The Vascularized Omentum Lymph Node Transfer - A Key Point in the Lymphedema Management.

Background: As an increased number of women beat breast cancer worldwide, the breast cancer related lymphedema has gained more attention recently. The vascularized omentum lymph node transfer has been approached as an useful tool for advanced and recurrent cases. The purpose of the paper is to emphasize the advantages and disadvantages of this method. Materials and Methods: This retrospective study consists of 17 patients known with breast cancer related lymphedema who received vascularized omentum lymph node transfer. Data was recorded between January 2022 and January 2023. Patients diagnosed with secondary lymphedema stage II or III, unresponsive to previous microsurgical lymphovenous bypass were included. Results: The most prevalent affected site was the left upper limb (59%), where edema was mainly identified in the forearm (75%). Nevertheless, more than half of the subjects have previously received lymphaticovenous anastomosis. The correlation between the stage of lymphedema and the postoperative reduction of the volume of the affected limb was -0.26, the slope to reached -0.33, with an intercept value of 2.64. The follow-up period showed reduced upper limb volume and an improved quality of life. Conclusion: Through an experienced hand, this versatile flap brings hope to breast cancer survivors with lymphedema.

Systematic Review on the Effectiveness and Outcomes of Nivolumab Treatment Schemes in Advanced and Metastatic Cervical Cancer.

Advanced and metastatic cervical cancer remains a formidable challenge in oncology, with immune checkpoint inhibitors such as the PD-1 inhibitor nivolumab emerging as a potential therapeutic option. This systematic review rigorously assesses the effectiveness and outcomes of various nivolumab treatment regimens within this patient cohort, drawing from clinical trials and real-world evidence up to December 2023. Following a comprehensive search across PubMed, Scopus, and Embase, four studies were deemed eligible, involving a collective total of 80 patients. One preliminary trial data were excluded from the final analysis, as well as four other proceedings and abstracts on the efficacy and safety of nivolumab on advanced cervical cancer. The patients' average age across these studies was 48 years, with an average of 38% having an Eastern Cooperative Oncology Group (ECOG) performance status of 1. Notably, 64% of all patients were positive for high-risk HPV, and 71% exhibited PD-L1 positivity, indicating a substantial target population for nivolumab. The analysis revealed a pooled objective response rate (ORR) of 48%, with a disease control rate (DCR) averaging 71%. Moreover, progression-free survival (PFS) at 6 months was observed at an average rate of 50%, reflecting the significant potential of nivolumab in managing advanced stages of the disease. The review highlights the influence of PD-L1 status on response rates and underscores the enhanced outcomes associated with combination therapy approaches. By delineating the variability in treatment efficacy and pinpointing key factors affecting therapeutic response and survival, this systematic review calls for further investigations to refine nivolumab's clinical application, aiming to improve patient outcomes in advanced and metastatic cervical cancer.

Maternal and Newborn Characteristics-A Comparison between Healthy and Thrombophilic Pregnancy.

A thrombophilic woman is more likely to experience difficulties during pregnancy, difficulties that will also affect the development of the newborn. This study aims to compare maternal and newborn characteristics between healthy and thrombophilic pregnancy. The following characteristics were analysed: maternal characteristics (BMI- body mass index, haemostasis parameters, thrombophilia-specific treatment) and newborn characteristics (gestational period, birth weight, the Apgar score). This follow-up study spanning five years, from 2018 to 2022, focuses on a cohort of 500 women who underwent delivery hospitalization in the western region of Romania. The maternal characteristics influence the newborn: the greater the weight of the mother with thrombophilia, the more the chances that the fetus will have a lower birth weight; increasing the dose of LMWH (low molecular weight heparin), connected with the necessity to control the homeostasis parameters, the more likely the fetus will be born with a lower birth weight. A pregnant woman with thrombophilia, treated appropriately, having a normal weight, and not presenting other risk factors independent of thrombophilia, will have a newborn with characteristics similar to a healthy pregnant woman.

Analysis of the Healthcare System in Romania: A Brief Review.

This manuscript provides a brief review and analysis of the healthcare system in Romania. This study aims to comprehensively analyse the healthcare system in Romania, evaluating its strengths, weaknesses, and impact on the population's access to quality healthcare services. Within the framework of the Romanian healthcare system, a multitude of pressing challenges endure. These encompass insufficient funding, shortages of medical personnel, and ineffectiveness in the provisioning of services. These impediments substantially hinder the accessibility of healthcare services, particularly in outlying and pastoral regions, thereby rendering the system susceptible and underserving certain demographics. Our investigation presents three hypotheses. The opening conjecture proposes that inadequate funding has a negative impact on the availability and standard of healthcare facilities in Romania. In addition, another hypothesis assumes that insufficient medical staff plays a considerable role in inequalities in access to and delivery of healthcare. Moreover, the existence of inadequacies in service provision serves as a significant barrier, obstructing the timely and efficient delivery of healthcare to those who need it. Our research encompasses a comprehensive analysis of key aspects of the Romanian healthcare system, ranging from healthcare infrastructure and financing mechanisms to service delivery and healthcare outcomes. Through a blend of qualitative and quantitative data sources, including government reports, academic studies, and statistical data, we have endeavoured to provide an in-depth evaluation. The analysis encompasses various aspects, including healthcare infrastructure, financing mechanisms, service delivery, and healthcare outcomes. Romania has a mixed healthcare system with both public and private providers. The primary level of care is delivered by family doctors, while hospitals and specialised medical centres provide secondary and tertiary care services. This research underlines the criticality of significant alterations being implemented in the healthcare system of Romania to address the issues arising from insufficient funding, a shortage of medical personnel, and shortcomings in service delivery. It is vital to tackle the obstacles presented by insufficient funding, the dearth of healthcare staff, and inadequacies in service delivery to attain impartial and reachable healthcare. By implementing these essential transformations, Romania can pave the way towards a healthcare system that efficaciously caters to the diverse requirements of its populace and guarantees the provision of prompt and superior healthcare services.

The Approach of Artificial Intelligence in Neuroendocrine Carcinomas of the Breast: A Next Step towards Precision Pathology?-A Case Report and Review of the Literature.

Primary neuroendocrine tumors (NETs) of the breast are considered a rare and undervalued subtype of breast carcinoma that occur mainly in postmenopausal women and are graded as G1 or G2 NETs or an invasive neuroendocrine carcinoma (NEC) (small cell or large cell). To establish a final diagnosis of breast carcinoma with neuroendocrine differentiation, it is essential to perform an immunohistochemical profile of the tumor, using antibodies against synaptophysin or chromogranin, as well as the MIB-1 proliferation index, one of the most controversial markers in breast pathology regarding its methodology in current clinical practice. A standardization error between institutions and pathologists regarding the evaluation of the MIB-1 proliferation index is present. Another challenge refers to the counting process of MIB-1's expressiveness, which is known as a time-consuming process. The involvement of AI (artificial intelligence) automated systems could be a solution for diagnosing early stages, as well. We present the case of a post-menopausal 79-year-old woman diagnosed with primary neuroendocrine carcinoma of the breast (NECB). The purpose of this paper is to expose the interpretation of MIB-1 expression in our patient' s case of breast neuroendocrine carcinoma, assisted by artificial intelligence (AI) software (HALO-IndicaLabs), and to analyze the associations between MIB-1 and common histopathological parameters.

Development of a Framework for On-Demand Caesarean Section in Romania.

BACKGROUND: Caesarean section rates have continued to trend upward in most countries, including Romania, creating a number of economic challenges. In the public health system, there is no regulation for performing Caesarean sections on demand; it is often done unlawfully, and in private hospitals, it is a real business. Thus, this study aims to investigate the budgetary impact at a hospital level and the profit per procedure by introducing on-demand caesarean sections for a fee. METHODS: This study was conducted in one of the largest maternity units in Western Romania-the "Bega" Maternity Clinic of the Timisoara County Emergency Hospital. For the analysis, the difference between a proposed occupancy rate (between 50 and 85%, increasing every 5 percent) and the actual occupancy rate was calculated. Considering that this difference can be used to admit patients to receive Caesarean sections on demand for a fee, the profit that could be obtained during the study period was calculated. RESULTS: It is reported that between 238 (proposed occupancy rate of 50%) and 4683 patients (a proposed occupancy rate of 85%) could have benefited from on-demand caesarean section surgery in 2017-2019. Between RON 419,999 and RON 8,551,636 could be obtained in the 3 years of study by implementing caesarean section against payment. CONCLUSION: The implementation of a system of on-demand payment for caesarean sections in Romania would bring significant profits to the hospital budget.

Diagnosis and Management of Dysgerminomas with a Brief Summary of Primitive Germ Cell Tumors.

Dysgerminoma represents a rare malignant tumor composed of germ cells, originally from the embryonic gonads. Regarding its incidence, we do not have precise data due to its rarity. Dysgerminoma occurs at a fertile age. The preferred treatment is the surgical removal of the tumor succeeded by the preservation of fertility. Even if a multidisciplinary team, founded in 2009 by a gynecologist, an oncologist, a pediatric oncologist and a pediatric surgeon, under the guidance of the Malignant Germ Cell International Consortium (MaGIC), studies this type of tumor, issues still remain related to the lack of a randomized study and to both the management and understanding of the concept of OMGCTs (ovarian malignant germ cell tumors). The aim of this review is to present from the literature the various approaches for this type of tumor, and, regarding innovative therapies or possible prevention, which can be applied in clinical practice. Multidisciplinarity and treatment in reference centers have proven their usefulness as well.

An Interesting Image of Transmural Migration of a Levonorgestrel-Releasing Intrauterine Device (LNg-IUD).

Intrauterine devices (IUDs) are very common as a method of birth control. By adding progesterone (levonorgestrel), a decrease in the risk of complications has been documented, including the risk of perforation. Even though only a few complications have been described, adjacent organs may be involved in the case of migration-a life-threatening situation. A 45-year-old G4P2 woman was seen in our clinic for LNg-IUD removal, according to the medical instructions. Her main complaints were abdominal discomfort, low back pain, and recurrent menorrhagia. A "lost" IUD was initially suspected; the patient confirmed the detection and removal of the control strings, and a subsequent discussion related to delayed transmural migration of the IUD being followed. The ultrasonography revealed the migration of the IUD to the uterine cervix and size-decreased uterine fibroids, confirming the effectiveness of the LNg-IUD. The MRI and ultrasonography being useless, a subsequent X-ray and CT scan were requested, both confirming a myometrium-positioned IUD, adjacent to the serosa and lumbosacral plexus. Even though the IUD is considered a safe device with reversible effect, it can be associated with severe morbidity, with an ultrasound follow-up being required. For more precise detection of the IUD, we strongly recommend an X-ray or CT scan examination, followed by safe removal.

Network controllability solutions for computational drug repurposing using genetic algorithms.

Control theory has seen recently impactful applications in network science, especially in connections with applications in network medicine. A key topic of research is that of finding minimal external interventions that offer control over the dynamics of a given network, a problem known as network controllability. We propose in this article a new solution for this problem based on genetic algorithms. We tailor our solution for applications in computational drug repurposing, seeking to maximize its use of FDA-approved drug targets in a given disease-specific protein-protein interaction network. We demonstrate our algorithm on several cancer networks and on several random networks with their edges distributed according to the Erdos-Rényi, the Scale-Free, and the Small World properties. Overall, we show that our new algorithm is more efficient in identifying relevant drug targets in a disease network, advancing the computational solutions needed for new therapeutic and drug repurposing approaches.

Scalable reaction network modeling with automatic validation of consistency in Event-B.

Constructing a large biological model is a difficult, error-prone process. Small errors in writing a part of the model cascade to the system level and their sources are difficult to trace back. In this paper we extend a recent approach based on Event-B, a state-based formal method with refinement as its central ingredient, allowing us to validate for model consistency step-by-step in an automated way. We demonstrate this approach on a model of the heat shock response in eukaryotes and its scalability on a model of the [Formula: see text] signaling pathway. All consistency properties of the model were proved automatically with computer support.

Network analytics for drug repurposing in COVID-19.

To better understand the potential of drug repurposing in COVID-19, we analyzed control strategies over essential host factors for SARS-CoV-2 infection. We constructed comprehensive directed protein-protein interaction (PPI) networks integrating the top-ranked host factors, the drug target proteins and directed PPI data. We analyzed the networks to identify drug targets and combinations thereof that offer efficient control over the host factors. We validated our findings against clinical studies data and bioinformatics studies. Our method offers a new insight into the molecular details of the disease and into potentially new therapy targets for it. Our approach for drug repurposing is significant beyond COVID-19 and may be applied also to other diseases.

NetControl4BioMed: a web-based platform for controllability analysis of protein-protein interaction networks.

MOTIVATION: There is an increasing amount of data coming from genome-wide studies identifying disease-specific survivability-essential proteins and host factors critical to a cell becoming infected. Targeting such proteins has a strong potential for targeted, precision therapies. Typically however, too few of them are drug targetable. An alternative approach is to influence them through drug targetable proteins upstream of them. Structural target network controllability is a suitable solution to this problem. It aims to discover suitable source nodes (e.g. drug targetable proteins) in a directed interaction network that can control (through a suitable set of input functions) a desired set of targets. RESULTS: We introduce NetControl4BioMed, a free open-source web-based application that allows users to generate or upload directed protein-protein interaction networks and to perform target structural network controllability analyses on them. The analyses can be customized to focus the search on drug targetable source nodes, thus providing drug therapeutic suggestions. The application integrates protein data from HGNC, Ensemble, UniProt, NCBI and InnateDB, directed interaction data from InnateDB, Omnipath and SIGNOR, cell-line data from COLT and DepMap, and drug-target data from DrugBank. AVAILABILITYAND IMPLEMENTATION: The application and data are available online at https://netcontrol.combio.org/. The source code is available at https://github.com/Vilksar/NetControl4BioMed under an MIT license.

Reaction Systems and Synchronous Digital Circuits.

A reaction system is a modeling framework for investigating the functioning of the living cell, focused on capturing cause-effect relationships in biochemical environments. Biochemical processes in this framework are seen to interact with each other by producing the ingredients enabling and/or inhibiting other reactions. They can also be influenced by the environment seen as a systematic driver of the processes through the ingredients brought into the cellular environment. In this paper, the first attempt is made to implement reaction systems in the hardware. We first show a tight relation between reaction systems and synchronous digital circuits, generally used for digital electronics design. We describe the algorithms allowing us to translate one model to the other one, while keeping the same behavior and similar size. We also develop a compiler translating a reaction systems description into hardware circuit description using field-programming gate arrays (FPGA) technology, leading to high performance, hardware-based simulations of reaction systems. This work also opens a novel interesting perspective of analyzing the behavior of biological systems using established industrial tools from electronic circuits design.

Refinement-based modeling of the ErbB signaling pathway.

The construction of large scale biological models is a laborious task, which is often addressed by adopting iterative routines for model augmentation, adding certain details to an initial high level abstraction of the biological phenomenon of interest. Refitting a model at every step of its development is time consuming and computationally intensive. The concept of model refinement brings about an effective alternative by providing adequate parameter values that ensure the preservation of its quantitative fit at every refinement step. We demonstrate this approach by constructing the largest-ever refinement-based biomodel, consisting of 421 species and 928 reactions. We start from an already fit, relatively small literature model whose consistency we check formally. We then construct the final model through an algorithmic step-by-step refinement procedure that ensures the preservation of the model's fit.

NetControl4BioMed: a pipeline for biomedical data acquisition and analysis of network controllability.

BACKGROUND: Network controllability focuses on discovering combinations of external interventions that can drive a biological system to a desired configuration. In practice, this approach translates into finding a combined multi-drug therapy in order to induce a desired response from a cell; this can lead to developments of novel therapeutic approaches for systemic diseases like cancer. RESULT: We develop a novel bioinformatics data analysis pipeline called NetControl4BioMed based on the concept of target structural control of linear networks. Our pipeline generates novel molecular interaction networks by combining pathway data from various public databases starting from the user's query. The pipeline then identifies a set of nodes that is enough to control a given, user-defined set of disease-specific essential proteins in the network, i.e., it is able to induce a change in their configuration from any initial state to any final state. We provide both the source code of the pipeline as well as an online web-service based on this pipeline http://combio.abo.fi/nc/net_control/remote_call.php . CONCLUSION: The pipeline can be used by researchers for controlling and better understanding of molecular interaction networks through combinatorial multi-drug therapies, for more efficient therapeutic approaches and personalised medicine.

Structural Target Controllability of Linear Networks.

Computational analysis of the structure of intra-cellular molecular interaction networks can suggest novel therapeutic approaches for systemic diseases like cancer. Recent research in the area of network science has shown that network control theory can be a powerful tool in the understanding and manipulation of such bio-medical networks. In 2011, Liu et al. developed a polynomial time algorithm computing the size of the minimal set of nodes controlling a linear network. In 2014, Gao et al. generalized the problem for target control, minimizing the set of nodes controlling a target within a linear network. The authors developed a Greedy approximation algorithm while leaving open the complexity of the optimization problem. We prove here that the target controllability problem is NP-hard in all practical setups, i.e., when the control power of any individual input is bounded by some constant. We also show that the algorithm provided by Gao et al. fails to provide a valid solution in some special cases, and an additional validation step is required. We fix and improve their algorithm using several heuristics, obtaining in the end an up to 10-fold decrease in running time and also a decrease in the size of solutions.

Stepwise construction of a metabolic network in Event-B: The heat shock response.

There is a high interest in constructing large, detailed computational models for biological processes. This is often done by putting together existing submodels and adding to them extra details/knowledge. The result of such approaches is usually a model that can only answer questions on a very specific level of detail, and thus, ultimately, is of limited use. We focus instead on an approach to systematically add details to a model, with formal verification of its consistency at each step. In this way, one obtains a set of reusable models, at different levels of abstraction, to be used for different purposes depending on the question to address. We demonstrate this approach using Event-B, a computational framework introduced to develop formal specifications of distributed software systems. We first describe how to model generic metabolic networks in Event-B. Then, we apply this method for modeling the biological heat shock response in eukaryotic cells, using Event-B refinement techniques. The advantage of using Event-B consists in having refinement as an intrinsic feature; this provides as a final result not only a correct model, but a chain of models automatically linked by refinement, each of which is provably correct and reusable. This is a proof-of-concept that refinement in Event-B is suitable for biomodeling, serving for mastering biological complexity.

Tailored Approaches in Drug Development and Diagnostics: From Molecular Design to Biological Model Systems.

Approaches to increase the efficiency in developing drugs and diagnostics tools, including new drug delivery and diagnostic technologies, are needed for improved diagnosis and treatment of major diseases and health problems such as cancer, inflammatory diseases, chronic wounds, and antibiotic resistance. Development within several areas of research ranging from computational sciences, material sciences, bioengineering to biomedical sciences and bioimaging is needed to realize innovative drug development and diagnostic (DDD) approaches. Here, an overview of recent progresses within key areas that can provide customizable solutions to improve processes and the approaches taken within DDD is provided. Due to the broadness of the area, unfortunately all relevant aspects such as pharmacokinetics of bioactive molecules and delivery systems cannot be covered. Tailored approaches within (i) bioinformatics and computer-aided drug design, (ii) nanotechnology, (iii) novel materials and technologies for drug delivery and diagnostic systems, and (iv) disease models to predict safety and efficacy of medicines under development are focused on. Current developments and challenges ahead are discussed. The broad scope reflects the multidisciplinary nature of the field of DDD and aims to highlight the convergence of biological, pharmaceutical, and medical disciplines needed to meet the societal challenges of the 21st century.

Controlling Directed Protein Interaction Networks in Cancer.

Control theory is a well-established approach in network science, with applications in bio-medicine and cancer research. We build on recent results for structural controllability of directed networks, which identifies a set of driver nodes able to control an a-priori defined part of the network. We develop a novel and efficient approach for the (targeted) structural controllability of cancer networks and demonstrate it for the analysis of breast, pancreatic, and ovarian cancer. We build in each case a protein-protein interaction network and focus on the survivability-essential proteins specific to each cancer type. We show that these essential proteins are efficiently controllable from a relatively small computable set of driver nodes. Moreover, we adjust the method to find the driver nodes among FDA-approved drug-target nodes. We find that, while many of the drugs acting on the driver nodes are part of known cancer therapies, some of them are not used for the cancer types analyzed here; some drug-target driver nodes identified by our algorithms are not known to be used in any cancer therapy. Overall we show that a better understanding of the control dynamics of cancer through computational modelling can pave the way for new efficient therapeutic approaches and personalized medicine.

The phosphorylation of the heat shock factor as a modulator for the heat shock response.

The heat shock response is a well-conserved defence mechanism against the accumulation of misfolded proteins due to prolonged elevated heat. The cell responds to heat shock by raising the levels of heat shock proteins (hsp), which are responsible for chaperoning protein refolding. The synthesis of hsp is highly regulated at the transcription level by specific heat shock (transcription) factors (hsf). One of the regulation mechanisms is the phosphorylation of hsf's. Experimental evidence shows a connection between the hyper-phosphorylation of hsfs and the transactivation of the hsp-encoding genes. In this paper, we incorporate several (de)phosphorylation pathways into an existing well-validated computational model of the heat shock response. We analyze the quantitative control of each of these pathways over the entire process. For each of these pathways we create detailed computational models which we subject to parameter estimation in order to fit them to existing experimental data. In particular, we find conclusive evidence supporting only one of the analyzed pathways. Also, we corroborate our results with a set of computational models of a more reduced size.