Lung cancer with the highest number of new cases diagnosed in Europe and in Poland, remains an example of malignancy with a very poor prognosis despite the recent progress in medicine. Different treatment strategies are now available for cancer therapy based on its type, molecular subtype and other factors including overall health, the stage of disease and cancer molecular profile. Immunotherapy is emerging as a potential addition to surgery, chemotherapy, radiotherapy or other targeted therapies, but also considered a mainstay therapy mode. This combination is an area of active investigation in order to enhance efficacy and overcome resistance. Due to the complexity and dynamic of cancer’s ecosystem, novel therapeutic targets and strategies need continued research into the cellular and molecular mechanisms within the tumour microenvironment. From the genetic point of view, several signatures ranging from a few mutated genes to hundreds of them have been identified and associated with therapy resistance and metastatic potential. ML techniques and AI can enhance the predictive potential of genetic signatures and model the prognosis. Here, we present the overview of already existing treatment approaches, the current findings of key aspects of immunotherapy, such as immune checkpoint inhibitors (ICIs), existing molecular biomarkers like PD-L1 expression, tumour mutation burden, immunoscore, and neoantigens, as well as their roles as predictive markers for treatment response and resistance.

Background/Objectives: Musculoskeletal disorders and their surgical treatment significantly affect global disability, healthcare utilization, and costs. Artificial intelligence (AI) is a key enabler of data-driven musculoskeletal care. Their applications include diagnostic imaging, surgical planning, risk prediction, rehabilitation, and digital health ecosystems. This narrative review synthesizes current evidence on the use of AI in orthopaedics and musculoskeletal care across five areas: diagnostic imaging, surgical planning and intraoperative augmentation, predictive analytics and patient-reported outcomes, rehabilitation intelligence and teleorthopaedics, and system-level management. An additional task is to identify translational gaps and priorities for safe, ethical, and equitable implementation of AI. Methods: A structured narrative review was conducted using targeted searches in PubMed, Scopus, and Web of Science supplemented by semantic and citation-based explorations in Semantic Scholar, OpenAlex, and Google Scholar. The main search period was January 2019 to December 2025. The retrieved peer-reviewed articles were analyzed for clinical relevance to human musculoskeletal care, quantitative outcomes, and the translational implications of the results. From the broader pool of eligible publications, 40 clinically relevant studies were selected for detailed synthesis covering imaging, surgical planning, predictive modeling, rehabilitation, and system-level applications. Owing to the significant heterogeneity in the model architectures, datasets, and endpoints, the results were organized into five predefined thematic areas. Results: The most mature evidence is for AI-assisted detection of bone fractures on radiographs, identification of implants, and use of sizing templates in preoperative planning for arthroplasty, where deep learning systems have achieved expert-level diagnostic performance (e.g., fracture detection sensitivity of approximately 90% and specificity of approximately 92% and implant identification accuracy of 97–99%) and improved the accuracy of preoperative planning compared to conventional templating. AI-based planning increases the likelihood of reducing intraoperative corrections, shortening surgery time, reducing blood loss, and improving the final functional outcomes. Predictive models can support the stratification of risk for complications, rehospitalizations, and patient-reported outcomes, although external validation remains limited and is often single-center at this stage of research. Emerging applications in rehabilitation and teleorthopaedics, including sensor-based monitoring and learning systems integrated with Patient-Reported Outcome Measures (PROMs), are conceptually promising, but are mainly limited to feasibility or pilot studies. Conclusions: AI is beginning to influence musculoskeletal care, moving beyond pattern recognition toward integrated, patient-centered decision support throughout the perioperative and rehabilitation periods. Its widespread use remains constrained by limited multicenter validation, dataset bias, algorithmic opacity, and immature regulatory and governance frameworks. Future work should prioritize prospective multicenter impact studies, repeatable revalidation of local models, integration of PROM and teleorthopedic data with health learning systems, and adaptation to changing regulatory requirements to enable safe, ethical, effective, and equitable implementation in routine orthopedic practice.

There is an interplay between plasma cells, endometritis, and infertility, particularly in the context of in vitro fertilization (IVF) failure. This narrative literature review explains the pathophysiology of endometritis, detailing the involvement of various immune cells, cytokines, and chemokines in the regulation of inflammatory responses within the uterine endometrium. Here, we discuss the physiological role of plasma cells in immunity and their detection as markers of chronic endometritis, a disease associated with reproductive disorders. Our study also highlights the importance of CD138 immunohistochemical staining in the diagnosis of chronic endometritis, emphasizing the presence of plasma cells in endometrial tissue and its association with infertility and recurrent implantation failure. Of particular interest are the proposed diagnostic criteria for chronic endometritis based on the presence of plasma cells and studies that suggest a threshold for diagnosing this condition. We highlight the importance of examining the regenerative potential of endometrial stem cells in the treatment of infertility related to endometrial disorders.

As a scientific community we assumed that exome sequencing will elucidate the basis of most heritable diseases. However, it turned out it was not the case; therefore, attention has been increasingly focused on the non-coding sequences that encompass 98% of the genome and may play an important regulatory function. The first WGS-based datasets have already been released including underrepresented populations. Although many databases contain pooled data from several cohorts, recently the importance of local databases has been highlighted. Genomic databases are not only collecting data but may also contribute to better diagnostics and therapies. They may find applications in population studies, rare diseases, oncology, pharmacogenetics, and infectious and inflammatory diseases. Further data may be analysed with Al technologies and in the context of other omics data. To exemplify their utility, we put a highlight on the Polish genome database and its practical application.

Introduction: Bariatric surgery is the mainstay of treatment of obesity, with a proven, long-lasting effect on body weight reduction and remission of co-morbidities. Sleep disorders, including insomnia, and deteriorated sleep quality and duration are associated with obesity, and a reduction in body weight can be associated with a reduction in prevalence of sleep disorders. The purpose of this study was to assess the influence of laparoscopic sleeve gastrectomy (LSG) on the prevalence and intensity of different sleep disturbances. Methods: This observational prospective study included 80 patients qualified for bariatric surgery who filled in a questionnaire with a set of structured questions about different sleep disturbances, such as difficulties in falling asleep, night awakenings, early morning awakenings, snoring, and nightmares, as well as eating at night and daytime dysfunction, supplemented with Athens Insomnia Scale (AIS), before and 6 months after bariatric surgery. Results: There was a statistically significant reduction in incidence of night awakenings, with 40.00% of participants reporting night awakenings before surgery and, respectively, 25.00% after surgery. A significant reduction was also observed in the rate of patients who reported snoring, with 60.00% before the surgery and 38.75% after the surgery (p < 0.05). There was a correlation present between estimated weight loss % (EWL%) and reduction in snoring (p < 0.05). The mean total AIS score before surgery was 7.21 and 5.99 after surgery, and the change was statistically significant (p < 0.05). A total AIS score of 8 or more, the cutoff score for insomnia diagnosis according to the Polish validation of the Athens Insomnia Scale, was present in 44.16% of cases before surgery and in 38.00% after surgery (p = 0.52). There was a significant difference in the incidence of awakening during the night score before and after surgery (p < 0.05; CI 0.022–0.341), sleep quality (p < 0.05; CI 0.0105–0.4311), well-being during the day (p < 0.05; CI 0.0273–0.4143), and sleepiness during the day (p < 0.05; CI 0.101–0.444). Conclusions: LSG is observed to have a positive effect on selected sleep disturbances and insomnia remission in patients with obesity, measured by a significant reduction in Athens Insomnia Scale scores in follow-up 6 months after surgery. Additionally, patients after bariatric surgery reported less night awakenings and there was a lower rate of snoring. Therefore, LSG can be considered an effective therapeutic tool for insomnia in patients with obesity.