The CD34 protein was identified almost four decades ago as a biomarker for hematopoietic stem cell progenitors. CD34 expression of these stem cells has been exploited for therapeutic purposes in various hematological disorders. In the last few decades, studies have revealed the presence of CD34 expression on other types of cells with non-hematopoietic origins, such as interstitial cells, endothelial cells, fibrocytes, and muscle satellite cells. Furthermore, CD34 expression may also be found on a variety of cancer stem cells. Nowadays, the molecular functions of this protein have been involved in a variety of cellular functions, such as enhancing proliferation and blocking cell differentiation, enhanced lymphocyte adhesion, and cell morphogenesis. Although a complete understanding of this transmembrane protein, including its developmental origins, its stem cell connections, and other functions, is yet to be achieved. In this paper, we aimed to carry out a systematic analysis of the structure, functions, and relationship with cancer stem cells of CD34 based on the literature overview.

Despite incessant research, colorectal cancer (CRC) is still one of the most common causes of fatality in both men and women worldwide. Over time, advancements in medical treatments have notably enhanced the survival rates of patients with colorectal cancer. Managing metastatic CRC involves a complex tradeoff between the potential benefits and adverse effects of treatment, considering factors like disease progression, treatment toxicity, drug resistance, and the overall impact on the patient’s quality of life. An increasing body of evidence highlights the significance of the cancer stem cell (CSC) concept, proposing that CSCs occupy a central role in triggering cancer. CSCs have been a focal point of extensive research in a variety of cancer types, including CRC. Colorectal cancer stem cells (CCSCs) play a crucial role in tumor initiation, metastasis, and therapy resistance, making them potential treatment targets. Various methods exist for isolating CCSCs, and understanding the mechanisms of drug resistance associated with them is crucial. This paper offers an overview of the current body of research pertaining to the comprehension of CSCs in colorectal cancer.

Inguinal hernia repair is one of the most commonly performed surgical procedures worldwide, with over 20 million cases annually. The evolution of hernia surgery has transitioned from tension-based techniques to tension-free approaches, significantly reducing recurrence rates. This review explores the history, advancements, and current trends in minimally invasive inguinal hernia repair, focusing on laparoscopic techniques such as transabdominal preperitoneal (TAPP), totally extraperitoneal (TEP), single-incision laparoscopic surgery (SILS), and robotic-assisted repair. The importance of prosthetic meshes is emphasized, detailing their mechanical properties, pore size, weight classifications, and biocompatibility. Additionally, various mesh fixation methods—including tacks, sutures, and glues—are analyzed, with a discussion on their impact on postoperative complications such as chronic pain, adhesions, and infection risk. The debate between TAPP and TEP techniques is examined, highlighting the ongoing quest to determine the most effective approach. Emerging advancements, including drug-loaded meshes and dual-layered prosthetics, aim to improve integration and reduce complications. Despite significant progress, no universally superior technique or mesh exists, underscoring the need for individualized surgical approaches. Future research should focus on optimizing materials, refining fixation strategies, and enhancing patient outcomes in minimally invasive hernia repair.

Periprosthetic joint infection (PJI) is a serious complication that can occur after joint arthroplasty, such as hip or knee replacement surgeries. It involves the invasion of the periprosthetic space by pathogens, leading to severe inflammation and often requiring complex medical intervention. PJI is associated with significant morbidity, increased healthcare costs, and a reduced quality of life for patients. This study aims to evaluate the performance of multiple supervised machine learning models in predicting PJI using clinical and demographic data collected from patients who underwent joint arthroplasty. Eight supervised machine learning models—Logistic Regression, Random Forest, XGBoost, Artificial Neural Network (ANN), k-Nearest Neighbors (KNN), AdaBoost, Gaussian Naive Bayes (GNB), and Stochastic Gradient Descent (SGD)—were trained and tested on a dataset of 27,854 patients. Models were evaluated using accuracy, precision, recall, specificity, F1 score, and area under the ROC curve (AUC). Random Forest and XGBoost showed the best overall performance, with high accuracy and balanced metrics across all evaluation criteria. KNN also performed strongly, particularly in minimizing misclassifications. GNB and SGD yielded weaker results, with higher error rates. Random Forest, XGBoost, and KNN are the most promising models for clinical implementation in PJI prediction. Their robust performance may support earlier diagnosis and improved patient outcomes in orthopedic care. This study demonstrates that machine learning models—particularly Random Forest and XGBoost—can accurately predict periprosthetic joint infection (PJI) using structured electronic health record data. By integrating these models into preoperative assessment workflows, clinicians may be able to identify high-risk patients earlier, personalize prophylactic strategies, and reduce infection-related morbidity. The implementation of these predictive tools has the potential to enhance clinical decision-making, improve surgical outcomes, and optimize the use of healthcare resources in orthopedic practice.

The development of general anesthesia techniques and anesthetic substances has opened new horizons for the expansion and improvement of surgical techniques. Nevertheless, more complex surgical procedures have brought a higher complexity and longer duration for general anesthesia, which has led to a series of adverse events such as hemodynamic instability, under- or overdosage of anesthetic drugs, and an increased number of post-anesthetic events. In order to adapt the anesthesia according to the particularities of each patient, the multimodal monitoring of these patients is highly recommended. Classically, general anesthesia monitoring consists of the analysis of vital functions and gas exchange. Multimodal monitoring refers to the concomitant monitoring of the degree of hypnosis and the nociceptive-antinociceptive balance. By titrating anesthetic drugs according to these parameters, clinical benefits can be obtained, such as hemodynamic stabilization, the reduction of awakening times, and the reduction of postoperative complications. Another important aspect is the impact on the status of inflammation and the redox balance. By minimizing inflammatory and oxidative impact, a faster recovery can be achieved that increases patient safety. The purpose of this literature review is to present the most modern multimodal monitoring techniques to discuss the particularities of each technique.

With the development of 3D bioprinting and the creation of innovative biocompatible materials, several new approaches have brought advantages to patients and surgical teams. Increasingly more bone defects are now treated using 3D-bioprinted prostheses and implementing new solutions relies on the ability of engineers and medical teams to identify methods of anchoring 3D-printed prostheses and to reveal the potential influence of bioactive materials on surrounding tissues. In this paper, we described why limb salvage surgery based on 3D bioprinting is a reliable and effective alternative to amputations, and why this approach is considered the new standard in modern medicine. The preliminary results of 3D bioprinting in one of the most challenging fields in surgery are promising for the future of machine-based medicine, but also for the possibility of replacing various parts from the human body with bioactive-based constructs. In addition, besides the materials and constructs that are already tested and applied in the human body, we also reviewed bioactive materials undergoing in vitro or in vivo testing with great potential for human applications in the near future. Also, we explored the recent advancements in clinically available 3D-bioprinted constructs and their relevance in this field.