Subfertility, a prevalent and emotionally taxing condition affecting couples worldwide, has garnered increasing attention in reproductive health. While its etiology remains multifaceted, emerging research has explored the role of serum homocysteine levels and nutrient deficiencies in influencing subfertility. This comprehensive review synthesizes current knowledge, beginning with an introduction to subfertility and the significance of investigating serum homocysteine levels. It proceeds to elucidate the role of nutrient deficiencies, particularly folate and vitamin B12, in homocysteine metabolism and examines existing research linking homocysteine to subfertility. The review explores potential mechanisms underlying this relationship, addressing the variability in study findings and their contributing factors. Implications for clinical practice, including assessing serum homocysteine levels, nutritional interventions, and personalized medicine, are discussed. Moreover, the review underscores the importance of ongoing research. It offers a call to action for advancing our understanding of subfertility and improving the lives of individuals and couples navigating reproduction challenges.

Epilepsy surgery is the treatment of choice for drug-resistant epilepsy patients, but up to 50% of patients continue to have seizures one year after the resection. In order to aid presurgical planning and predict postsurgical outcome on a patient-by-patient basis, we developed a framework of individualized computational models that combines epidemic spreading with patient-specific connectivity and epileptogeneity maps: the Epidemic Spreading Seizure and Epilepsy Surgery framework (ESSES). ESSES parameters were fitted in a retrospective study (N = 15) to reproduce invasive electroencephalography (iEEG)-recorded seizures. ESSES reproduced the iEEG-recorded seizures, and significantly better so for patients with good (seizure-free, SF) than bad (nonseizure-free, NSF) outcome. We illustrate here the clinical applicability of ESSES with a pseudo-prospective study (N = 34) with a blind setting (to the resection strategy and surgical outcome) that emulated presurgical conditions. By setting the model parameters in the retrospective study, ESSES could be applied also to patients without iEEG data. ESSES could predict the chances of good outcome after any resection by finding patient-specific model-based optimal resection strategies, which we found to be smaller for SF than NSF patients, suggesting an intrinsic difference in the network organization or presurgical evaluation results of NSF patients. The actual surgical plan overlapped more with the model-based optimal resection, and had a larger effect in decreasing modeled seizure propagation, for SF patients than for NSF patients. Overall, ESSES could correctly predict 75% of NSF and 80.8% of SF cases pseudo-prospectively. Our results show that individualised computational models may inform surgical planning by suggesting alternative resections and providing information on the likelihood of a good outcome after a proposed resection. This is the first time that such a model is validated with a fully independent cohort and without the need for iEEG recordings.
Individualized computational models of epilepsy surgery capture some of the key aspects of seizure propagation and the resective surgery. It is to be established whether this information can be integrated during the presurgical evaluation of the patient to improve surgical planning and the chances of a good surgical outcome. Here we address this question with a pseudo-prospective study that applies a computational framework of seizure propagation and epilepsy surgery—the ESSES framework—in a pseudo-prospective study mimicking the presurgical conditions. We found that within this pseudo-prospective setting, ESSES could correctly predict 75% of NSF and 80.8% of SF cases. This finding suggests the potential of individualised computational models to inform surgical planning by suggesting alternative resections and providing information on the likelihood of a good outcome after a proposed resection.

BACKGROUND: Few studies have evaluated the frequency of and the reasons behind the refusal of listing liver transplantation candidates.
OBJECTIVE: To assess the ineligibility rate for liver transplantation and its motivations.
METHODS: A single-center retrospective study was conducted on adult patients which entailed a formal multidisciplinary assessment for liver transplantation eligibility. The predictors for listing were evaluated using multivariable logistic regression.
RESULTS: In our center, 314 patients underwent multidisciplinary work-up before liver transplantation enlisting over a three-year period. The most frequent reasons for transplant evaluation were decompensated cirrhosis (51.6%) and hepatocellular carcinoma (35.7%). The non-listing rate was 53.8% and the transplant rate was 34.4% for the whole cohort. Two hundred and five motivations for ineligibility were collected. The most common contraindications were psychological (9.3%), cardiovascular (6.8%), and surgical (5.9%). Inappropriate or premature referral accounted for 76 (37.1%) cases. On multivariable analysis, a referral from another hospital (OR: 2.113; 95%CI: 1.259-3.548) served as an independent predictor of non-listing.
CONCLUSIONS: A non-listing decision occurred in half of our cohort and was based on an inappropriate or premature referral in one case out of three. The referral from another hospital was taken as a strong predictor of non-listing.

This case report describes the treatment selection process for a 36-year-old woman with stress urinary incontinence (SUI) and an overactive bladder (OAB) who desired pregnancy. The patient had comorbidities of hypertension and type 2 diabetes, which required consideration to improve her quality of life and reproductive health. A recently developed decision support tool using a discrete mathematical approach was used to select a treatment method tailored to the patient\'s individual situation. The analysis determined that vaginal erbium laser (VEL) treatment (Renovalase SP Dynamis Fotona d.o.o, Ljubljana, Slovenia) was the most suitable for this patient. VEL treatment significantly improved both SUI and OAB and changing antihypertensive medication eliminated nocturia. This case suggests the potential application of graph theory in treatment selection for SUI patients.

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Cell culture-based technologies are widely utilized in various domains such as drug evaluation, toxicity assessment, vaccine and biopharmaceutical development, reproductive technology, and regenerative medicine. It has been demonstrated that pre-adsorption of extracellular matrix (ECM) proteins including collagen, laminin and fibronectin provide more degrees of support for cell adhesion. The purpose of cell imprinting is to imitate the natural topography of cell membranes by gels or polymers to create a reliable environment for the regulation of cell function. The results of recent studies show that cell imprinting is a tool to guide the behavior of cultured cells by controlling their adhesive interactions with surfaces. Therefore, in this review we aim to compare different cell cultures with the imprinting method and discuss different cell imprinting applications in regenerative medicine, personalized medicine, disease modeling, and cell therapy.

BACKGROUND: Emergency department crowding continues to threaten patient safety and cause poor patient outcomes. Prior models designed to predict hospital admission have had biases. Predictive models that successfully estimate the probability of patient hospital admission would be useful in reducing or preventing emergency department \"boarding\" and hospital \"exit block\" and would reduce emergency department crowding by initiating earlier hospital admission and avoiding protracted bed procurement processes.
OBJECTIVE: To develop a model to predict imminent adult patient hospital admission from the emergency department early in the patient visit by utilizing existing clinical descriptors (ie, patient biomarkers) that are routinely collected at triage and captured in the hospital\'s electronic medical records. Biomarkers are advantageous for modeling due to their early and routine collection at triage; instantaneous availability; standardized definition, measurement, and interpretation; and their freedom from the confines of patient histories (ie, they are not affected by inaccurate patient reports on medical history, unavailable reports, or delayed report retrieval).
METHODS: This retrospective cohort study evaluated 1 year of consecutive data events among adult patients admitted to the emergency department and developed an algorithm that predicted which patients would require imminent hospital admission. Eight predictor variables were evaluated for their roles in the outcome of the patient emergency department visit. Logistic regression was used to model the study data.
RESULTS: The 8-predictor model included the following biomarkers: age, systolic blood pressure, diastolic blood pressure, heart rate, respiration rate, temperature, gender, and acuity level. The model used these biomarkers to identify emergency department patients who required hospital admission. Our model performed well, with good agreement between observed and predicted admissions, indicating a well-fitting and well-calibrated model that showed good ability to discriminate between patients who would and would not be admitted.
CONCLUSIONS: This prediction model based on primary data identified emergency department patients with an increased risk of hospital admission. This actionable information can be used to improve patient care and hospital operations, especially by reducing emergency department crowding by looking ahead to predict which patients are likely to be admitted following triage, thereby providing needed information to initiate the complex admission and bed assignment processes much earlier in the care continuum.

Sepsis-associated acute kidney injury (SA-AKI) is a serious complication in critically ill patients, resulting in higher mortality, morbidity, and cost. The intricate pathophysiology of SA-AKI requires vigilant clinical monitoring and appropriate, prompt intervention. While traditional statistical analyses have identified severe risk factors for SA-AKI, the results have been inconsistent across studies. This has led to growing interest in leveraging artificial intelligence (AI) and machine learning (ML) to predict SA-AKI better. ML can uncover complex patterns beyond human discernment by analyzing vast datasets. Supervised learning models like XGBoost and RNN-LSTM have proven remarkably accurate at predicting SA-AKI onset and subsequent mortality, often surpassing traditional risk scores. Meanwhile, unsupervised learning reveals clinically relevant sub-phenotypes among diverse SA-AKI patients, enabling more tailored care. In addition, it potentially optimizes sepsis treatment to prevent SA-AKI through continual refinement based on patient outcomes. However, utilizing AI/ML presents ethical and practical challenges regarding data privacy, algorithmic biases, and regulatory compliance. AI/ML allows early risk detection, personalized management, optimal treatment strategies, and collaborative learning for SA-AKI management. Future directions include real-time patient monitoring, simulated data generation, and predictive algorithms for timely interventions. However, a smooth transition to clinical practice demands continuous model enhancements and rigorous regulatory oversight. In this article, we outlined the conventional methods used to address SA-AKI and explore how AI and ML can be applied to diagnose and manage SA-AKI, highlighting their potential to revolutionize SA-AKI care.

To create a review of the published scientific literature on the benefits and potential perspectives of the use of 3D bio-nitrification in the field of pharmaceutics. This work was performed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines for reporting meta-analyses and systematic reviews. The scientific databases PubMed, Scopus, Google Scholar, and ScienceDirect were used to search and extract data using the following keywords: 3D bioprinting, drug research and development, personalized medicine, pharmaceutical companies, clinical trials, drug testing. The data points to several aspects of the application of bioprinting in pharmaceutics were reviewed. The main applications of bioprinting are in the development of new drug molecules as well as in the preparation of personalized drugs, but the greatest benefits are in terms of drug screening and testing. Growth in the field of 3D printing has facilitated pharmaceutical applications, enabling the development of personalized drug screening and drug delivery systems for individual patients. Bioprinting presents the opportunity to print drugs on demand according to the individual needs of the patient, making the shape, structure, and dosage suitable for each of the patient\'s physical conditions, i.e., print specific drugs for controlled release rates; print porous tablets to reduce swallowing difficulties; make transdermal microneedle patches to reduce patient pain; and so on. On the other hand, bioprinting can precisely control the distribution of cells and biomaterials to build organoids, or an Organ-on-a-Chip, for the testing of drugs on printed organs mimicking specified disease characteristics instead of animal testing and clinical trials. The development of bioprinting has the potential to offer customized drug screening platforms and drug delivery systems meeting a range of individualized needs, as well as prospects at different stages of drug development and patient therapy. The role of bioprinting in preclinical and clinical testing of drugs is also of significant importance in terms of shortening the time to launch a medicinal product on the market.

Some infectious agents have the potential to cause specific modifications in the cellular microenvironment that could be propitious to the carcinogenesis process. Currently, there are specific viruses and bacteria, such as human papillomavirus (HPV) and Helicobacter pylori, that are well established as risk factors for neoplasia. Chlamydia trachomatis (CT) infections are one of the most common bacterial sexually transmitted infections worldwide, and recent European data confirmed a continuous rise across Europe. The infection is often asymptomatic in both sexes, requiring a screening program for early detection. Notwithstanding, not all countries in Europe have it. Chlamydia trachomatis can cause chronic and persistent infections, resulting in inflammation, and there are plausible biological mechanisms that link the genital infection with tumorigenesis. Herein, we aimed to understand the epidemiological and biological plausibility of CT genital infections causing endometrial, ovarian, and cervical tumors. Also, we covered some of the best suitable in vitro techniques that could be used to study this potential association. In addition, we defend the point of view of a personalized medicine strategy to treat those patients through the discovery of some biomarkers that could allow it. This review supports the need for the development of further fundamental studies in this area, in order to investigate and establish the role of chlamydial genital infections in oncogenesis.