symptoms referred by the patient into consideration, in conjunction with specific clinical tests. However, the final diagnosis is set after the radiological evaluation. Eagle’s syndrome manifests with non-specific symptomatology, thus it should be differentiated from other diseases and conditions of the head and neck anatomical area. Regarding the therapy of the syndrome, a vast range of conservative choices is available nowadays, mainly basedon drugs, while at the same time there are surgical techniques with comparatively better long-term outcome. Conclusions: There is an expressively increased interest, regarding the diagnosis and the therapeutics of Eagle’s syndrome. Modern medical sciences attempt to gradually advance the diagnostic methodology and constantly improve the therapeutic choices of this disease. The ultimate purpose is the more effective management of the patients and the enhancement of their life quality.

The effect of endometriosis on the fertility of women

Endometriosis is defined by the presence of ectopic endometrial tissue outside the uterus, frequently located on pelvic organs such as the fallopian tubes and ovaries, and occasionally beyond the pelvic region. This condition manifests as dysmenorrhea, chronic pelvic pain, dyspareunia, and subfertility. Despite extensive research, the etiology and pathogenesis of endometriosis remain unclear, with laparoscopy being the definitive diagnostic method. The association between endometriosis and infertility has been extensively debated. Endometriosis can impair fertility by disrupting embryo implantation, altering hormone levels, and compromising oocyte quality. This literature review aims to examine the effects of endometriosis on female fertility. The review encompasses documents from clinical trials with control groups involving 196 to 22,416 reproductive-age participants (25-42 years), and case studies published over the past thirty-seven years from various regions (USA, Australia, Turkey, Africa, and Europe. Reputable databases such as BMJ, NEJM, Elsevier, AJR, Medline, and PubMed were utilized, with references compiled in the bibliography. A risk-benefit analysis indicates that up to 50% of women with endometriosis experience infertility. Consensus on treatment options remains elusive. The relationship between endometriosis and infertility is supported by studies of both fertile and infertile women, animal studies, donor sperm studies, and in vitro fertilization outcomes. Diagnostic methodologies based on endometrial changes are providing insights into potential mechanisms of infertility, especially in women with milder disease. However, clinical management of endometriosis-related infertility has not shown conclusive success beyond in vitro fertilization.

Карьерные ориентации студентов — будущих педагогов на завершающем этапе вузовской подготовки

<p><em>One of the areas of studying the motivational bases of choosing and entering the teaching profession is the study of career orientations of students at different stages of professional training. The article presents the results of a study conducted with 36 fifth year undergraduate students in the Field of training 44.03.05 Pedagogical Education (double major) of Smolensk State University. The use of the diagnostic methodology “Career Anchors” by E. Schein and a questionnaire to determine socio-demographic and motivational-professional characteristics showed that the dominant career orientations of graduates correlate with both the values of social utility (service) and the motives of external utility (job stability, integration of lifestyles). Subsequent analysis showed that students who consciously chose the teaching profession and want to work in their specialty also have higher scores on the scale of professional competence. The subjects are aware of the social significance, creative nature and complexity of the teaching profession. They attach great importance to stable working and living conditions for professional development.</em></p>