Raman-based machine learning platform reveals unique metabolic differences between IDHmut and IDHwt glioma.

BACKGROUND: Formalin-fixed, paraffin-embedded (FFPE) tissue slides are routinely used in cancer diagnosis, clinical decision-making, and stored in biobanks, but their utilization in Raman spectroscopy-based studies has been limited due to the background coming from embedding media. METHODS: Spontaneous Raman spectroscopy was used for molecular fingerprinting of FFPE tissue from 46 patient samples with known methylation subtypes. Spectra were used to construct tumor/non-tumor, IDH1WT/IDH1mut, and methylation-subtype classifiers. Support vector machine and random forest were used to identify the most discriminatory Raman frequencies. Stimulated Raman spectroscopy was used to validate the frequencies identified. Mass spectrometry of glioma cell lines and TCGA were used to validate the biological findings. RESULTS: Here we develop APOLLO (rAman-based PathOLogy of maLignant glioma) - a computational workflow that predicts different subtypes of glioma from spontaneous Raman spectra of FFPE tissue slides. Our novel APOLLO platform distinguishes tumors from nontumor tissue and identifies novel Raman peaks corresponding to DNA and proteins that are more intense in the tumor. APOLLO differentiates isocitrate dehydrogenase 1 mutant (IDH1mut) from wildtype (IDH1WT) tumors and identifies cholesterol ester levels to be highly abundant in IDHmut glioma. Moreover, APOLLO achieves high discriminative power between finer, clinically relevant glioma methylation subtypes, distinguishing between the CpG island hypermethylated phenotype (G-CIMP)-high and G-CIMP-low molecular phenotypes within the IDH1mut types. CONCLUSIONS: Our results demonstrate the potential of label-free Raman spectroscopy to classify glioma subtypes from FFPE slides and to extract meaningful biological information thus opening the door for future applications on these archived tissues in other cancers.

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.

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.

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.

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.

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.